Initial commit of Weather App

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Environment variables
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Logs
+*.log
+logs/
+data/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Temporary files
+*.tmp
+*.temp
+.cache/
+
+# API Keys (should be in HF Spaces secrets)
+*.key
+api_keys.txt
+credentials.json
+
+# Local development
+test_*
+debug_*
+scratch_*
+
+# Node modules (if any)
+node_modules/
+
+# Coverage reports
+htmlcov/
+.coverage
+.coverage.*
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/

CONVERSATIONAL_FEATURES.md

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+# Enhanced Conversational Weather Features
+
+## Overview
+The enhanced weather app now supports natural, conversational queries including activity advice, forecast requests, and historical comparisons. You can ask questions just like you would to a weather-savvy friend!
+
+## New Query Types
+
+### 🚴‍♂️ Activity-Based Queries
+Ask for weather advice for specific activities:
+
+**Examples:**
+- "Should I go biking in Seattle today?"
+- "Is it good weather for walking in Central Park?"
+- "Can I have a picnic in Austin this weekend?"
+- "Is it safe to drive to Chicago tomorrow?"
+- "Good conditions for running in Miami?"
+- "Should I plan an outdoor BBQ in Denver?"
+
+**Supported Activities:**
+- **Biking/Cycling**: Considers wind speed, precipitation, temperature comfort, visibility
+- **Walking/Hiking**: Focuses on precipitation, temperature, air quality
+- **Outdoor Events**: Considers all weather factors, especially precipitation probability
+- **Driving/Travel**: Emphasizes visibility, precipitation, wind, road conditions
+- **Water Activities**: Considers temperature, thunderstorm risk, UV considerations
+- **Sports**: Factors in wind for ball sports, temperature for comfort
+
+### 📅 Forecast & Timeline Queries
+Get weather information for different time horizons:
+
+**Examples:**
+- "What's the forecast for New York this week?"
+- "Will it rain in Portland tomorrow?"
+- "Extended forecast for Miami"
+- "Hourly weather breakdown for today in Chicago"
+- "What's the weather trend for Los Angeles?"
+- "Next few days outlook for Boston"
+
+**Timeline Options:**
+- **Short-term**: Today, tonight, tomorrow, next few hours
+- **Medium-term**: This week, next week, weekend, next few days
+- **Long-term**: Next month, seasonal outlook, extended forecast
+
+### 📊 Historical & Comparative Queries
+Compare current conditions to historical patterns:
+
+**Examples:**
+- "How does today's weather in Phoenix compare to normal?"
+- "Is this typical weather for Seattle in June?"
+- "Temperature compared to last year in New York"
+- "Has it been rainier than usual in Portland?"
+- "Climate data for Denver this season"
+- "Weather records for Chicago"
+
+**Historical Context:**
+- **Recent**: Yesterday, last week, past week
+- **Seasonal**: Last month, past season, seasonal averages
+- **Yearly**: Last year, annual comparisons
+- **Long-term**: Historical records, climate data, trends
+
+## Smart Features
+
+### 🧠 Context-Aware Responses
+The AI understands context and provides relevant advice:
+
+```
+User: "Should I go biking in Seattle?"
+AI: "Based on current conditions in Seattle - 65°F with light winds 
+    and only 10% chance of rain - it's excellent weather for biking! 
+    The temperature is comfortable and conditions are dry. Perfect 
+    time to get out on the trails."
+```
+
+### 🌍 Multi-City Comparisons
+Compare weather and activity conditions across cities:
+
+```
+User: "Compare biking conditions between Portland and Seattle"
+AI: "For biking today: Portland has 72°F with calm winds and 0% 
+    rain chance - ideal conditions! Seattle is 65°F with 5-10 mph 
+    winds and 15% rain chance - still good but slightly windier. 
+    Both cities offer great biking weather today."
+```
+
+### ⚠️ Safety Considerations
+The AI prioritizes safety in activity recommendations:
+
+- **High wind warnings** for outdoor activities
+- **Precipitation alerts** for outdoor events
+- **Temperature extremes** for physical activities
+- **Visibility concerns** for travel
+- **Storm warnings** for all outdoor activities
+
+### 🎯 Personalized Advice
+Get specific recommendations based on your activity:
+
+- **Timing suggestions**: "Better conditions expected this afternoon"
+- **Alternative activities**: "Not ideal for biking, but great for indoor activities"
+- **Preparation tips**: "Bring layers for temperature changes"
+- **Safety reminders**: "High winds - secure outdoor items"
+
+## Sample Conversations
+
+### Conversation 1: Activity Planning
+```
+User: "Should I go on a bike ride in Austin?"
+AI: "Great question! In Austin right now it's 78°F with partly cloudy 
+    skies and light 5 mph winds. Only 5% chance of rain - these are 
+    excellent biking conditions! The temperature is perfect for cycling, 
+    winds are calm, and no precipitation expected. Perfect time to hit 
+    the trails! 🚴‍♂️"
+```
+
+### Conversation 2: Forecast Planning
+```
+User: "What's the forecast for outdoor events in Miami this weekend?"
+AI: "For outdoor events in Miami this weekend: Saturday looks perfect 
+    with 82°F, sunny skies, and only 10% rain chance. Sunday has a 40% 
+    chance of afternoon showers with temps around 85°F. I'd recommend 
+    planning outdoor activities for Saturday morning/afternoon, and have 
+    backup indoor plans for Sunday afternoon. 🌞"
+```
+
+### Conversation 3: Travel Advice
+```
+User: "Is it safe to drive from Chicago to Detroit tomorrow?"
+AI: "Travel conditions from Chicago to Detroit tomorrow look good! 
+    Clear skies with 45°F temperatures and light winds. No precipitation 
+    expected and good visibility. Road conditions should be excellent 
+    for your drive. Safe travels! 🚗"
+```
+
+## Pro Tips
+
+1. **Be specific about activities**: The more specific you are, the better advice you'll get
+2. **Mention timeframes**: "today", "tomorrow", "this weekend" help focus the response
+3. **Ask follow-up questions**: The AI remembers context within the conversation
+4. **Combine queries**: "Should I bike to the park or drive due to weather?"
+5. **Safety first**: The AI will always prioritize safety in recommendations
+
+## Technical Features
+
+- **Real-time data**: All recommendations based on current NWS data
+- **AI-powered**: Gemini 2.0 Flash provides intelligent, contextual responses
+- **Natural language**: Ask questions in plain English
+- **Memory**: Conversation context is maintained for follow-up questions
+- **Comprehensive**: Considers multiple weather factors for each activity
+
+Start chatting with your enhanced weather assistant today! 🌤️

INSTALLATION.md

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+# 🌤️ Weather App Pro - Complete Package
+
+## 📦 What's Included
+
+This package contains everything you need to run the Weather App Pro:
+
+### 📁 Files:
+- **`app.py`** - Main application (self-contained)
+- **`requirements.txt`** - Python dependencies
+- **`README.md`** - Documentation
+- **`run.sh`** - Quick start script (Linux/Mac)
+- **`run.bat`** - Quick start script (Windows)
+
+## 🚀 Quick Start
+
+### Option 1: Use Quick Start Scripts
+
+**Linux/Mac:**
+```bash
+# Extract the archive
+tar -xzf weather_app_pro_final.tar.gz
+cd weather_app_final
+
+# Run the quick start script
+./run.sh
+```
+
+**Windows:**
+```cmd
+REM Extract the archive
+REM Open the weather_app_final folder
+
+REM Double-click run.bat or run in command prompt:
+run.bat
+```
+
+### Option 2: Manual Installation
+
+```bash
+# Extract and navigate
+tar -xzf weather_app_pro_final.tar.gz
+cd weather_app_final
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the application
+python app.py
+```
+
+### Option 3: Deploy to Hugging Face Spaces
+
+1. Create new Space on Hugging Face
+2. Select **Gradio** SDK
+3. Upload `app.py`, `requirements.txt`, and `README.md`
+4. Space auto-deploys!
+
+## 🌟 Features
+
+### 🤖 Smart Weather Chat
+- Natural language processing
+- Auto-zoom maps to mentioned cities
+- Smart city comparisons
+- Real-time weather data
+
+### 🗺️ Dynamic Interactive Maps
+- Dark theme optimized
+- Auto-zoom to queried locations
+- Comparison lines between cities
+- Detailed weather popups
+
+### 📊 Multi-Tab Interface
+- **Chat + Map**: Main interactive experience
+- **Forecasts**: Detailed 7-day predictions
+- **Alerts**: Real-time weather alerts
+- **About**: Documentation and tips
+
+### 🌙 Beautiful Dark Mode
+- Professional gradient theme
+- Dark maps and charts
+- Comfortable viewing
+
+## 💬 Example Queries
+
+Try these natural language questions:
+
+- "What's the temperature in Wichita?"
+- "Compare rain in New York and Miami"
+- "Show me weather in Los Angeles"
+- "Wind conditions in Chicago"
+
+## 📍 Coverage
+
+100+ US cities supported including:
+- All major metropolitan areas
+- State capitals
+- Popular tourist destinations
+
+## 🔧 System Requirements
+
+- Python 3.7+
+- Internet connection (for weather data)
+- Modern web browser
+
+## 📱 Access
+
+Once running, open your browser to:
+**http://localhost:7860**
+
+## 🆘 Troubleshooting
+
+### Common Issues:
+
+1. **Port already in use**: Change port in app.py (line with `server_port=7860`)
+2. **Dependencies fail**: Try upgrading pip: `pip install --upgrade pip`
+3. **Python not found**: Ensure Python 3.7+ is installed and in PATH
+
+### Support:
+- Check the console output for error messages
+- Ensure all dependencies are installed
+- Verify internet connection for weather data
+
+---
+
+**🌟 Enjoy your intelligent weather assistant!**
+
+*Powered by the National Weather Service API*
+

README.md

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+# 🌤️ Weather App Pro Enhanced - Complete Package
+
+## 🚀 **NEW: Full AI Integration with LlamaIndex & Gemini!**
+
+This is the **COMPLETE** Weather App Pro package with **full LLM integration** for intelligent weather conversations.
+
+### 🤖 **Enhanced AI Features**
+
+#### **LlamaIndex Integration**
+- **Document Understanding**: Advanced context processing
+- **Memory Management**: Maintains conversation context
+- **Vector Search**: Intelligent information retrieval
+- **Chat Engine**: Sophisticated conversation handling
+
+#### **Gemini API Integration**
+- **Google's Gemini Pro**: State-of-the-art language model
+- **Natural Conversations**: Human-like weather discussions
+- **Smart Responses**: Context-aware and informative
+- **Fallback System**: Works even without API key
+
+#### **🆕 Conversational Weather Intelligence**
+- **Activity Advice**: "Should I go biking in Seattle?" - Get weather-based recommendations
+- **Forecast Queries**: Natural language forecast requests with timeline awareness
+- **Historical Comparisons**: "How does today compare to normal?" context
+- **Safety Considerations**: Intelligent warnings for outdoor activities
+- **Multi-City Analysis**: Smart comparisons between locations
+- **Personalized Responses**: Context-aware advice for your specific needs
+
+> 📖 **See [CONVERSATIONAL_FEATURES.md](CONVERSATIONAL_FEATURES.md) for detailed examples and usage guide**
+
+### 📁 **Complete Project Structure**
+
+```
+weather_app_complete/
+├── enhanced_main.py         # 🆕 Enhanced app with full AI
+├── app.py                   # Standalone version (Hugging Face ready)
+├── main.py                  # Basic modular version
+├── requirements.txt         # Updated with LlamaIndex & Gemini
+├── run_enhanced.sh          # 🆕 Enhanced quick start script
+├── run.sh                   # Basic quick start (Linux/Mac)
+├── run.bat                  # Basic quick start (Windows)
+├── README.md               # This comprehensive guide
+├── INSTALLATION.md         # Detailed setup instructions
+├── src/                    # Complete modular architecture
+│   ├── __init__.py
+│   ├── ai/                 # Multi-AI provider system
+│   │   ├── __init__.py
+│   │   └── multi_provider.py
+│   ├── analysis/           # Climate analysis & ML
+│   │   ├── __init__.py
+│   │   └── climate_analyzer.py
+│   ├── api/                # Advanced weather API client
+│   │   ├── __init__.py
+│   │   └── weather_client.py
+│   ├── chatbot/            # 🆕 Enhanced chatbot system
+│   │   ├── __init__.py
+│   │   ├── nlp_processor.py
+│   │   └── enhanced_chatbot.py  # 🆕 Full LLM integration
+│   ├── geovisor/          # Interactive maps & visualization
+│   │   ├── __init__.py
+│   │   └── map_manager.py
+│   ├── mcp_server/        # MCP protocol support
+│   │   └── __init__.py
+│   └── utils/             # Utility functions
+│       └── __init__.py
+├── static/                # Static assets
+├── templates/             # HTML templates
+├── data/                  # Data storage
+└── logs/                  # Application logs
+```
+
+## 🚀 **Quick Start Options**
+
+### **Option 1: Enhanced AI Version (Recommended)**
+```bash
+# Extract the package
+tar -xzf weather_app_complete_ENHANCED.tar.gz
+cd weather_app_complete
+
+# Set Gemini API key for full AI features
+export GEMINI_API_KEY="your-gemini-api-key"
+
+# Run enhanced version
+./run_enhanced.sh
+```
+
+### **Option 2: Standalone Version (Hugging Face)**
+```bash
+# For Hugging Face Spaces deployment
+python app.py
+```
+
+### **Option 3: Basic Modular Version**
+```bash
+# Basic version without advanced AI
+./run.sh
+```
+
+## 🤖 **AI Configuration**
+
+### **Gemini API Key (Recommended)**
+```bash
+# Get your free API key from: https://makersuite.google.com/app/apikey
+export GEMINI_API_KEY="your-gemini-api-key-here"
+```
+
+### **Optional: Additional AI Providers**
+```bash
+export OPENAI_API_KEY="your-openai-key"
+export CLAUDE_API_KEY="your-claude-key"
+```
+
+## 💬 **Enhanced Conversation Examples**
+
+The AI now provides **intelligent, contextual responses**:
+
+### **Simple Query**
+**User:** "What's the temperature in Wichita?"
+**AI:** "The current temperature in Wichita is 72°F with partly cloudy skies. It's a pleasant day with light winds from the southwest at 8 mph. The humidity is comfortable at 45%, making it feel quite nice outside. Perfect weather for outdoor activities!"
+
+### **Comparison Query**
+**User:** "Compare rain in New York and Miami"
+**AI:** "Great question! Looking at the current conditions:
+
+🗽 **New York**: 15% chance of rain, mostly sunny with 68°F
+🏖️ **Miami**: 65% chance of rain, scattered thunderstorms with 82°F
+
+Miami has a significantly higher chance of precipitation today - about 50% more likely than New York. This is typical for Miami's subtropical climate, especially during summer months when afternoon thunderstorms are common. New York is experiencing a drier weather pattern today with high pressure dominating the region."
+
+### **Complex Analysis**
+**User:** "Should I plan outdoor activities in Denver this weekend?"
+**AI:** "Based on Denver's weekend forecast, here's my recommendation:
+
+**Saturday**: Excellent for outdoor activities! 75°F, sunny skies, light winds. Perfect hiking or biking weather.
+
+**Sunday**: Plan indoor alternatives. 30% chance of afternoon thunderstorms, temperatures dropping to 65°F with gusty winds up to 25 mph.
+
+**Recommendation**: Schedule your outdoor activities for Saturday morning through early afternoon. If you must be outside Sunday, plan for earlier in the day before the storm system moves in around 2 PM."
+
+## 🌟 **Key Enhancements**
+
+### **🧠 Intelligent Responses**
+- **Context Awareness**: Remembers conversation history
+- **Weather Expertise**: Trained on meteorological knowledge
+- **Practical Advice**: Offers actionable recommendations
+- **Natural Language**: Human-like conversation flow
+
+### **🗺️ Dynamic Map Integration**
+- **Auto-Focus**: Map zooms to mentioned cities automatically
+- **Smart Markers**: Rich weather information in popups
+- **Comparison Lines**: Visual connections between compared cities
+- **Weather Layers**: Precipitation radar and overlays
+
+### **📊 Advanced Analytics**
+- **Real-time Processing**: Instant weather data analysis
+- **Trend Recognition**: Identifies weather patterns
+- **Anomaly Detection**: Highlights unusual conditions
+- **Predictive Insights**: Future weather implications
+
+## 🔧 **Technical Features**
+
+### **LlamaIndex Integration**
+- **Vector Store**: Efficient weather knowledge retrieval
+- **Chat Memory**: Maintains conversation context
+- **Document Processing**: Weather data understanding
+- **Query Engine**: Intelligent information synthesis
+
+### **Gemini API Features**
+- **Advanced NLP**: Superior language understanding
+- **Context Retention**: Multi-turn conversations
+- **Weather Expertise**: Domain-specific knowledge
+- **Error Handling**: Graceful fallback mechanisms
+
+### **Robust Architecture**
+- **Modular Design**: Clean, maintainable code
+- **Error Recovery**: Handles API failures gracefully
+- **Performance Optimized**: Fast response times
+- **Scalable**: Ready for production deployment
+
+## 📱 **Deployment Options**
+
+### **Hugging Face Spaces**
+1. Upload `app.py` and `requirements.txt`
+2. Add `GEMINI_API_KEY` in Space settings
+3. Select Gradio SDK
+4. Auto-deploys with full AI features!
+
+### **Local Development**
+```bash
+pip install -r requirements.txt
+export GEMINI_API_KEY="your-key"
+python enhanced_main.py
+```
+
+### **Docker Deployment**
+```dockerfile
+FROM python:3.9
+COPY . /app
+WORKDIR /app
+RUN pip install -r requirements.txt
+ENV GEMINI_API_KEY="your-key"
+CMD ["python", "enhanced_main.py"]
+```
+
+## 🆘 **Troubleshooting**
+
+### **AI Features Not Working**
+1. Verify Gemini API key is set correctly
+2. Check internet connection
+3. Ensure API key has proper permissions
+4. App works in basic mode without API key
+
+### **Import Errors**
+1. Install all requirements: `pip install -r requirements.txt`
+2. Check Python version (3.7+ required)
+3. Verify all files are in correct structure
+
+### **Performance Issues**
+1. Ensure stable internet connection
+2. Check system resources (512MB RAM minimum)
+3. Try basic version if enhanced is slow
+
+## 🌟 **What's New in Enhanced Version**
+
+✅ **Full LlamaIndex Integration** - Advanced AI framework  
+✅ **Gemini API Integration** - Google's powerful LLM  
+✅ **Intelligent Conversations** - Context-aware responses  
+✅ **Enhanced NLP** - Better query understanding  
+✅ **Memory Management** - Conversation history retention  
+✅ **Weather Expertise** - Domain-specific knowledge  
+✅ **Fallback Systems** - Works without API keys  
+✅ **Performance Optimized** - Fast, efficient processing  
+
+---
+
+**🌟 The Most Advanced Weather Intelligence Platform**
+
+*Complete AI integration with LlamaIndex & Gemini for truly intelligent weather conversations!*
+

README_HF.md

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+---
+title: Weather App Pro Enhanced
+emoji: 🌤️
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.0.0
+app_file: hf_app.py
+pinned: false
+license: mit
+short_description: AI-powered weather assistant with LlamaIndex & Gemini integration
+---
+
+# 🌤️ Weather App Pro Enhanced
+
+## 🚀 Overview
+
+An intelligent weather application powered by **LlamaIndex** and **Google Gemini 2.0 Flash** that provides natural language weather interactions, real-time forecasts, and interactive maps.
+
+## ✨ Features
+
+### 🤖 AI-Powered Chat Assistant
+- **Natural Language Processing**: Ask questions in plain English
+- **Smart City Recognition**: Understands 100+ US cities and variations
+- **Intelligent Comparisons**: Compare weather between multiple cities
+- **Contextual Responses**: Powered by Google Gemini 2.0 Flash
+- **LlamaIndex Integration**: Advanced document understanding
+
+### 🗺️ Interactive Weather Maps
+- **Auto-Zoom**: Automatically focuses on mentioned cities  
+- **Real-time Data**: Official weather.gov API integration
+- **Dark Theme**: Professional appearance optimized for viewing
+- **Comparison Mode**: Visual connections between cities
+
+### 📊 Advanced Analytics
+- **7-Day Forecasts**: Detailed predictions with interactive charts
+- **Weather Alerts**: Real-time severe weather monitoring
+- **Temperature Trends**: Visual analysis with Plotly charts
+- **Precipitation Tracking**: Hourly probability forecasts
+
+## 💬 How to Use
+
+1. **Natural Questions**: "What's the weather in Boston?"
+2. **City Comparisons**: "Compare temperature in Miami and Seattle"  
+3. **Specific Queries**: "Is it rainy in Wichita?"
+4. **Weather Patterns**: "Show me the forecast for New York"
+
+## 🛠️ Technology Stack
+
+- **Frontend**: Gradio with custom CSS themes
+- **AI**: Google Gemini 2.0 Flash + LlamaIndex framework
+- **Data**: National Weather Service (weather.gov) API
+- **Maps**: Folium with OpenStreetMap integration
+- **Charts**: Plotly for interactive visualizations
+- **NLP**: Custom natural language processing engine
+
+## ⚙️ Configuration
+
+### For Full AI Features
+Set your **Gemini API key** as a Hugging Face Space secret:
+- Secret Name: `GEMINI_API_KEY`
+- Secret Value: Your Google AI Studio API key
+
+### API Key Setup
+1. Visit [Google AI Studio](https://makersuite.google.com/app/apikey)
+2. Create a new API key
+3. Add it as a Space secret in your Hugging Face repository
+
+## 📍 City Coverage
+
+**Supported US cities include:**
+- All major metropolitan areas (NYC, LA, Chicago, etc.)
+- State capitals and major cities
+- Popular tourist destinations  
+- 100+ cities with intelligent disambiguation
+
+**City Disambiguation Examples:**
+- "Wichita" → Defaults to Wichita, KS
+- "Portland" → Defaults to Portland, OR
+- "Springfield" → Defaults to Springfield, IL
+
+## 🌟 Example Queries
+
+```
+"What's the temperature in Wichita?"
+"Compare rain in New York and Miami"
+"Show me weather in Los Angeles"
+"Is it going to be windy in Chicago tomorrow?"
+"What's the difference between Dallas and Austin weather?"
+```
+
+## 🔧 Technical Details
+
+### AI Integration
+- **Model**: Google Gemini 2.0 Flash (latest experimental)
+- **Framework**: LlamaIndex for document understanding
+- **Context Window**: 3000 tokens for conversation memory
+- **Response Generation**: Contextual with real weather data
+
+### Data Sources
+- **Weather API**: National Weather Service (weather.gov)
+- **Geocoding**: Built-in US city database with coordinates
+- **Maps**: OpenStreetMap with custom weather overlays
+- **Alerts**: Real-time weather warning system
+
+### Performance
+- **Response Time**: < 3 seconds for AI responses
+- **Data Freshness**: Real-time weather updates
+- **Caching**: Intelligent forecast caching
+- **Fallback**: Graceful degradation without API keys
+
+## 🐛 Troubleshooting
+
+### Limited Features?
+- Check that `GEMINI_API_KEY` is set as a Space secret
+- Verify API key is valid and has quota remaining
+- App works in basic mode without AI features
+
+### City Not Found?
+- Try full city name with state (e.g., "Portland, Oregon")
+- Check spelling of city name
+- Currently supports US cities only
+
+### Map Not Loading?
+- Check internet connection for map tiles
+- Allow some time for initial map rendering
+- Try refreshing the page
+
+## 📄 License
+
+MIT License - Feel free to use and modify!
+
+## 🙏 Acknowledgments
+
+- **Data**: National Weather Service for weather data
+- **AI**: Google for Gemini API access  
+- **Framework**: LlamaIndex team for document AI
+- **Interface**: Gradio team for the web framework
+- **Maps**: OpenStreetMap contributors
+
+---
+
+**Built with ❤️ for weather enthusiasts and AI developers**
+
+*Powered by Google Gemini 2.0 Flash, LlamaIndex, and the National Weather Service*

UI_ENHANCEMENT_SUMMARY.md

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+# 🌤️ Weather App Pro Enhanced - UI Enhancement Summary
+
+## ✨ Complete Interface Redesign Completed
+
+The weather app has been successfully enhanced with a comprehensive premium UI redesign that improves visual appeal, user experience, and overall functionality while maintaining all core features.
+
+## 🎨 Major UI Improvements
+
+### 1. **Premium Dark Theme & Modern Styling**
+- **Enhanced Color Palette**: Switched from basic blue gradients to sophisticated slate/blue professional theme
+- **Modern Gradient Backgrounds**: `linear-gradient(135deg, #0f172a, #1e293b, #334155)`
+- **Improved Typography**: Enhanced font weights, sizing, and spacing with Segoe UI family
+- **Professional Shadows**: Added depth with layered box-shadows and backdrop blur effects
+
+### 2. **Advanced Button Styling**
+- **3D Effect Buttons**: Gradient backgrounds with hover transformations
+- **Interactive Animations**: Scale and translateY effects on hover
+- **Enhanced Visual Feedback**: Improved shadow depth and color transitions
+- **Consistent Sizing**: Standardized padding and border-radius across all buttons
+
+### 3. **Input Field Enhancements**
+- **Glassmorphism Design**: Backdrop blur effects with semi-transparent backgrounds
+- **Focus States**: Enhanced border colors and glow effects on focus
+- **Better Contrast**: Improved text color and background contrast for readability
+- **Smooth Transitions**: All input interactions have smooth CSS transitions
+
+### 4. **Section Organization & Layout**
+- **Container System**: New `.section-container` class with consistent styling
+- **Hover Effects**: Subtle lift animations on section hover
+- **Visual Hierarchy**: Clear section titles with consistent spacing
+- **Feature Grid Layout**: Organized information in responsive card grids
+
+### 5. **Enhanced Header Design**
+- **Premium Header**: New `.premium-header` with sophisticated styling
+- **Status Badges**: Color-coded badges for AI status, sync status, and real-time features
+- **Better Typography**: Improved font sizing and weight hierarchy
+- **Visual Accents**: Top border gradient for premium feel
+
+## 🔧 Functional Improvements
+
+### 1. **Auto-Sync Status Indicators**
+- Visual feedback showing when chat and forecast sections are synchronized
+- Real-time status badges indicating AI and sync capabilities
+- Enhanced user awareness of app state
+
+### 2. **Improved Chart Containers**
+- Better background styling for charts with backdrop blur
+- Consistent styling across temperature and precipitation charts
+- Enhanced visibility on dark theme
+
+### 3. **Feature Cards & Information Display**
+- Interactive feature cards with hover effects
+- Better organization of app capabilities and examples
+- Responsive grid layout that adapts to screen size
+
+### 4. **Enhanced Visual Feedback**
+- Loading states and interactive feedback
+- Improved button states and hover effects
+- Better visual hierarchy for information display
+
+## 📱 Mobile Responsiveness
+
+### 1. **Responsive Design Elements**
+- CSS media queries for mobile optimization
+- Adaptive grid layouts that stack on smaller screens
+- Consistent padding and margin adjustments for mobile
+
+### 2. **Touch-Friendly Interface**
+- Larger button sizes for better touch interaction
+- Improved spacing for mobile interaction
+- Consistent styling across different screen sizes
+
+## 🎯 User Experience Enhancements
+
+### 1. **Visual Hierarchy**
+- Clear section titles with emoji icons and consistent styling
+- Better contrast and readability across all elements
+- Logical flow and organization of information
+
+### 2. **Interactive Elements**
+- Smooth animations and transitions throughout the interface
+- Consistent hover states and interactive feedback
+- Professional styling that feels modern and polished
+
+### 3. **Information Architecture**
+- Organized feature cards showing app capabilities
+- Clear separation between different functional areas
+- Better use of visual space and information density
+
+## 🔄 Maintained Core Functionality
+
+### ✅ All Original Features Preserved
+- **AI-Powered Chat**: Full conversational AI with Google Gemini integration
+- **City Synchronization**: Auto-sync between chat and forecast sections
+- **Auto-Clear Functionality**: Forecast input clears after processing
+- **Interactive Maps**: Dynamic weather map with city markers
+- **Professional Charts**: Temperature and precipitation visualizations
+- **Weather Alerts**: Real-time alert monitoring
+- **7-Day Forecasts**: Comprehensive weather forecasts with emojis
+
+### ✅ Enhanced Existing Features
+- Better visual presentation of all features
+- Improved user guidance and examples
+- Enhanced accessibility and usability
+- Professional styling that maintains functionality
+
+## 🚀 Technical Implementation
+
+### 1. **CSS Architecture**
+- Modular CSS class system with consistent naming
+- Advanced CSS features: backdrop-filter, gradients, transforms
+- Mobile-first responsive design approach
+- Performance-optimized animations and transitions
+
+### 2. **Component Organization**
+- Structured HTML with semantic class names
+- Consistent styling patterns across all components
+- Reusable design elements and patterns
+
+### 3. **Theme Integration**
+- Dark theme optimization throughout the interface
+- Consistent color palette and styling
+- Professional gradient and shadow systems
+
+## 🎨 Visual Design Elements
+
+### 1. **Color System**
+- **Primary**: Blue gradients (#3b82f6, #6366f1, #8b5cf6)
+- **Background**: Slate gradients (#0f172a, #1e293b, #334155)
+- **Accent**: Green for sync indicators (#10b981)
+- **Status**: Orange/Purple for various badges
+
+### 2. **Typography**
+- **Font Family**: Segoe UI, Tahoma, Geneva, Verdana, sans-serif
+- **Hierarchy**: Clear font weight and size progression
+- **Readability**: Optimized contrast and spacing
+
+### 3. **Spacing & Layout**
+- **Consistent Padding**: 20-40px for sections, 10-25px for elements
+- **Border Radius**: 12-20px for modern rounded corners
+- **Grid System**: Responsive feature card grids
+
+## 📊 Results Summary
+
+### ✅ **Completed Successfully**
+1. **Premium UI Redesign**: Complete visual overhaul with modern styling
+2. **Enhanced User Experience**: Better organization and visual hierarchy
+3. **Maintained Functionality**: All original features preserved and enhanced
+4. **Mobile Optimization**: Responsive design for all screen sizes
+5. **Professional Appearance**: Enterprise-grade visual design
+6. **Improved Accessibility**: Better contrast and interaction feedback
+
+### 🌟 **Key Benefits**
+- **Professional Appearance**: Enterprise-grade visual design
+- **Better Usability**: Improved user guidance and interaction
+- **Modern Design**: Contemporary UI/UX patterns and styling
+- **Enhanced Accessibility**: Better contrast and readability
+- **Responsive Layout**: Works great on all devices
+- **Visual Feedback**: Clear interactive states and animations
+
+## 🔗 Access Information
+
+**Application URL**: http://localhost:7860
+**Status**: ✅ Running successfully with all enhancements
+**AI Features**: ✅ Fully enabled with Gemini API integration
+**All Features**: ✅ Tested and working correctly
+
+---
+
+*The weather app now provides a premium user experience with professional styling while maintaining all the powerful AI and weather functionality that users expect.*

app.py

@@ -0,0 +1,1394 @@
+"""
+Complete Weather App with AI Integration
+Full LlamaIndex and Gemini API integration for intelligent conversations
+"""
+
+import gradio as gr
+import asyncio
+import logging
+import os
+import sys
+from datetime import datetime
+import json
+import plotly.graph_objects as go
+
+# Load environment variables from .env file
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+    print("✅ Loaded .env file successfully")
+except ImportError:
+    print("⚠️  python-dotenv not installed. Install with: pip install python-dotenv")
+    print("🔄 Trying to read environment variables directly...")
+
+# Add src to path
+sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
+
+# Import enhanced modules
+try:
+    from api.weather_client import create_weather_client
+    from chatbot.nlp_processor import create_nlp_processor
+    from chatbot.enhanced_chatbot import create_enhanced_chatbot
+    from geovisor.map_manager import create_map_manager
+    from analysis.climate_analyzer import create_climate_analyzer
+except ImportError as e:
+    print(f"Import error: {e}")
+    print("Falling back to standalone mode...")
+    # Fallback to standalone app
+    exec(open('app.py').read())
+    exit()
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class WeatherAppProEnhanced:
+    """Enhanced Weather App with full AI integration"""
+    
+    def __init__(self):
+        """Initialize the enhanced weather app"""
+        # Get Gemini API key from environment (now supports .env files)
+        self.gemini_api_key = os.getenv("GEMINI_API_KEY")
+        
+        if self.gemini_api_key:
+            print("🤖 Gemini API key found - AI features enabled!")
+            print(f"🔑 API key starts with: {self.gemini_api_key[:10]}...")
+        else:
+            print("⚠️  GEMINI_API_KEY not found in environment variables or .env file.")
+            print("💡 Create a .env file with: GEMINI_API_KEY=your-api-key")
+            print("🔄 App will work in basic mode without AI features.")
+        
+        # Initialize components
+        self.weather_client = create_weather_client()
+        self.nlp_processor = create_nlp_processor()
+        self.enhanced_chatbot = create_enhanced_chatbot(
+            self.weather_client, 
+            self.nlp_processor, 
+            self.gemini_api_key
+        )
+        self.map_manager = create_map_manager()
+        self.climate_analyzer = create_climate_analyzer(self.weather_client)
+        
+        # App state
+        self.current_cities = []
+        self.chat_history = []
+        self.last_weather_data = {}
+        
+        logger.info("Enhanced Weather App initialized successfully")
+    
+    async def process_chat_message(self, message: str, history: list) -> tuple:
+        """Process chat message with enhanced AI"""
+        try:
+            if not message.strip():
+                return history, "", self._create_default_map(), "Please enter a weather question!"
+            
+            # DEBUG: Check if LLM is enabled
+            llm_status = "🤖 LLM Enabled" if self.gemini_api_key else "⚠️ Basic mode (no LLM)"
+            print(f"Processing: '{message}' | {llm_status}")
+            
+            # Add user message to history in messages format
+            history.append({"role": "user", "content": message})
+            
+            # Process with appropriate method based on AI availability
+            if self.gemini_api_key:
+                result = await self._process_with_ai(message, history)
+            else:
+                result = self._process_basic(message, history)
+
+            # DEBUG: Show what the processing returned
+            print(f"Processing result keys: {list(result.keys()) if isinstance(result, dict) else 'Not a dict'}")
+            
+            ai_response = result.get('response', 'Sorry, I could not process your request.')
+            cities = result.get('cities', [])
+            weather_data = result.get('weather_data', {})
+            map_data = result.get('map_data', [])
+            comparison_mode = result.get('comparison_mode', False)
+            
+            # Update app state
+            self.current_cities = cities
+            self.last_weather_data = weather_data
+            
+            # Add AI response to history in messages format
+            history.append({"role": "assistant", "content": ai_response})
+            
+            # Create updated map
+            if map_data:
+                map_html = self.map_manager.create_weather_map(
+                    map_data, 
+                    comparison_mode=comparison_mode,
+                    show_weather_layers=True
+                )
+            else:
+                map_html = self._create_default_map()
+            
+            # Create status message
+            if cities:
+                status = f"🎯 Found weather data for: {', '.join([c.title() for c in cities])}"
+                if comparison_mode:
+                    status += " (Comparison mode active)"
+            else:
+                status = "💬 General weather assistance"
+            
+            return history, "", map_html, status
+            
+        except Exception as e:
+            logger.error(f"Error processing chat message: {e}")
+            error_response = f"I apologize, but I encountered an error: {str(e)}"
+            history.append({"role": "assistant", "content": error_response})
+            return history, "", self._create_default_map(), "❌ Error processing request"
+    
+    def get_detailed_forecast(self, city_input: str) -> str:
+        """Get detailed forecast text for a city"""
+        try:
+            if not city_input.strip():
+                return "Please enter a city name"
+            
+            coords = self.weather_client.geocode_location(city_input)
+            if not coords:
+                return f"City '{city_input}' not found"
+            
+            lat, lon = coords
+            forecast = self.weather_client.get_forecast(lat, lon)
+            
+            if not forecast:
+                return f"No forecast data available for {city_input}"
+            
+            # Create detailed forecast text with emojis
+            forecast_text = f"# 📊 7-Day Forecast for {city_input.title()}\n\n"
+            
+            for i, period in enumerate(forecast[:7]):  # 7-day forecast
+                condition = period.get('shortForecast', 'N/A')
+                weather_emoji = self._get_weather_emoji(condition)
+                temp = period.get('temperature', 'N/A')
+                temp_unit = period.get('temperatureUnit', 'F')
+                wind_speed = period.get('windSpeed', 'N/A')
+                wind_dir = period.get('windDirection', '')
+                precip = period.get('precipitationProbability', 0)
+                
+                # Temperature emoji based on value
+                if isinstance(temp, (int, float)):
+                    if temp >= 85:
+                        temp_emoji = "🔥"
+                    elif temp >= 75:
+                        temp_emoji = "🌡️"
+                    elif temp >= 60:
+                        temp_emoji = "🌡️"
+                    elif temp >= 40:
+                        temp_emoji = "🧊"
+                    else:
+                        temp_emoji = "❄️"
+                else:
+                    temp_emoji = "🌡️"
+                
+                # Day/Night emoji
+                day_night_emoji = "☀️" if period.get('isDaytime', True) else "🌙"
+                
+                forecast_text += f"## {day_night_emoji} {period.get('name', f'Period {i+1}')}\n"
+                forecast_text += f"**{temp_emoji} Temperature:** {temp}°{temp_unit}\n"
+                forecast_text += f"**{weather_emoji} Conditions:** {condition}\n"
+                forecast_text += f"**💨 Wind:** {wind_speed} {wind_dir}\n"
+                forecast_text += f"**🌧️ Rain Chance:** {precip}%\n"
+                
+                # Add detailed forecast with line breaks for readability
+                details = period.get('detailedForecast', 'No details available')
+                if len(details) > 100:
+                    details = details[:100] + "..."
+                forecast_text += f"**📝 Details:** {details}\n\n"
+                forecast_text += "---\n\n"
+            
+            return forecast_text
+            
+        except Exception as e:
+            logger.error(f"Error getting detailed forecast: {e}")
+            return f"Error getting forecast: {str(e)}"
+    
+    def get_weather_alerts(self) -> str:
+        """Get current weather alerts"""
+        try:
+            alerts = self.weather_client.get_alerts()
+            
+            if not alerts:
+                return "# 🟢 No Active Weather Alerts\n\nThere are currently no active weather alerts in the system."
+            
+            alerts_text = f"# 🚨 Active Weather Alerts ({len(alerts)} alerts)\n\n"
+            
+            for alert in alerts[:10]:  # Limit to 10 alerts
+                severity = alert.get('severity', 'Unknown')
+                event = alert.get('event', 'Weather Alert')
+                headline = alert.get('headline', 'No headline available')
+                areas = alert.get('areas', 'Unknown areas')
+                expires = alert.get('expires', 'Unknown expiration')
+                
+                # Color code by severity
+                if severity.lower() == 'severe':
+                    icon = "🔴"
+                elif severity.lower() == 'moderate':
+                    icon = "🟡"
+                else:
+                    icon = "🟠"
+                
+                alerts_text += f"## {icon} {event}\n"
+                alerts_text += f"**Severity:** {severity}\n"
+                alerts_text += f"**Areas:** {areas}\n"
+                alerts_text += f"**Expires:** {expires}\n"
+                alerts_text += f"**Details:** {headline}\n\n"
+                alerts_text += "---\n\n"
+            
+            return alerts_text
+            
+        except Exception as e:
+            logger.error(f"Error getting weather alerts: {e}")
+            return f"# ❌ Error Getting Alerts\n\nError retrieving weather alerts: {str(e)}"
+    
+    def _create_default_map(self) -> str:
+        """Create default map view"""
+        try:
+            return self.map_manager.create_weather_map([])
+        except Exception as e:
+            logger.error(f"Error creating default map: {e}")
+            return """
+            <div style="width: 100%; height: 400px; background: #2c3e50; color: white; 
+                        display: flex; align-items: center; justify-content: center; 
+                        font-family: Arial, sans-serif; border-radius: 10px;">
+                <div style="text-align: center;">
+                    <h3>🗺️ Weather Map</h3>
+                    <p>Ask about weather in a city to see it on the map!</p>
+                </div>
+            </div>
+            """
+    
+    def _create_default_chart(self) -> go.Figure:
+        """Create default empty chart with professional dark styling"""
+        fig = go.Figure()
+        
+        # Add placeholder data
+        fig.add_trace(go.Scatter(
+            x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5', 'Day 6', 'Day 7'],
+            y=[72, 75, 71, 68, 73, 76, 74],
+            mode='lines+markers',
+            name='Temperature (°F)',
+            line=dict(color='#ff6b6b', width=3),
+            marker=dict(size=8, color='#ff6b6b')
+        ))
+        
+        fig.add_trace(go.Bar(
+            x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5', 'Day 6', 'Day 7'],
+            y=[20, 10, 40, 60, 30, 15, 25],
+            name='Precipitation (%)',
+            yaxis='y2',
+            opacity=0.7,
+            marker_color='#4ecdc4'
+        ))
+        
+        fig.update_layout(
+            title="📈 7-Day Weather Forecast",
+            xaxis_title="Days",
+            yaxis_title="Temperature (°F)",
+            yaxis2=dict(
+                title="Precipitation (%)",
+                overlaying='y',
+                side='right',
+                range=[0, 100]
+            ),
+            template='plotly_dark',
+            height=400,
+            showlegend=True,
+            paper_bgcolor='rgba(0,0,0,0)',
+            plot_bgcolor='rgba(0,0,0,0)',
+            font=dict(color='white'),
+            title_font=dict(size=16, color='white')
+        )
+        
+        return fig
+    
+    def _get_weather_emoji(self, condition: str) -> str:
+        """Get appropriate emoji for weather condition"""
+        condition_lower = condition.lower()
+        
+        if any(word in condition_lower for word in ['sunny', 'clear', 'fair']):
+            return '☀️'
+        elif any(word in condition_lower for word in ['partly cloudy', 'partly sunny']):
+            return '⛅'
+        elif any(word in condition_lower for word in ['cloudy', 'overcast']):
+            return '☁️'
+        elif any(word in condition_lower for word in ['rain', 'shower', 'drizzle']):
+            return '🌧️'
+        elif any(word in condition_lower for word in ['thunderstorm', 'storm', 'lightning']):
+            return '⛈️'
+        elif any(word in condition_lower for word in ['snow', 'snowy', 'blizzard']):
+            return '❄️'
+        elif any(word in condition_lower for word in ['fog', 'mist', 'haze']):
+            return '🌫️'
+        elif any(word in condition_lower for word in ['wind', 'breezy', 'gusty']):
+            return '💨'
+        else:
+            return '🌤️'
+
+    # ...existing code...
+    
+    async def _process_with_ai(self, message: str, history: list) -> dict:
+        """Process message with full AI integration and weather context"""
+        try:
+            # Check if query lacks cities but might reference previous context
+            enhanced_message = await self._inject_context_if_needed(message, history)
+            
+            # Use the enhanced chatbot's process_weather_query method for full AI integration
+            result = await self.enhanced_chatbot.process_weather_query(
+                enhanced_message, 
+                chat_history=history
+            )
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Error in AI processing: {e}")
+            return {
+                'response': f"I encountered an error processing your request: {str(e)}",
+                'cities': [],
+                'weather_data': {},
+                'map_data': [],
+                'query_analysis': {},
+                'map_update_needed': False,
+                'comparison_mode': False
+            }
+    
+    def _process_basic(self, message: str, history: list) -> dict:
+        """Process message with basic functionality (no AI)"""
+        try:
+            # Parse the query using NLP
+            query_analysis = self.nlp_processor.process_query(message)
+            cities = query_analysis.get('cities', [])
+            query_type = query_analysis.get('query_type', 'general')
+            is_comparison = query_analysis.get('comparison_info', {}).get('is_comparison', False)
+            
+            # Basic city geocoding
+            geocoded_cities = []
+            weather_data = {}
+            
+            for city in cities:
+                coords = self.weather_client.geocode_location(city)
+                if coords:
+                    lat, lon = coords
+                    forecast = self.weather_client.get_forecast(lat, lon)
+                    current_obs = self.weather_client.get_current_observations(lat, lon)
+                    
+                    weather_data[city] = {
+                        'name': city,
+                        'coordinates': coords,
+                        'forecast': forecast,
+                        'current': current_obs
+                    }
+                    geocoded_cities.append(city)
+            
+            # Generate basic response
+            basic_response = self._generate_basic_response(
+                message, weather_data, query_analysis
+            )
+            
+            return {
+                'response': basic_response,
+                'cities': geocoded_cities,
+                'weather_data': weather_data,
+                'map_data': self._prepare_map_data(geocoded_cities, weather_data),
+                'query_analysis': query_analysis,
+                'map_update_needed': len(geocoded_cities) > 0,
+                'comparison_mode': is_comparison
+            }
+            
+        except Exception as e:
+            logger.error(f"Error in basic processing: {e}")
+            return {
+                'response': f"I encountered an error: {str(e)}",
+                'cities': [],
+                'weather_data': {},
+                'map_data': [],
+                'query_analysis': {},
+                'map_update_needed': False,
+                'comparison_mode': False
+            }
+    
+    async def _geocode_with_disambiguation(self, city: str) -> dict:
+        """Enhanced geocoding with disambiguation for multiple matches"""
+        try:
+            # Common city disambiguation patterns
+            city_mappings = {
+                'wichita': 'Wichita, KS',  # Default to Kansas
+                'portland': 'Portland, OR',  # Default to Oregon
+                'springfield': 'Springfield, IL',  # Default to Illinois
+                'columbia': 'Columbia, SC',  # Default to South Carolina
+                'franklin': 'Franklin, TN',  # Default to Tennessee
+                'manchester': 'Manchester, NH',  # Default to New Hampshire
+                'canton': 'Canton, OH',  # Default to Ohio
+                'auburn': 'Auburn, AL',  # Default to Alabama
+            }
+            
+            # Check for disambiguation
+            city_lower = city.lower().strip()
+            if city_lower in city_mappings:
+                disambiguated_city = city_mappings[city_lower]
+                coords = self.weather_client.geocode_location(disambiguated_city)
+            else:
+                coords = self.weather_client.geocode_location(city)
+            
+            if not coords:
+                return None
+            
+            lat, lon = coords
+            
+            # Get comprehensive weather data
+            forecast = self.weather_client.get_forecast(lat, lon)
+            current_obs = self.weather_client.get_current_observations(lat, lon)
+            
+            return {
+                'name': city,
+                'original_name': city,
+                'disambiguated_name': city_mappings.get(city_lower, city),
+                'coordinates': coords,
+                'lat': lat,
+                'lon': lon,
+                'forecast': forecast,
+                'current': current_obs
+            }
+            
+        except Exception as e:
+            logger.error(f"Error geocoding {city}: {e}")
+            return None
+    
+    async def _generate_contextual_response(self, message: str, weather_data: dict, query_analysis: dict) -> str:
+        """Generate AI-powered contextual response with weather data"""
+        try:
+            # Format weather context for AI
+            weather_context = self._format_weather_context_for_ai(weather_data, query_analysis)
+            
+            # Enhanced prompt with detailed weather data
+            enhanced_prompt = f"""
+            User Query: {message}
+            
+            Current Weather Data:
+            {weather_context}
+            
+            Query Analysis:
+            - Cities mentioned: {query_analysis.get('cities', [])}
+            - Query type: {query_analysis.get('query_type', 'general')}
+            - Is comparison: {query_analysis.get('comparison_info', {}).get('is_comparison', False)}
+            
+            Please provide a helpful, accurate, and engaging response about the weather.
+            Include specific data from the weather information provided.
+            If comparing cities, highlight key differences with specific numbers.
+            Offer practical advice or insights when relevant.
+            Be conversational and friendly.
+            """
+            
+            # Use the enhanced chatbot to generate response
+            if self.enhanced_chatbot.chat_engine:
+                response = await self.enhanced_chatbot._get_llamaindex_response(enhanced_prompt)
+            elif self.enhanced_chatbot.llm:
+                response = await self.enhanced_chatbot._get_direct_llm_response(enhanced_prompt)
+            elif self.gemini_api_key:
+                response = await self.enhanced_chatbot._get_gemini_response(enhanced_prompt)
+            else:
+                response = self._generate_basic_response(message, weather_data, query_analysis)
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error generating contextual response: {e}")
+            return self._generate_basic_response(message, weather_data, query_analysis)
+    
+    def _format_weather_context_for_ai(self, weather_data: dict, query_analysis: dict) -> str:
+        """Format weather data for AI context with rich details"""
+        if not weather_data:
+            return "No weather data available."
+        
+        context_parts = []
+        
+        for city, data in weather_data.items():
+            forecast = data.get('forecast', [])
+            current = data.get('current', {})
+            
+            city_context = f"\n{city.title()}:"
+            
+            if forecast:
+                current_period = forecast[0]
+                temp = current_period.get('temperature', 'N/A')
+                temp_unit = current_period.get('temperatureUnit', 'F')
+                conditions = current_period.get('shortForecast', 'N/A')
+                wind_speed = current_period.get('windSpeed', 'N/A')
+                wind_dir = current_period.get('windDirection', '')
+                precip = current_period.get('precipitationProbability', 0)
+                
+                city_context += f"""
+                - Current Temperature: {temp}°{temp_unit}
+                - Conditions: {conditions}
+                - Wind: {wind_speed} {wind_dir}
+                - Precipitation Chance: {precip}%
+                - Detailed Forecast: {current_period.get('detailedForecast', 'N/A')[:200]}...
+                """
+                
+                # Add next few periods for context
+                if len(forecast) > 1:
+                    city_context += "\n  Next periods:"
+                    for i, period in enumerate(forecast[1:4], 1):
+                        name = period.get('name', f'Period {i+1}')
+                        temp = period.get('temperature', 'N/A')
+                        conditions = period.get('shortForecast', 'N/A')
+                        city_context += f"\n    - {name}: {temp}°F, {conditions}"
+            
+            if current:
+                temp_c = current.get('temperature')
+                if temp_c:
+                    temp_f = (temp_c * 9/5) + 32
+                    city_context += f"\n- Observed Temperature: {temp_f:.1f}°F"
+                
+                humidity = current.get('relativeHumidity', {})
+                if isinstance(humidity, dict):
+                    humidity_val = humidity.get('value')
+                    if humidity_val:
+                        city_context += f"\n- Humidity: {humidity_val}%"
+            
+            context_parts.append(city_context)
+        
+        return "\n".join(context_parts)
+    
+    def _generate_basic_response(self, message: str, weather_data: dict, query_analysis: dict) -> str:
+        """Generate basic response without AI"""
+        cities = query_analysis.get('cities', [])
+        query_type = query_analysis.get('query_type', 'general')
+        is_comparison = query_analysis.get('comparison_info', {}).get('is_comparison', False)
+        
+        if not cities:
+            return "I'd be happy to help with weather information! Please specify a city you're interested in."
+        
+        if len(cities) == 1:
+            city = cities[0]
+            if city in weather_data:
+                return self._generate_single_city_response(city, weather_data[city], query_type)
+            else:
+                return f"I couldn't find weather data for {city.title()}. Please check the city name."
+        
+        elif is_comparison and len(cities) >= 2:
+            return self._generate_comparison_response(cities, weather_data, query_type)
+        
+        return "I can help you with weather information for US cities. Try asking about temperature, conditions, or comparing cities!"
+    
+    def _generate_single_city_response(self, city: str, city_data: dict, query_type: str) -> str:
+        """Generate response for single city"""
+        forecast = city_data.get('forecast', [])
+        if not forecast:
+            return f"Sorry, I couldn't get weather data for {city.title()}."
+        
+        current = forecast[0]
+        temp = current.get('temperature', 'N/A')
+        temp_unit = current.get('temperatureUnit', 'F')
+        conditions = current.get('shortForecast', 'N/A')
+        wind = current.get('windSpeed', 'N/A')
+        wind_dir = current.get('windDirection', '')
+        precip = current.get('precipitationProbability', 0)
+        
+        if query_type == 'temperature':
+            return f"🌡️ The current temperature in **{city.title()}** is **{temp}°{temp_unit}**. Conditions are {conditions.lower()}."
+        elif query_type == 'precipitation':
+            return f"🌧️ In **{city.title()}**, there's a **{precip}% chance of precipitation**. Current conditions: {conditions}."
+        elif query_type == 'wind':
+            return f"💨 Wind in **{city.title()}** is **{wind} {wind_dir}**. Current conditions: {conditions}."
+        else:  # general
+            return f"""
+🌤️ **Weather in {city.title()}:**
+- **Temperature:** {temp}°{temp_unit}
+- **Conditions:** {conditions}
+- **Wind:** {wind} {wind_dir}
+- **Rain Chance:** {precip}%
+
+*I've updated the map to show {city.title()}. Click the marker for more details!*
+"""
+    
+    def _generate_comparison_response(self, cities: list, weather_data: dict, query_type: str) -> str:
+        """Generate response for city comparison"""
+        if len(cities) < 2:
+            return "I need at least two cities to make a comparison."
+        
+        comparison_text = f"Weather comparison between {' and '.join([c.title() for c in cities])}:\n\n"
+        
+        for city in cities:
+            if city in weather_data:
+                forecast = weather_data[city].get('forecast', [])
+                if forecast:
+                    current = forecast[0]
+                    temp = current.get('temperature', 'N/A')
+                    conditions = current.get('shortForecast', 'N/A')
+                    precip = current.get('precipitationProbability', 0)
+                    
+                    comparison_text += f"📍 **{city.title()}**: {temp}°F, {conditions}, {precip}% rain chance\n"
+        
+        comparison_text += "\n*Check the map to see both locations with detailed weather data!*"
+        return comparison_text
+    
+    def _generate_fallback_response(self, message: str, query_analysis: dict) -> str:
+        """Generate fallback response when no weather data is available"""
+        cities = query_analysis.get('cities', [])
+        
+        if cities:
+            return f"I couldn't find weather data for {', '.join([c.title() for c in cities])}. Please check the city names and try again."
+        else:
+            return "I can help you with weather information for US cities. Please mention a city you're interested in!"
+    
+    def _prepare_map_data(self, cities: list, weather_data: dict) -> list:
+        """Prepare data for map visualization"""
+        map_data = []
+        
+        for city in cities:
+            if city in weather_data:
+                city_data = weather_data[city]
+                coords = city_data.get('coordinates')
+                forecast = city_data.get('forecast', [])
+                
+                if coords and forecast:
+                    lat, lon = coords
+                    map_data.append({
+                        'name': city,
+                        'lat': lat,
+                        'lon': lon,
+                        'forecast': forecast
+                    })
+        
+        return map_data
+
+    async def _inject_context_if_needed(self, message: str, history: list) -> str:
+        """Inject previous city context if the query lacks explicit cities but seems weather-related"""
+        try:
+            # Quick NLP analysis to see if message has cities
+            query_analysis = self.nlp_processor.process_query(message)
+            cities_in_query = query_analysis.get('cities', [])
+            
+            # If query already has cities, no context injection needed
+            if cities_in_query:
+                return message
+            
+            # Check if this looks like a follow-up weather query
+            weather_related_keywords = [
+                'weather', 'temperature', 'rain', 'snow', 'wind', 'forecast', 
+                'conditions', 'humidity', 'precipitation', 'cloudy', 'sunny',
+                'hot', 'cold', 'warm', 'cool', 'transport', 'transportation',
+                'recommended', 'should i', 'can i', 'good for', 'advice',
+                'biking', 'walking', 'driving', 'outdoor', 'activity'
+            ]
+            
+            message_lower = message.lower()
+            is_weather_related = any(keyword in message_lower for keyword in weather_related_keywords)
+            
+            if not is_weather_related:
+                return message
+            
+            # Look for the most recent cities mentioned in conversation history
+            recent_cities = []
+            
+            # Search through recent conversation history (last 10 messages)
+            for entry in reversed(history[-10:]):
+                if isinstance(entry, dict) and entry.get('role') == 'user':
+                    content = entry.get('content', '')
+                elif isinstance(entry, list) and len(entry) >= 1:
+                    content = entry[0]  # User message in [user, assistant] format
+                else:
+                    continue
+                
+                # Extract cities from this historical message
+                historical_analysis = self.nlp_processor.process_query(content)
+                historical_cities = historical_analysis.get('cities', [])
+                
+                if historical_cities:
+                    recent_cities.extend(historical_cities)
+                    break  # Use the most recent cities found
+            
+            # If we found recent cities, inject them into the current query
+            if recent_cities:
+                # Remove duplicates while preserving order
+                unique_cities = []
+                for city in recent_cities:
+                    if city not in unique_cities:
+                        unique_cities.append(city)
+                
+                # Inject context into the message
+                cities_context = ", ".join(unique_cities[:2])  # Limit to 2 most recent cities
+                enhanced_message = f"{message} (referring to {cities_context})"
+                
+                logger.info(f"Context injection: '{message}' -> '{enhanced_message}'")
+                return enhanced_message
+            
+            return message
+            
+        except Exception as e:
+            logger.error(f"Error in context injection: {e}")
+            return message
+
+    def create_interface(self):
+        """Create the enhanced Gradio interface with integrated forecast functionality"""
+        
+        # Enhanced custom CSS for premium dark theme and modern UI
+        custom_css = """
+        .gradio-container {
+            background: linear-gradient(135deg, #0f172a, #1e293b, #334155) !important;
+            min-height: 100vh;
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
+        }
+        
+        .gr-button {
+            background: linear-gradient(45deg, #3b82f6, #6366f1, #8b5cf6) !important;
+            border: none !important;
+            border-radius: 12px !important;
+            color: white !important;
+            font-weight: 600 !important;
+            box-shadow: 0 4px 15px rgba(59, 130, 246, 0.3) !important;
+            transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
+            padding: 12px 24px !important;
+            font-size: 14px !important;
+        }
+        
+        .gr-button:hover {
+            transform: translateY(-2px) scale(1.02) !important;
+            box-shadow: 0 8px 25px rgba(59, 130, 246, 0.4) !important;
+            background: linear-gradient(45deg, #2563eb, #4f46e5, #7c3aed) !important;
+        }
+        
+        .gr-textbox {
+            background: rgba(15, 23, 42, 0.8) !important;
+            border: 2px solid rgba(59, 130, 246, 0.3) !important;
+            color: #e2e8f0 !important;
+            border-radius: 12px !important;
+            backdrop-filter: blur(10px) !important;
+            transition: all 0.3s ease !important;
+        }
+        
+        .gr-textbox:focus {
+            border-color: rgba(59, 130, 246, 0.6) !important;
+            box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1) !important;
+        }
+        
+        .gr-chatbot {
+            background: rgba(15, 23, 42, 0.6) !important;
+            border-radius: 16px !important;
+            border: 2px solid rgba(59, 130, 246, 0.2) !important;
+            backdrop-filter: blur(20px) !important;
+            box-shadow: inset 0 2px 10px rgba(0, 0, 0, 0.3) !important;
+        }
+        
+        .premium-header {
+            background: linear-gradient(135deg, #1e293b, #334155, #475569);
+            color: white;
+            padding: 40px 30px;
+            border-radius: 20px;
+            margin-bottom: 30px;
+            text-align: center;
+            box-shadow: 0 20px 40px rgba(0, 0, 0, 0.4);
+            border: 1px solid rgba(59, 130, 246, 0.2);
+            position: relative;
+            overflow: hidden;
+        }
+        
+        .premium-header::before {
+            content: '';
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            height: 2px;
+            background: linear-gradient(90deg, #3b82f6, #6366f1, #8b5cf6);
+        }
+        
+        .section-container {
+            background: rgba(15, 23, 42, 0.4) !important;
+            border-radius: 16px !important;
+            padding: 25px !important;
+            margin: 20px 0 !important;
+            border: 2px solid rgba(59, 130, 246, 0.15) !important;
+            backdrop-filter: blur(20px) !important;
+            box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3) !important;
+            transition: all 0.3s ease !important;
+        }
+        
+        .section-container:hover {
+            border-color: rgba(59, 130, 246, 0.3) !important;
+            transform: translateY(-2px) !important;
+            box-shadow: 0 12px 40px rgba(0, 0, 0, 0.4) !important;
+        }
+        
+        .chart-container {
+            background: rgba(15, 23, 42, 0.6) !important;
+            border-radius: 12px !important;
+            padding: 15px !important;
+            border: 1px solid rgba(59, 130, 246, 0.2) !important;
+            backdrop-filter: blur(10px) !important;
+        }
+        
+        .feature-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+            gap: 20px;
+            margin: 20px 0;
+        }
+        
+        .feature-card {
+            background: rgba(15, 23, 42, 0.6);
+            border-radius: 12px;
+            padding: 20px;
+            border: 1px solid rgba(59, 130, 246, 0.2);
+            backdrop-filter: blur(10px);
+            transition: all 0.3s ease;
+        }
+        
+        .feature-card:hover {
+            transform: translateY(-4px);
+            border-color: rgba(59, 130, 246, 0.4);
+            box-shadow: 0 8px 25px rgba(0, 0, 0, 0.3);
+        }
+        
+        .sync-indicator {
+            background: linear-gradient(45deg, #10b981, #059669);
+            color: white;
+            padding: 10px 20px;
+            border-radius: 25px;
+            font-size: 12px;
+            font-weight: 600;
+            text-align: center;
+            margin: 15px 0;
+            box-shadow: 0 4px 15px rgba(16, 185, 129, 0.3);
+            display: inline-block;
+        }
+        
+        .section-title {
+            font-size: 20px;
+            font-weight: 700;
+            color: #60a5fa;
+            margin-bottom: 20px;
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+        
+        .status-badge {
+            background: linear-gradient(45deg, #f59e0b, #d97706);
+            color: white;
+            padding: 6px 12px;
+            border-radius: 20px;
+            font-size: 11px;
+            font-weight: 600;
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+        }
+        
+        .ai-badge {
+            background: linear-gradient(45deg, #8b5cf6, #7c3aed);
+            color: white;
+            padding: 6px 12px;
+            border-radius: 20px;
+            font-size: 11px;
+            font-weight: 600;
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+        }
+        
+        /* Mobile responsiveness */
+        @media (max-width: 768px) {
+            .premium-header {
+                padding: 25px 20px;
+            }
+            
+            .section-container {
+                padding: 20px;
+                margin: 15px 0;
+            }
+            
+            .feature-grid {
+                grid-template-columns: 1fr;
+                gap: 15px;
+            }
+        }
+        """
+        
+        with gr.Blocks(css=custom_css, title="🌤️ Weather App", theme=gr.themes.Base()) as app:
+            
+            # Premium Header
+            gr.HTML("""
+            <div class="premium-header">
+                <h1 style="font-size: 2.5rem; margin-bottom: 15px; font-weight: 800;">
+                    🌤️ Weather App
+                </h1>
+                <p style="font-size: 1.2rem; margin-bottom: 10px; opacity: 0.9;">
+                    🤖 AI-Powered Weather Intelligence Platform
+                </p>
+                <div style="display: flex; justify-content: center; gap: 10px; margin: 20px 0; flex-wrap: wrap;">
+                    <span class="ai-badge">🧠 AI ENABLED</span>
+                    <span class="status-badge">🔗 SYNC ACTIVE</span>
+                    <span class="status-badge">📊 REAL-TIME</span>
+                </div>
+                <p style="font-size: 1rem; opacity: 0.8; max-width: 600px; margin: 0 auto;">
+                    <strong>✨ Experience intelligent weather conversations ✨</strong><br>
+                    Ask natural questions, compare cities, get forecasts, and explore interactive maps
+                </p>
+            </div>
+            """)
+            
+            # Main AI Weather Assistant Section
+            with gr.Group(elem_classes=["section-container"]):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        gr.HTML('<div class="section-title">💬 AI Weather Assistant</div>')
+                        chatbot = gr.Chatbot(
+                            label="Enhanced Weather Assistant",
+                            height=400,
+                            placeholder="👋 Hi! I'm your AI weather assistant. Ask me about weather in any city, compare locations, or use the forecast section below!",
+                            elem_classes=["dark"],
+                            type="messages"
+                        )
+                        
+                        with gr.Row():
+                            msg_input = gr.Textbox(
+                                placeholder="Ask about weather: 'Compare rain in Seattle and Portland' or 'What's the forecast for Miami?'",
+                                label="Your Question",
+                                scale=4,
+                                elem_classes=["dark"]
+                            )
+                            send_btn = gr.Button("🚀 Send", scale=1)
+                        
+                        clear_btn = gr.Button("🗑️ Clear Chat")
+                        
+                        gr.HTML("""
+                        <div class="feature-grid">
+                            <div class="feature-card">
+                                <h4>🧠 Smart Conversations</h4>
+                                <p>"What's the weather like in Denver today?"</p>
+                            </div>
+                            <div class="feature-card">
+                                <h4>🔍 City Comparisons</h4>
+                                <p>"Compare temperature between Austin and Houston"</p>
+                            </div>
+                            <div class="feature-card">
+                                <h4>🌂 Activity Advice</h4>
+                                <p>"Should I bring an umbrella in Portland?"</p>
+                            </div>
+                            <div class="feature-card">
+                                <h4>💨 Specialized Queries</h4>
+                                <p>"Wind conditions for sailing in San Francisco"</p>
+                            </div>
+                        </div>
+                        """)
+                    
+                    with gr.Column(scale=1):
+                        gr.HTML('<div class="section-title">🗺️ Dynamic Weather Map</div>')
+                        weather_map = gr.HTML(
+                            value=self._get_current_map(),
+                            label="Interactive Map"
+                        )
+                        
+                        gr.HTML("""
+                        <div class="feature-grid">
+                            <div class="feature-card">
+                                <h4>🔍 Auto-Zoom</h4>
+                                <p>Automatically focus on mentioned cities</p>
+                            </div>
+                            <div class="feature-card">
+                                <h4>📍 Rich Markers</h4>
+                                <p>Click for detailed weather information</p>
+                            </div>
+                            <div class="feature-card">
+                                <h4>🔗 Comparison Lines</h4>
+                                <p>Visual connections between cities</p>
+                            </div>
+                            <div class="feature-card">
+                                <h4>🌙 Dark Theme</h4>
+                                <p>Optimized for comfortable viewing</p>
+                            </div>
+                        </div>
+                        """)
+            
+            # Premium Integrated Forecast Section
+            with gr.Group(elem_classes=["section-container"]):
+                gr.HTML('<div class="section-title">📊 Professional Weather Forecasts</div>')
+                gr.HTML('<div class="sync-indicator">🔄 Auto-synced with AI Assistant</div>')
+                gr.Markdown("Get comprehensive 7-day forecasts with professional charts, detailed analysis, and weather emojis")
+                
+                with gr.Row():
+                    forecast_city_input = gr.Textbox(
+                        placeholder="Enter city name (e.g., New York, Los Angeles, Chicago)",
+                        label="🏙️ City for Detailed Forecast",
+                        scale=3
+                    )
+                    get_forecast_btn = gr.Button("📈 Get Professional Forecast", scale=1)
+                
+                forecast_output = gr.Markdown("Enter a city name above to see detailed forecast with weather emojis! 🌤️")
+                
+                with gr.Row():
+                    with gr.Column(elem_classes=["chart-container"]):
+                        temp_chart = gr.Plot(label="🌡️ Temperature Analysis")
+                    with gr.Column(elem_classes=["chart-container"]):
+                        precip_chart = gr.Plot(label="🌧️ Precipitation Forecast")
+            
+            # Weather Alerts Section
+            with gr.Group(elem_classes=["section-container"]):
+                gr.HTML('<div class="section-title">🚨 Live Weather Alerts</div>')
+                with gr.Row():
+                    refresh_alerts_btn = gr.Button("🔄 Refresh Alerts")
+                    alerts_output = gr.Markdown("Click 'Refresh Alerts' to see current weather alerts.")
+            
+            # Enhanced About Section
+            with gr.Group(elem_classes=["section-container"]):
+                gr.HTML('<div class="section-title">ℹ️ About Weather App</div>')
+                
+                gr.HTML("""
+                <div class="feature-grid">
+                    <div class="feature-card">
+                        <h3>🤖 AI-Powered Chat</h3>
+                        <p>Natural language processing with Google Gemini for intelligent weather conversations</p>
+                    </div>
+                    <div class="feature-card">
+                        <h3>📊 Professional Charts</h3>
+                        <p>Interactive visualizations with temperature trends and precipitation forecasts</p>
+                    </div>
+                    <div class="feature-card">
+                        <h3>🗺️ Dynamic Maps</h3>
+                        <p>Real-time weather visualization with auto-zoom and comparison features</p>
+                    </div>
+                    <div class="feature-card">
+                        <h3>🔄 Smart Sync</h3>
+                        <p>Automatic synchronization between chat and forecast sections</p>
+                    </div>
+                    <div class="feature-card">
+                        <h3>⚠️ Live Alerts</h3>
+                        <p>Real-time weather alert monitoring and notifications</p>
+                    </div>
+                    <div class="feature-card">
+                        <h3>🌡️ Detailed Forecasts</h3>
+                        <p>Comprehensive 7-day forecasts with weather emojis and analysis</p>
+                    </div>
+                </div>
+                """)
+                
+                gr.HTML("""
+                <div style="text-align: center; margin-top: 30px; padding: 20px; background: rgba(59, 130, 246, 0.1); border-radius: 12px; border: 1px solid rgba(59, 130, 246, 0.2);">
+                    <h3 style="color: #60a5fa; margin-bottom: 15px;">🌟 Built with ❤️ for weather enthusiasts</h3>
+                    <p style="color: #cbd5e1; margin-bottom: 10px;">
+                        <strong>Powered by:</strong> National Weather Service API & Google Gemini AI
+                    </p>
+                    <p style="color: #94a3b8; font-size: 0.9rem;">
+                        Experience the future of weather intelligence with seamless AI integration
+                    </p>
+                </div>
+                """)
+            
+            # Event handlers
+            def handle_chat(message, history):
+                """Handle chat messages with enhanced AI processing and auto-sync cities"""
+                try:
+                    if self.gemini_api_key:
+                        # Use enhanced AI processing
+                        loop = asyncio.new_event_loop()
+                        asyncio.set_event_loop(loop)
+                        result = loop.run_until_complete(
+                            self._process_with_ai(message, history)
+                        )
+                        loop.close()
+                    else:
+                        # Fallback to basic processing
+                        result = self._process_basic(message, history)
+                    
+                    # Update chat history in messages format
+                    new_history = history + [{"role": "user", "content": message}, {"role": "assistant", "content": result['response']}]
+                    
+                    # Update map if needed
+                    new_map = self._get_current_map()
+                    if result.get('map_update_needed', False):
+                        new_map = self._update_map_with_cities(result.get('cities', []), result.get('weather_data', {}))
+                    
+                    # Auto-sync cities to forecast input (get the first city mentioned)
+                    cities = result.get('cities', [])
+                    forecast_city_sync = cities[0] if cities else ""
+                    
+                    return new_history, "", new_map, forecast_city_sync
+                    
+                except Exception as e:
+                    logger.error(f"Error in chat handler: {e}")
+                    error_history = history + [{"role": "user", "content": message}, {"role": "assistant", "content": f"Sorry, I encountered an error: {str(e)}"}]
+                    return error_history, "", self._get_current_map(), ""
+            
+            def handle_forecast(city_name):
+                """Handle detailed forecast requests with auto-clear and enhanced styling"""
+                try:
+                    if not city_name.strip():
+                        return (
+                            "Please enter a city name to get the forecast! 🏙️",
+                            self._create_default_chart(),
+                            self._create_default_chart(),
+                            ""  # Clear input
+                        )
+                    
+                    # Get enhanced forecast text with emojis
+                    forecast_text = self.get_detailed_forecast(city_name)
+                    
+                    # Get coordinates for charts
+                    coords = self.weather_client.geocode_location(city_name)
+                    if not coords:
+                        return (
+                            f"❌ Could not find weather data for '{city_name}'. Please check the city name and try again.",
+                            self._create_default_chart(),
+                            self._create_default_chart(),
+                            ""  # Clear input even on error
+                        )
+                    
+                    lat, lon = coords
+                    
+                    # Get forecast data for charts
+                    forecast_data = self.weather_client.get_forecast(lat, lon)
+                    hourly_data = self.weather_client.get_hourly_forecast(lat, lon, hours=24)
+                    
+                    # Create professional charts
+                    temp_chart = self._create_temperature_chart(forecast_data) if forecast_data else self._create_default_chart()
+                    precip_chart = self._create_precipitation_chart(hourly_data) if hourly_data else self._create_default_chart()
+                    
+                    return forecast_text, temp_chart, precip_chart, ""  # Clear input after successful processing
+                    
+                except Exception as e:
+                    logger.error(f"Error in forecast handler: {e}")
+                    return (
+                        f"❌ Error getting forecast for '{city_name}': {str(e)}",
+                        self._create_default_chart(),
+                        self._create_default_chart(),
+                        ""  # Clear input on error
+                    )
+            
+            # Wire up event handlers with enhanced synchronization
+            send_btn.click(
+                fn=handle_chat,
+                inputs=[msg_input, chatbot],
+                outputs=[chatbot, msg_input, weather_map, forecast_city_input]
+            )
+            
+            msg_input.submit(
+                fn=handle_chat,
+                inputs=[msg_input, chatbot],
+                outputs=[chatbot, msg_input, weather_map, forecast_city_input]
+            )
+            
+            clear_btn.click(
+                fn=lambda: ([], "", ""),
+                outputs=[chatbot, msg_input, forecast_city_input]
+            )
+            
+            get_forecast_btn.click(
+                fn=handle_forecast,
+                inputs=[forecast_city_input],
+                outputs=[forecast_output, temp_chart, precip_chart, forecast_city_input]
+            )
+            
+            forecast_city_input.submit(
+                fn=handle_forecast,
+                inputs=[forecast_city_input],
+                outputs=[forecast_output, temp_chart, precip_chart, forecast_city_input]
+            )
+            
+            refresh_alerts_btn.click(
+                fn=self.get_weather_alerts,
+                outputs=[alerts_output]
+            )
+        
+        return app
+
+    def _get_current_map(self) -> str:
+        """Get the current map HTML"""
+        try:
+            return self.map_manager.create_weather_map([])
+        except Exception as e:
+            logger.error(f"Error getting current map: {e}")
+            return "<div>Map temporarily unavailable</div>"
+    
+    def _update_map_with_cities(self, cities: list, weather_data: dict) -> str:
+        """Update map with city markers"""
+        try:
+            # Transform weather_data to the format expected by map manager
+            cities_data = []
+            for city in cities:
+                if city in weather_data:
+                    city_data = weather_data[city]
+                    coords = city_data.get('coordinates')
+                    if coords:
+                        lat, lon = coords
+                        cities_data.append({
+                            'name': city,
+                            'lat': lat,
+                            'lon': lon,
+                            'forecast': city_data.get('forecast', []),
+                            'current': city_data.get('current', {})
+                        })
+            
+            return self.map_manager.create_weather_map(cities_data)
+        except Exception as e:
+            logger.error(f"Error updating map: {e}")
+            return self._get_current_map()
+
+    def _create_temperature_chart(self, forecast: list) -> go.Figure:
+        """Create professional temperature chart with dark theme and emojis"""
+        if not forecast:
+            return self._create_default_chart()
+        
+        try:
+            dates = []
+            temps_high = []
+            temps_low = []
+            
+            for day in forecast[:7]:  # 7-day forecast
+                dates.append(day.get('name', 'Unknown'))
+                temps_high.append(day.get('temperature', 0))
+                # For low temps, we'll use a simple estimation
+                high_temp = day.get('temperature', 0)
+                temps_low.append(max(high_temp - 15, high_temp * 0.7))
+            
+            fig = go.Figure()
+            
+            # High temperatures with gradient
+            fig.add_trace(go.Scatter(
+                x=dates,
+                y=temps_high,
+                mode='lines+markers',
+                name='🌡️ High Temp',
+                line=dict(color='#ff6b6b', width=3),
+                marker=dict(size=8, symbol='circle'),
+                fill=None
+            ))
+            
+            # Low temperatures with gradient
+            fig.add_trace(go.Scatter(
+                x=dates,
+                y=temps_low,
+                mode='lines+markers',
+                name='🧊 Low Temp',
+                line=dict(color='#4ecdc4', width=3),
+                marker=dict(size=8, symbol='circle'),
+                fill='tonexty',
+                fillcolor='rgba(78, 205, 196, 0.1)'
+            ))
+            
+            # Professional dark theme styling
+            fig.update_layout(
+                title=dict(
+                    text="🌡️ 7-Day Temperature Forecast",
+                    font=dict(size=16, color='white')
+                ),
+                paper_bgcolor='rgba(0,0,0,0)',
+                plot_bgcolor='rgba(0,0,0,0.1)',
+                font=dict(color='white'),
+                xaxis=dict(
+                    gridcolor='rgba(255,255,255,0.1)',
+                    zerolinecolor='rgba(255,255,255,0.2)'
+                ),
+                yaxis=dict(
+                    title="Temperature (°F)",
+                    gridcolor='rgba(255,255,255,0.1)',
+                    zerolinecolor='rgba(255,255,255,0.2)'
+                ),
+                legend=dict(
+                    bgcolor='rgba(0,0,0,0.5)',
+                    bordercolor='rgba(255,255,255,0.2)'
+                ),
+                hovermode='x unified'
+            )
+            
+            return fig
+            
+        except Exception as e:
+            logger.error(f"Error creating temperature chart: {e}")
+            return self._create_default_chart()
+
+    def _create_precipitation_chart(self, hourly: list) -> go.Figure:
+        """Create professional precipitation chart with dark theme"""
+        if not hourly:
+            return self._create_default_chart()
+        
+        try:
+            hours = []
+            precip_prob = []
+            
+            for hour in hourly[:24]:  # 24-hour forecast
+                time_str = hour.get('startTime', '')
+                if time_str:
+                    # Extract hour from ISO format
+                    try:
+                        from datetime import datetime
+                        dt = datetime.fromisoformat(time_str.replace('Z', '+00:00'))
+                        hours.append(dt.strftime('%I %p'))
+                    except:
+                        hours.append(f"Hour {len(hours)}")
+                else:
+                    hours.append(f"Hour {len(hours)}")
+                
+                # Get precipitation probability
+                prob = hour.get('probabilityOfPrecipitation', {}).get('value', 0) or 0
+                precip_prob.append(prob)
+            
+            fig = go.Figure()
+            
+            # Precipitation probability bars with gradient
+            fig.add_trace(go.Bar(
+                x=hours,
+                y=precip_prob,
+                name='🌧️ Rain Probability',
+                marker=dict(
+                    color=precip_prob,
+                    colorscale='Blues',
+                    colorbar=dict(title="Probability %")
+                ),
+                hovertemplate='%{x}: %{y}% chance of rain<extra></extra>'
+            ))
+            
+            # Professional dark theme styling
+            fig.update_layout(
+                title=dict(
+                    text="🌧️ 24-Hour Precipitation Forecast",
+                    font=dict(size=16, color='white')
+                ),
+                paper_bgcolor='rgba(0,0,0,0)',
+                plot_bgcolor='rgba(0,0,0,0.1)',
+                font=dict(color='white'),
+                xaxis=dict(
+                    title="Time",
+                    gridcolor='rgba(255,255,255,0.1)',
+                    zerolinecolor='rgba(255,255,255,0.2)',
+                    tickangle=45
+                ),
+                yaxis=dict(
+                    title="Precipitation Probability (%)",
+                    gridcolor='rgba(255,255,255,0.1)',
+                    zerolinecolor='rgba(255,255,255,0.2)',
+                    range=[0, 100]
+                ),
+                showlegend=False
+            )
+            
+            return fig
+            
+        except Exception as e:
+            logger.error(f"Error creating precipitation chart: {e}")
+            return self._create_default_chart()
+
+    # ...existing code...
+def main():
+    """Main application entry point"""
+    print("🌤️ Starting Weather App")
+    print("🤖 AI Features:", "✅ Enabled" if os.getenv("GEMINI_API_KEY") else "⚠️  Limited (no API key)")
+    print("📱 App will be available at: http://localhost:7860")
+    
+    try:
+        app_instance = WeatherAppProEnhanced()
+        app = app_instance.create_interface()
+        
+        app.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            share=False,
+            show_error=True
+        )
+        
+    except Exception as e:
+        logger.error(f"Error starting application: {e}")
+        print(f"❌ Error starting app: {e}")
+
+if __name__ == "__main__":
+    main()
+

demo_features.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+"""
+Enhanced Weather App Demo Script
+Demonstrates the new conversational features
+"""
+
+import asyncio
+from src.chatbot.nlp_processor import WeatherNLPProcessor
+from src.api.weather_client import WeatherClient
+from src.chatbot.enhanced_chatbot import EnhancedWeatherChatbot
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+async def demo_conversational_features():
+    """Demo the enhanced conversational features"""
+    
+    print("🌤️ Enhanced Weather App Demo - Conversational Features\n")
+    print("=" * 60)
+    
+    # Initialize components
+    weather_client = WeatherClient()
+    nlp_processor = WeatherNLPProcessor()
+    gemini_api_key = os.getenv("GEMINI_API_KEY")
+    chatbot = EnhancedWeatherChatbot(weather_client, nlp_processor, gemini_api_key)
+    
+    # Demo queries to test
+    demo_queries = [
+        {
+            "category": "🚴‍♂️ Activity Advice",
+            "queries": [
+                "Should I go biking in Seattle?",
+                "Is it good weather for walking in New York?",
+                "Can I have a picnic in Austin today?"
+            ]
+        },
+        {
+            "category": "📅 Forecast Queries", 
+            "queries": [
+                "What's the forecast for Miami this week?",
+                "Will it rain in Portland tomorrow?",
+                "Hourly weather for Chicago today"
+            ]
+        },
+        {
+            "category": "📊 Historical Comparisons",
+            "queries": [
+                "How does today's weather in Phoenix compare to normal?",
+                "Is this typical weather for Boston?",
+                "Temperature trends for Denver"
+            ]
+        },
+        {
+            "category": "🌍 Multi-City Comparisons",
+            "queries": [
+                "Compare biking conditions between Portland and Seattle",
+                "Weather differences between Miami and Chicago",
+                "Which city is better for outdoor events: Austin or Dallas?"
+            ]
+        }
+    ]
+    
+    for category_info in demo_queries:
+        print(f"\n{category_info['category']}")
+        print("-" * 40)
+        
+        for query in category_info['queries']:
+            print(f"\n💬 Query: \"{query}\"")
+            print("🔍 Analyzing...")
+            
+            # Process the query with NLP analysis
+            analysis = nlp_processor.process_query(query)
+            
+            print(f"   📍 Cities detected: {analysis.get('cities', [])}")
+            print(f"   🎯 Query type: {analysis.get('query_type', 'general')}")
+            print(f"   🏃‍♂️ Activity context: {analysis.get('activity_context', {}).get('has_activity', False)}")
+            print(f"   📅 Forecast intent: {analysis.get('forecast_context', {}).get('has_forecast_intent', False)}")
+            print(f"   📊 Historical intent: {analysis.get('historical_context', {}).get('has_historical_intent', False)}")
+            
+            if analysis.get('activity_context', {}).get('has_activity'):
+                activity_type = analysis.get('activity_context', {}).get('activity_type')
+                print(f"   🎪 Activity type: {activity_type}")
+            
+            if gemini_api_key:
+                try:
+                    # Get AI response (would need actual weather data in real demo)
+                    print("   🤖 AI response would provide contextual advice based on current weather data")
+                except Exception as e:
+                    print(f"   ⚠️ AI processing: {str(e)}")
+            else:
+                print("   ℹ️ Add GEMINI_API_KEY to .env for full AI responses")
+    
+    print("\n" + "=" * 60)
+    print("✅ Demo completed! Run the app with 'python enhanced_main.py' to try these features interactively.")
+    print("🌐 App will be available at: http://localhost:7860")
+    print("\n📖 For more examples, see CONVERSATIONAL_FEATURES.md")
+
+if __name__ == "__main__":
+    asyncio.run(demo_conversational_features())

hf_app.py

@@ -0,0 +1,69 @@
+"""
+Hugging Face Spaces Entry Point for Weather App Pro Enhanced
+"""
+
+import os
+import sys
+import logging
+
+# Set up environment for Hugging Face Spaces
+os.environ['GRADIO_SERVER_NAME'] = '0.0.0.0'
+os.environ['GRADIO_SERVER_PORT'] = '7860'
+
+# Add src to path for imports
+sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
+
+# Configure logging for Hugging Face Spaces
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+def main():
+    """Main entry point for Hugging Face Spaces"""
+    try:
+        # Import and run the enhanced weather app
+        from app import WeatherAppProEnhanced
+        
+        print("🌤️ Starting Weather App Pro Enhanced for Hugging Face Spaces...")
+        print("🤖 AI Features:", "✅ Enabled" if os.getenv("GEMINI_API_KEY") else "⚠️  Limited (set GEMINI_API_KEY secret)")
+        
+        # Create app instance
+        app_instance = WeatherAppProEnhanced()
+        app = app_instance.create_interface()
+        
+        # Launch for Hugging Face Spaces
+        app.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            share=False,
+            show_error=True,
+            enable_queue=True
+        )
+        
+    except Exception as e:
+        logging.error(f"Error starting Weather App: {e}")
+        print(f"❌ Error: {e}")
+        
+        # Fallback to basic app if enhanced fails
+        try:
+            print("🔄 Falling back to basic weather app...")
+            from app import WeatherAppPro
+            
+            basic_app = WeatherAppPro()
+            basic_interface = basic_app.create_interface()
+            
+            basic_interface.launch(
+                server_name="0.0.0.0",
+                server_port=7860,
+                share=False,
+                show_error=True,
+                enable_queue=True
+            )
+            
+        except Exception as fallback_error:
+            logging.error(f"Fallback app also failed: {fallback_error}")
+            print(f"❌ Fallback error: {fallback_error}")
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,17 @@
+gradio>=4.0.0
+requests>=2.25.0
+folium>=0.14.0
+plotly>=5.0.0
+pandas>=1.3.0
+numpy>=1.21.0
+python-dateutil>=2.8.0
+scikit-learn>=1.0.0
+google-generativeai>=0.8.0
+anthropic>=0.52.0
+openai>=1.0.0
+llama-index>=0.12.0
+llama-index-llms-gemini>=0.3.0
+llama-index-embeddings-gemini>=0.3.0
+python-dotenv>=1.0.0
+asyncio
+

run.bat

@@ -0,0 +1,61 @@
+@echo off
+REM Weather App Pro - Quick Start Script for Windows
+REM This script sets up and runs the Weather App Pro
+
+echo 🌤️  Weather App Pro - Quick Start
+echo ==================================
+echo.
+
+REM Check if Python is installed
+python --version >nul 2>&1
+if errorlevel 1 (
+    echo ❌ Python is required but not installed.
+    echo Please install Python and try again.
+    pause
+    exit /b 1
+)
+
+echo ✅ Python found
+
+REM Check if pip is installed
+pip --version >nul 2>&1
+if errorlevel 1 (
+    echo ❌ pip is required but not installed.
+    echo Please install pip and try again.
+    pause
+    exit /b 1
+)
+
+echo ✅ pip found
+
+REM Create virtual environment (optional but recommended)
+echo 🔧 Setting up virtual environment...
+python -m venv weather_env
+
+REM Activate virtual environment
+call weather_env\Scripts\activate.bat
+
+echo ✅ Virtual environment activated
+
+REM Install dependencies
+echo 📦 Installing dependencies...
+pip install -r requirements.txt
+
+if errorlevel 1 (
+    echo ❌ Failed to install dependencies
+    pause
+    exit /b 1
+)
+
+echo ✅ Dependencies installed successfully
+echo.
+echo 🚀 Starting Weather App Pro...
+echo 📱 The app will be available at: http://localhost:7860
+echo 🌟 Press Ctrl+C to stop the application
+echo.
+
+REM Run the application
+python app.py
+
+pause
+

run.sh

@@ -0,0 +1,62 @@
+#!/bin/bash
+
+# Weather App Pro - Quick Start Script
+# This script sets up and runs the Weather App Pro
+
+echo "🌤️  Weather App Pro - Quick Start"
+echo "=================================="
+echo ""
+
+# Check if Python is installed
+if ! command -v python3 &> /dev/null; then
+    echo "❌ Python 3 is required but not installed."
+    echo "Please install Python 3 and try again."
+    exit 1
+fi
+
+echo "✅ Python 3 found"
+
+# Check if pip is installed
+if ! command -v pip3 &> /dev/null; then
+    echo "❌ pip3 is required but not installed."
+    echo "Please install pip3 and try again."
+    exit 1
+fi
+
+echo "✅ pip3 found"
+
+# Create virtual environment (optional but recommended)
+echo "🔧 Setting up virtual environment..."
+python3 -m venv weather_env
+
+# Activate virtual environment
+if [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" ]]; then
+    # Windows
+    source weather_env/Scripts/activate
+else
+    # Linux/Mac
+    source weather_env/bin/activate
+fi
+
+echo "✅ Virtual environment activated"
+
+# Install dependencies
+echo "📦 Installing dependencies..."
+pip3 install -r requirements.txt
+
+if [ $? -eq 0 ]; then
+    echo "✅ Dependencies installed successfully"
+else
+    echo "❌ Failed to install dependencies"
+    exit 1
+fi
+
+echo ""
+echo "🚀 Starting Weather App Pro..."
+echo "📱 The app will be available at: http://localhost:7860"
+echo "🌟 Press Ctrl+C to stop the application"
+echo ""
+
+# Run the application
+python3 app.py
+

run_enhanced.sh

@@ -0,0 +1,72 @@
+#!/bin/bash
+
+# Weather App Pro Enhanced - Quick Start Script
+# This script sets up and runs the Enhanced Weather App Pro with AI
+
+echo "🌤️  Weather App Pro Enhanced - Quick Start"
+echo "=========================================="
+echo ""
+
+# Check if Python is installed
+if ! command -v python3 &> /dev/null; then
+    echo "❌ Python 3 is required but not installed."
+    echo "Please install Python 3 and try again."
+    exit 1
+fi
+
+echo "✅ Python 3 found"
+
+# Check if pip is installed
+if ! command -v pip3 &> /dev/null; then
+    echo "❌ pip3 is required but not installed."
+    echo "Please install pip3 and try again."
+    exit 1
+fi
+
+echo "✅ pip3 found"
+
+# Check for Gemini API key
+if [ -z "$GEMINI_API_KEY" ]; then
+    echo "⚠️  GEMINI_API_KEY not found in environment variables."
+    echo "💡 For full AI features, set it with:"
+    echo "   export GEMINI_API_KEY='your-api-key'"
+    echo "🔄 App will work in basic mode without AI features."
+    echo ""
+fi
+
+# Create virtual environment (optional but recommended)
+echo "🔧 Setting up virtual environment..."
+python3 -m venv weather_env
+
+# Activate virtual environment
+if [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" ]]; then
+    # Windows
+    source weather_env/Scripts/activate
+else
+    # Linux/Mac
+    source weather_env/bin/activate
+fi
+
+echo "✅ Virtual environment activated"
+
+# Install dependencies
+echo "📦 Installing dependencies (this may take a few minutes)..."
+pip3 install -r requirements.txt
+
+if [ $? -eq 0 ]; then
+    echo "✅ Dependencies installed successfully"
+else
+    echo "❌ Failed to install dependencies"
+    exit 1
+fi
+
+echo ""
+echo "🚀 Starting Weather App Pro Enhanced..."
+echo "🤖 AI Features: LlamaIndex + Gemini integration"
+echo "📱 The app will be available at: http://localhost:7860"
+echo "🌟 Press Ctrl+C to stop the application"
+echo ""
+
+# Run the enhanced application
+python3 enhanced_main.py
+

src/ai/multi_provider.py

@@ -0,0 +1,170 @@
+"""
+Multi-Provider AI System for Weather App
+Supports OpenAI, Google Gemini, and Anthropic Claude
+"""
+
+import os
+import logging
+from typing import Dict, List, Optional, Any
+from enum import Enum
+import asyncio
+
+# Import AI clients
+try:
+    import openai
+    OPENAI_AVAILABLE = True
+except ImportError:
+    OPENAI_AVAILABLE = False
+
+try:
+    import google.generativeai as genai
+    GEMINI_AVAILABLE = True
+except ImportError:
+    GEMINI_AVAILABLE = False
+
+try:
+    import anthropic
+    CLAUDE_AVAILABLE = True
+except ImportError:
+    CLAUDE_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+class AIProvider(Enum):
+    """Available AI providers"""
+    OPENAI = "openai"
+    GEMINI = "gemini"
+    CLAUDE = "claude"
+
+class AIProviderManager:
+    """Manages multiple AI providers with fallback"""
+    
+    def __init__(self, 
+                 openai_api_key: str = None,
+                 gemini_api_key: str = None,
+                 claude_api_key: str = None,
+                 default_provider: AIProvider = AIProvider.OPENAI):
+        """Initialize AI provider manager"""
+        self.providers = {}
+        self.available_providers = []
+        self.default_provider = default_provider
+        
+        # Configure OpenAI
+        if OPENAI_AVAILABLE and (openai_api_key or os.getenv("OPENAI_API_KEY")):
+            try:
+                self.providers[AIProvider.OPENAI] = openai.OpenAI(
+                    api_key=openai_api_key or os.getenv("OPENAI_API_KEY")
+                )
+                self.available_providers.append(AIProvider.OPENAI)
+                logger.info("OpenAI configured successfully")
+            except Exception as e:
+                logger.error(f"Error configuring OpenAI: {e}")
+        
+        # Configure Gemini
+        if GEMINI_AVAILABLE and (gemini_api_key or os.getenv("GEMINI_API_KEY")):
+            try:
+                genai.configure(api_key=gemini_api_key or os.getenv("GEMINI_API_KEY"))
+                self.providers[AIProvider.GEMINI] = genai.GenerativeModel('gemini-2.0-flash-exp')
+                self.available_providers.append(AIProvider.GEMINI)
+                logger.info("Gemini 2.0 Flash configured successfully")
+            except Exception as e:
+                logger.error(f"Error configuring Gemini: {e}")
+        
+        # Configure Claude
+        if CLAUDE_AVAILABLE and (claude_api_key or os.getenv("CLAUDE_API_KEY")):
+            try:
+                self.providers[AIProvider.CLAUDE] = anthropic.Anthropic(
+                    api_key=claude_api_key or os.getenv("CLAUDE_API_KEY")
+                )
+                self.available_providers.append(AIProvider.CLAUDE)
+                logger.info("Claude configured successfully")
+            except Exception as e:
+                logger.error(f"Error configuring Claude: {e}")
+        
+        if not self.available_providers:
+            logger.warning("No AI providers configured. Using basic mode.")
+        else:
+            if self.default_provider not in self.available_providers:
+                self.default_provider = self.available_providers[0]
+    
+    def get_available_providers(self) -> List[AIProvider]:
+        """Get list of available providers"""
+        return self.available_providers
+    
+    async def generate_response(self, prompt: str, provider: AIProvider = None) -> str:
+        """Generate response using specified or default provider"""
+        if not self.available_providers:
+            return self._generate_basic_response(prompt)
+        
+        provider = provider or self.default_provider
+        
+        try:
+            if provider == AIProvider.OPENAI and provider in self.providers:
+                return await self._generate_openai_response(prompt)
+            elif provider == AIProvider.GEMINI and provider in self.providers:
+                return await self._generate_gemini_response(prompt)
+            elif provider == AIProvider.CLAUDE and provider in self.providers:
+                return await self._generate_claude_response(prompt)
+            else:
+                # Fallback to first available provider
+                if self.available_providers:
+                    return await self.generate_response(prompt, self.available_providers[0])
+                else:
+                    return self._generate_basic_response(prompt)
+        except Exception as e:
+            logger.error(f"Error generating response with {provider.value}: {e}")
+            # Try fallback providers
+            for fallback_provider in self.available_providers:
+                if fallback_provider != provider:
+                    try:
+                        return await self.generate_response(prompt, fallback_provider)
+                    except:
+                        continue
+            return self._generate_basic_response(prompt)
+    
+    async def _generate_openai_response(self, prompt: str) -> str:
+        """Generate response using OpenAI"""
+        client = self.providers[AIProvider.OPENAI]
+        response = await client.chat.completions.acreate(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=500,
+            temperature=0.7
+        )
+        return response.choices[0].message.content
+    
+    async def _generate_gemini_response(self, prompt: str) -> str:
+        """Generate response using Gemini"""
+        model = self.providers[AIProvider.GEMINI]
+        response = await model.generate_content_async(prompt)
+        return response.text
+    
+    async def _generate_claude_response(self, prompt: str) -> str:
+        """Generate response using Claude"""
+        client = self.providers[AIProvider.CLAUDE]
+        response = await client.messages.acreate(
+            model="claude-3-sonnet-20240229",
+            max_tokens=500,
+            messages=[{"role": "user", "content": prompt}]
+        )
+        return response.content[0].text
+    
+    def _generate_basic_response(self, prompt: str) -> str:
+        """Generate basic response when no AI providers available"""
+        prompt_lower = prompt.lower()
+        
+        if "temperature" in prompt_lower:
+            return "I can help you find temperature information. Please specify a city."
+        elif "rain" in prompt_lower or "precipitation" in prompt_lower:
+            return "I can provide precipitation data. Please specify a location."
+        elif "wind" in prompt_lower:
+            return "I can show wind information. Please specify a city."
+        elif "compare" in prompt_lower:
+            return "I can compare weather between cities. Please specify the cities you'd like to compare."
+        else:
+            return "I'm a weather assistant. Ask me about temperature, precipitation, wind, or weather comparisons for US cities."
+
+def create_ai_provider_manager(**kwargs) -> AIProviderManager:
+    """Factory function to create AI provider manager"""
+    return AIProviderManager(**kwargs)
+

src/analysis/climate_analyzer.py

@@ -0,0 +1,380 @@
+"""
+Advanced Climate Analysis System
+Historical data analysis, trend detection, and anomaly identification
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from typing import List, Dict, Tuple, Optional
+import logging
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import KMeans
+import json
+
+logger = logging.getLogger(__name__)
+
+class ClimateAnalyzer:
+    """Advanced climate analysis with machine learning capabilities"""
+    
+    def __init__(self, weather_client):
+        self.weather_client = weather_client
+        self.scaler = StandardScaler()
+        
+    def analyze_temperature_trends(self, city: str, days: int = 30) -> Dict:
+        """Analyze temperature trends for a city"""
+        try:
+            coords = self.weather_client.geocode_location(city)
+            if not coords:
+                return {'error': f'City {city} not found'}
+            
+            lat, lon = coords
+            
+            # Get historical data (simulated for demo)
+            historical_data = self._generate_historical_data(lat, lon, days)
+            
+            if not historical_data:
+                return {'error': 'No historical data available'}
+            
+            # Convert to DataFrame
+            df = pd.DataFrame(historical_data)
+            df['date'] = pd.to_datetime(df['date'])
+            df = df.sort_values('date')
+            
+            # Calculate trends
+            X = np.arange(len(df)).reshape(-1, 1)
+            y = df['temperature'].values
+            
+            model = LinearRegression()
+            model.fit(X, y)
+            
+            trend_slope = model.coef_[0]
+            trend_direction = 'increasing' if trend_slope > 0 else 'decreasing'
+            
+            # Calculate statistics
+            stats = {
+                'mean_temp': float(np.mean(y)),
+                'max_temp': float(np.max(y)),
+                'min_temp': float(np.min(y)),
+                'std_temp': float(np.std(y)),
+                'trend_slope': float(trend_slope),
+                'trend_direction': trend_direction,
+                'data_points': len(df)
+            }
+            
+            # Detect anomalies
+            anomalies = self._detect_temperature_anomalies(df)
+            
+            return {
+                'city': city,
+                'analysis_period': f'{days} days',
+                'statistics': stats,
+                'anomalies': anomalies,
+                'trend_analysis': {
+                    'slope': trend_slope,
+                    'direction': trend_direction,
+                    'significance': 'significant' if abs(trend_slope) > 0.1 else 'minimal'
+                }
+            }
+            
+        except Exception as e:
+            logger.error(f"Error analyzing temperature trends: {e}")
+            return {'error': str(e)}
+    
+    def compare_cities_climate(self, cities: List[str], metric: str = 'temperature') -> Dict:
+        """Compare climate metrics between multiple cities"""
+        try:
+            results = {}
+            
+            for city in cities:
+                coords = self.weather_client.geocode_location(city)
+                if coords:
+                    lat, lon = coords
+                    forecast = self.weather_client.get_forecast(lat, lon)
+                    
+                    if forecast:
+                        current = forecast[0]
+                        results[city] = {
+                            'temperature': current.get('temperature', 0),
+                            'precipitation_prob': current.get('precipitationProbability', 0),
+                            'wind_speed': self._extract_wind_speed(current.get('windSpeed', '0 mph')),
+                            'conditions': current.get('shortForecast', 'Unknown')
+                        }
+            
+            if not results:
+                return {'error': 'No data available for comparison'}
+            
+            # Perform comparison analysis
+            comparison = self._analyze_city_differences(results, metric)
+            
+            return {
+                'cities': list(results.keys()),
+                'metric': metric,
+                'data': results,
+                'comparison': comparison
+            }
+            
+        except Exception as e:
+            logger.error(f"Error comparing cities: {e}")
+            return {'error': str(e)}
+    
+    def detect_weather_patterns(self, city: str, pattern_type: str = 'seasonal') -> Dict:
+        """Detect weather patterns and cycles"""
+        try:
+            coords = self.weather_client.geocode_location(city)
+            if not coords:
+                return {'error': f'City {city} not found'}
+            
+            lat, lon = coords
+            
+            # Get extended forecast for pattern analysis
+            forecast = self.weather_client.get_forecast(lat, lon)
+            hourly = self.weather_client.get_hourly_forecast(lat, lon, 168)  # 7 days
+            
+            if not forecast or not hourly:
+                return {'error': 'Insufficient data for pattern analysis'}
+            
+            patterns = {}
+            
+            if pattern_type == 'seasonal':
+                patterns = self._analyze_seasonal_patterns(forecast, hourly)
+            elif pattern_type == 'daily':
+                patterns = self._analyze_daily_patterns(hourly)
+            elif pattern_type == 'precipitation':
+                patterns = self._analyze_precipitation_patterns(forecast, hourly)
+            
+            return {
+                'city': city,
+                'pattern_type': pattern_type,
+                'patterns': patterns,
+                'confidence': self._calculate_pattern_confidence(patterns)
+            }
+            
+        except Exception as e:
+            logger.error(f"Error detecting weather patterns: {e}")
+            return {'error': str(e)}
+    
+    def predict_weather_anomalies(self, city: str, days_ahead: int = 7) -> Dict:
+        """Predict potential weather anomalies"""
+        try:
+            coords = self.weather_client.geocode_location(city)
+            if not coords:
+                return {'error': f'City {city} not found'}
+            
+            lat, lon = coords
+            forecast = self.weather_client.get_forecast(lat, lon)
+            
+            if not forecast:
+                return {'error': 'No forecast data available'}
+            
+            # Analyze forecast for anomalies
+            anomalies = []
+            
+            for i, period in enumerate(forecast[:days_ahead]):
+                temp = period.get('temperature', 0)
+                precip = period.get('precipitationProbability', 0)
+                wind_speed = self._extract_wind_speed(period.get('windSpeed', '0 mph'))
+                
+                # Check for temperature anomalies
+                if temp > 100 or temp < -20:  # Extreme temperatures
+                    anomalies.append({
+                        'type': 'extreme_temperature',
+                        'period': period.get('name'),
+                        'value': temp,
+                        'severity': 'high' if temp > 110 or temp < -30 else 'moderate'
+                    })
+                
+                # Check for precipitation anomalies
+                if precip > 80:  # High precipitation probability
+                    anomalies.append({
+                        'type': 'high_precipitation',
+                        'period': period.get('name'),
+                        'value': precip,
+                        'severity': 'high' if precip > 90 else 'moderate'
+                    })
+                
+                # Check for wind anomalies
+                if wind_speed > 25:  # High wind speeds
+                    anomalies.append({
+                        'type': 'high_wind',
+                        'period': period.get('name'),
+                        'value': wind_speed,
+                        'severity': 'high' if wind_speed > 40 else 'moderate'
+                    })
+            
+            return {
+                'city': city,
+                'prediction_period': f'{days_ahead} days',
+                'anomalies_detected': len(anomalies),
+                'anomalies': anomalies,
+                'risk_level': self._calculate_risk_level(anomalies)
+            }
+            
+        except Exception as e:
+            logger.error(f"Error predicting anomalies: {e}")
+            return {'error': str(e)}
+    
+    def _generate_historical_data(self, lat: float, lon: float, days: int) -> List[Dict]:
+        """Generate simulated historical data for analysis"""
+        # In a real implementation, this would fetch actual historical data
+        data = []
+        base_temp = 70  # Base temperature
+        
+        for i in range(days):
+            date = datetime.now() - timedelta(days=days-i)
+            # Add seasonal variation and random noise
+            seasonal_factor = 10 * np.sin(2 * np.pi * date.timetuple().tm_yday / 365)
+            noise = np.random.normal(0, 5)
+            temp = base_temp + seasonal_factor + noise
+            
+            data.append({
+                'date': date.strftime('%Y-%m-%d'),
+                'temperature': round(temp, 1),
+                'humidity': round(50 + np.random.normal(0, 15), 1),
+                'precipitation': round(max(0, np.random.exponential(0.1)), 2)
+            })
+        
+        return data
+    
+    def _detect_temperature_anomalies(self, df: pd.DataFrame) -> List[Dict]:
+        """Detect temperature anomalies using statistical methods"""
+        temps = df['temperature'].values
+        mean_temp = np.mean(temps)
+        std_temp = np.std(temps)
+        
+        anomalies = []
+        threshold = 2 * std_temp  # 2 standard deviations
+        
+        for idx, temp in enumerate(temps):
+            if abs(temp - mean_temp) > threshold:
+                anomalies.append({
+                    'date': df.iloc[idx]['date'].strftime('%Y-%m-%d'),
+                    'temperature': temp,
+                    'deviation': abs(temp - mean_temp),
+                    'type': 'hot' if temp > mean_temp else 'cold'
+                })
+        
+        return anomalies
+    
+    def _analyze_city_differences(self, results: Dict, metric: str) -> Dict:
+        """Analyze differences between cities for a specific metric"""
+        values = [data[metric] for data in results.values()]
+        cities = list(results.keys())
+        
+        max_val = max(values)
+        min_val = min(values)
+        avg_val = np.mean(values)
+        
+        max_city = cities[values.index(max_val)]
+        min_city = cities[values.index(min_val)]
+        
+        return {
+            'highest': {'city': max_city, 'value': max_val},
+            'lowest': {'city': min_city, 'value': min_val},
+            'average': avg_val,
+            'range': max_val - min_val,
+            'std_deviation': np.std(values)
+        }
+    
+    def _analyze_seasonal_patterns(self, forecast: List[Dict], hourly: List[Dict]) -> Dict:
+        """Analyze seasonal weather patterns"""
+        # Simplified seasonal analysis
+        day_temps = []
+        night_temps = []
+        
+        for period in forecast[:14]:  # 2 weeks
+            if period.get('isDaytime'):
+                day_temps.append(period.get('temperature', 0))
+            else:
+                night_temps.append(period.get('temperature', 0))
+        
+        return {
+            'day_night_difference': np.mean(day_temps) - np.mean(night_temps) if day_temps and night_temps else 0,
+            'temperature_stability': np.std(day_temps + night_temps),
+            'pattern_type': 'stable' if np.std(day_temps + night_temps) < 10 else 'variable'
+        }
+    
+    def _analyze_daily_patterns(self, hourly: List[Dict]) -> Dict:
+        """Analyze daily weather patterns"""
+        hourly_temps = []
+        hourly_precip = []
+        
+        for hour in hourly[:24]:  # 24 hours
+            hourly_temps.append(hour.get('temperature', 0))
+            precip_prob = hour.get('probabilityOfPrecipitation', {})
+            if isinstance(precip_prob, dict):
+                hourly_precip.append(precip_prob.get('value', 0))
+            else:
+                hourly_precip.append(precip_prob or 0)
+        
+        return {
+            'temperature_range': max(hourly_temps) - min(hourly_temps) if hourly_temps else 0,
+            'peak_precipitation_hour': hourly_precip.index(max(hourly_precip)) if hourly_precip else 0,
+            'temperature_trend': 'warming' if hourly_temps[-1] > hourly_temps[0] else 'cooling'
+        }
+    
+    def _analyze_precipitation_patterns(self, forecast: List[Dict], hourly: List[Dict]) -> Dict:
+        """Analyze precipitation patterns"""
+        precip_periods = []
+        
+        for period in forecast[:7]:  # 7 days
+            precip_periods.append(period.get('precipitationProbability', 0))
+        
+        return {
+            'average_precipitation_probability': np.mean(precip_periods),
+            'max_precipitation_probability': max(precip_periods),
+            'precipitation_days': sum(1 for p in precip_periods if p > 30),
+            'pattern': 'wet' if np.mean(precip_periods) > 50 else 'dry'
+        }
+    
+    def _calculate_pattern_confidence(self, patterns: Dict) -> float:
+        """Calculate confidence level for detected patterns"""
+        # Simplified confidence calculation
+        if not patterns:
+            return 0.0
+        
+        # Base confidence on data consistency
+        confidence = 0.7  # Base confidence
+        
+        # Adjust based on pattern strength
+        if 'temperature_stability' in patterns:
+            stability = patterns['temperature_stability']
+            if stability < 5:
+                confidence += 0.2
+            elif stability > 15:
+                confidence -= 0.2
+        
+        return min(1.0, max(0.0, confidence))
+    
+    def _calculate_risk_level(self, anomalies: List[Dict]) -> str:
+        """Calculate overall risk level based on anomalies"""
+        if not anomalies:
+            return 'low'
+        
+        high_severity_count = sum(1 for a in anomalies if a.get('severity') == 'high')
+        total_count = len(anomalies)
+        
+        if high_severity_count > 0:
+            return 'high'
+        elif total_count > 3:
+            return 'moderate'
+        else:
+            return 'low'
+    
+    def _extract_wind_speed(self, wind_str: str) -> float:
+        """Extract numeric wind speed from string"""
+        try:
+            # Extract number from string like "10 mph" or "5 to 10 mph"
+            import re
+            numbers = re.findall(r'\d+', wind_str)
+            if numbers:
+                return float(numbers[0])
+            return 0.0
+        except:
+            return 0.0
+
+def create_climate_analyzer(weather_client) -> ClimateAnalyzer:
+    """Factory function to create climate analyzer"""
+    return ClimateAnalyzer(weather_client)
+

src/api/weather_client.py

@@ -0,0 +1,442 @@
+"""
+Advanced Weather API Client
+Comprehensive interface to weather.gov API with enhanced features
+"""
+
+import requests
+import logging
+from typing import List, Dict, Tuple, Optional
+from datetime import datetime, timedelta
+import json
+import time
+from urllib.parse import quote
+
+logger = logging.getLogger(__name__)
+
+class WeatherClient:
+    """Advanced weather client for weather.gov API"""
+    
+    def __init__(self):
+        self.base_url = "https://api.weather.gov"
+        self.geocoding_url = "https://nominatim.openstreetmap.org/search"
+        self.session = requests.Session()
+        self.session.headers.update({
+            'User-Agent': 'WeatherAppPro/1.0 ([email protected])'
+        })
+        
+        # Cache for geocoding results to avoid repeated API calls
+        self.geocoding_cache = {}
+        
+        # Common city aliases and abbreviations for better user experience
+        self.city_aliases = {
+            'nyc': 'New York City, NY',
+            'la': 'Los Angeles, CA',
+            'sf': 'San Francisco, CA',
+            'dc': 'Washington, DC',
+            'philly': 'Philadelphia, PA',
+            'vegas': 'Las Vegas, NV',
+            'chi': 'Chicago, IL',
+            'atx': 'Austin, TX',
+            'h-town': 'Houston, TX',
+            'pdx': 'Portland, OR',
+            'nola': 'New Orleans, LA',
+            'the bay': 'San Francisco Bay Area, CA',
+            'south beach': 'Miami Beach, FL',
+            'motor city': 'Detroit, MI',
+            'space city': 'Houston, TX',
+            'city of angels': 'Los Angeles, CA',
+            'windy city': 'Chicago, IL',
+            'big apple': 'New York City, NY',
+            'music city': 'Nashville, TN',
+            'silicon valley': 'San Jose, CA',
+        }
+        
+        # Fallback coordinate database for common cities when external geocoding fails
+        # This ensures the app works reliably even if external APIs are down
+        self.fallback_coordinates = {
+            # Major US cities with commonly ambiguous names
+            'lincoln': (40.8136, -96.7026),  # Lincoln, NE (most populous Lincoln)
+            'columbia': (34.0007, -81.0348),  # Columbia, SC (state capital)
+            'springfield': (39.7817, -89.6501),  # Springfield, IL (state capital)
+            'portland': (45.5152, -122.6784),  # Portland, OR (larger than Portland, ME)
+            'manchester': (42.9956, -71.4548),  # Manchester, NH
+            'franklin': (35.9251, -86.8689),  # Franklin, TN
+            'canton': (40.7990, -81.3785),  # Canton, OH
+            'auburn': (32.6010, -85.4808),  # Auburn, AL
+            'athens': (33.9519, -83.3576),  # Athens, GA
+            'madison': (43.0731, -89.4012),  # Madison, WI (state capital)
+            'richmond': (37.5407, -77.4360),  # Richmond, VA (state capital)
+            'charleston': (32.7765, -79.9311),  # Charleston, SC
+            'fayetteville': (36.0625, -94.1574),  # Fayetteville, AR
+            'burlington': (44.4759, -73.2121),  # Burlington, VT
+            'dover': (39.1581, -75.5244),  # Dover, DE (state capital)
+            'concord': (43.2081, -71.5376),  # Concord, NH (state capital)
+            'albany': (42.6526, -73.7562),  # Albany, NY (state capital)
+            'jackson': (32.2988, -90.1848),  # Jackson, MS (state capital)
+            'montgomery': (32.3617, -86.2792),  # Montgomery, AL (state capital)
+            'pierre': (44.3683, -100.3510),  # Pierre, SD (state capital)
+            'helena': (46.5956, -112.0362),  # Helena, MT (state capital)
+            'juneau': (58.3019, -134.4197),  # Juneau, AK (state capital)
+            'olympia': (47.0379, -122.9015),  # Olympia, WA (state capital)
+            'salem': (44.9429, -123.0351),  # Salem, OR (state capital)
+            'cheyenne': (41.1400, -104.8197),  # Cheyenne, WY (state capital)
+            'bismarck': (46.8083, -100.7837),  # Bismarck, ND (state capital)
+            'topeka': (39.0473, -95.6890),  # Topeka, KS (state capital)
+            'jefferson city': (38.5767, -92.1735),  # Jefferson City, MO (state capital)
+            'little rock': (34.7465, -92.2896),  # Little Rock, AR (state capital)
+            'harrisburg': (40.2732, -76.8839),  # Harrisburg, PA (state capital)
+            'annapolis': (38.9784, -76.5021),  # Annapolis, MD (state capital)
+            'trenton': (40.2206, -74.7562),  # Trenton, NJ (state capital)
+            'hartford': (41.7658, -72.6734),  # Hartford, CT (state capital)
+            'providence': (41.8240, -71.4128),  # Providence, RI (state capital)
+            'montpelier': (44.2601, -72.5806),  # Montpelier, VT (state capital)
+            'augusta': (44.3106, -69.7795),  # Augusta, ME (state capital)
+            
+            # Additional commonly requested cities
+            'wichita': (37.6872, -97.3301),  # Wichita, KS
+            'lubbock': (33.5779, -101.8552),  # Lubbock, TX
+            'shreveport': (32.5252, -93.7502),  # Shreveport, LA
+            'mobile': (30.6944, -88.0431),  # Mobile, AL
+            'pensacola': (30.4213, -87.2169),  # Pensacola, FL
+            'tallahassee': (30.4518, -84.2807),  # Tallahassee, FL
+            'gainesville': (29.6516, -82.3248),  # Gainesville, FL
+            'eugene': (44.0521, -123.0868),  # Eugene, OR
+            'spokane': (47.6588, -117.4260),  # Spokane, WA
+            'tacoma': (47.2529, -122.4443),  # Tacoma, WA
+            'anchorage': (61.2181, -149.9003),  # Anchorage, AK
+            'honolulu': (21.3099, -157.8581),  # Honolulu, HI
+            
+            # Texas cities (commonly requested)
+            'midland': (31.9973, -102.0779),  # Midland, TX
+            'odessa': (31.8457, -102.3676),  # Odessa, TX
+        }
+    
+    def geocode_location(self, location: str) -> Optional[Tuple[float, float]]:
+        """
+        Advanced geocoding using external API with fallback mechanisms.
+        Supports any city worldwide via OpenStreetMap/Nominatim geocoding.
+        """
+        location_clean = location.strip()
+        location_lower = location_clean.lower()
+        
+        # Check cache first to avoid repeated API calls
+        if location_lower in self.geocoding_cache:
+            logger.info(f"Using cached coordinates for {location}")
+            return self.geocoding_cache[location_lower]
+        
+        # Handle common aliases and abbreviations
+        if location_lower in self.city_aliases:
+            search_location = self.city_aliases[location_lower]
+            logger.info(f"Using alias: {location} -> {search_location}")
+        else:
+            search_location = location_clean
+            
+        # Add country bias for US if not specified
+        if ',' not in search_location and 'usa' not in search_location.lower() and 'united states' not in search_location.lower():
+            search_location += ', USA'
+            
+        try:
+            # Use Nominatim (OpenStreetMap) geocoding service
+            params = {
+                'q': search_location,
+                'format': 'json',
+                'limit': 1,
+                'countrycodes': 'us',  # Prefer US results for weather.gov compatibility
+                'addressdetails': 1,
+                'bounded': 1,
+                'viewbox': '-125,50,-66,25'  # US bounding box
+            }
+            
+            logger.info(f"Geocoding '{search_location}' via Nominatim API")
+            response = self.session.get(
+                self.geocoding_url, 
+                params=params, 
+                timeout=10,
+                headers={'User-Agent': 'WeatherAppPro/1.0 ([email protected])'}
+            )
+            response.raise_for_status()
+            
+            # Rate limiting - Nominatim requests max 1 request per second
+            time.sleep(1)
+            
+            data = response.json()
+            
+            if data and len(data) > 0:
+                result = data[0]
+                lat = float(result['lat'])
+                lon = float(result['lon'])
+                
+                # Validate coordinates are within reasonable US bounds
+                if self._is_valid_us_coordinates(lat, lon):
+                    coords = (lat, lon)
+                    # Cache the result
+                    self.geocoding_cache[location_lower] = coords
+                    
+                    # Log the successful geocoding
+                    display_name = result.get('display_name', search_location)
+                    logger.info(f"Successfully geocoded '{location}' to {lat:.4f}, {lon:.4f} ({display_name})")
+                    
+                    return coords
+                else:
+                    logger.warning(f"Coordinates {lat}, {lon} for '{location}' are outside US bounds")
+                    
+            else:
+                logger.warning(f"No geocoding results found for '{location}'")
+                
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Geocoding API request failed for '{location}': {e}")
+        except (ValueError, KeyError) as e:
+            logger.error(f"Error parsing geocoding response for '{location}': {e}")
+        except Exception as e:
+            logger.error(f"Unexpected error during geocoding for '{location}': {e}")
+        
+        # If geocoding fails, try fallback search without country constraint
+        try:
+            logger.info(f"Trying fallback geocoding for '{location}' without country constraint")
+            params = {
+                'q': location_clean,
+                'format': 'json',
+                'limit': 1,
+                'addressdetails': 1
+            }
+            
+            response = self.session.get(
+                self.geocoding_url, 
+                params=params, 
+                timeout=10,
+                headers={'User-Agent': 'WeatherAppPro/1.0 ([email protected])'}
+            )
+            response.raise_for_status()
+            time.sleep(1)
+            
+            data = response.json()
+            if data and len(data) > 0:
+                result = data[0]
+                lat = float(result['lat'])
+                lon = float(result['lon'])
+                
+                # Check if it's at least in North America for weather.gov compatibility
+                if -170 <= lon <= -50 and 15 <= lat <= 75:
+                    coords = (lat, lon)
+                    self.geocoding_cache[location_lower] = coords
+                    
+                    display_name = result.get('display_name', location_clean)
+                    logger.info(f"Fallback geocoded '{location}' to {lat:.4f}, {lon:.4f} ({display_name})")
+                    
+                    return coords
+                    
+        except Exception as e:
+            logger.error(f"Fallback geocoding also failed for '{location}': {e}")
+        
+        # Final fallback: check our local coordinate database
+        if location_lower in self.fallback_coordinates:
+            coords = self.fallback_coordinates[location_lower]
+            self.geocoding_cache[location_lower] = coords
+            logger.info(f"Using fallback coordinates for '{location}': {coords[0]:.4f}, {coords[1]:.4f}")
+            return coords
+        
+        # Try partial matching in fallback database for more flexible city name matching
+        for city_key, coords in self.fallback_coordinates.items():
+            if city_key in location_lower or location_lower in city_key:
+                self.geocoding_cache[location_lower] = coords
+                logger.info(f"Using partial match fallback coordinates for '{location}' (matched '{city_key}'): {coords[0]:.4f}, {coords[1]:.4f}")
+                return coords
+        
+        logger.error(f"Unable to geocode location: '{location}' - not found in external APIs or fallback database")
+        return None
+    
+    def _is_valid_us_coordinates(self, lat: float, lon: float) -> bool:
+        """Check if coordinates are within reasonable US bounds"""
+        # Continental US, Alaska, Hawaii, and territories
+        return (
+            (25 <= lat <= 49 and -125 <= lon <= -66) or  # Continental US
+            (54 <= lat <= 71 and -179 <= lon <= -130) or  # Alaska
+            (18 <= lat <= 23 and -161 <= lon <= -154) or  # Hawaii
+            (17 <= lat <= 19 and -68 <= lon <= -64) or    # Puerto Rico
+            (14 <= lat <= 15 and -65 <= lon <= -64)       # US Virgin Islands
+        )
+    
+    def get_point_info(self, lat: float, lon: float) -> Dict:
+        """Get point information from weather.gov"""
+        try:
+            url = f"{self.base_url}/points/{lat},{lon}"
+            response = self.session.get(url, timeout=10)
+            response.raise_for_status()
+            data = response.json()
+            props = data.get('properties', {})
+            return {
+                'office': props.get('gridId'),
+                'grid_x': props.get('gridX'),
+                'grid_y': props.get('gridY'),
+                'forecast_url': props.get('forecast'),
+                'forecast_hourly_url': props.get('forecastHourly'),
+                'timezone': props.get('timeZone'),
+                'radar_station': props.get('radarStation'),
+                'fire_weather_zone': props.get('fireWeatherZone'),
+                'county': props.get('county')
+            }
+        except Exception as e:
+            logger.error(f"Error getting point info: {e}")
+            return {}
+    
+    def get_forecast(self, lat: float, lon: float) -> List[Dict]:
+        """Get weather forecast"""
+        try:
+            point_info = self.get_point_info(lat, lon)
+            if not point_info.get('office'):
+                return []
+            
+            office = point_info['office']
+            grid_x = point_info['grid_x']
+            grid_y = point_info['grid_y']
+            
+            url = f"{self.base_url}/gridpoints/{office}/{grid_x},{grid_y}/forecast"
+            response = self.session.get(url, timeout=10)
+            response.raise_for_status()
+            
+            data = response.json()
+            periods = data.get('properties', {}).get('periods', [])
+            
+            forecast = []
+            for period in periods:
+                forecast.append({
+                    'name': period.get('name'),
+                    'temperature': period.get('temperature'),
+                    'temperatureUnit': period.get('temperatureUnit'),
+                    'windSpeed': period.get('windSpeed'),
+                    'windDirection': period.get('windDirection'),
+                    'shortForecast': period.get('shortForecast'),
+                    'detailedForecast': period.get('detailedForecast'),
+                    'precipitationProbability': period.get('probabilityOfPrecipitation', {}).get('value', 0),
+                    'startTime': period.get('startTime'),
+                    'endTime': period.get('endTime'),
+                    'isDaytime': period.get('isDaytime'),
+                    'temperatureTrend': period.get('temperatureTrend')
+                })
+            
+            return forecast
+        except Exception as e:
+            logger.error(f"Error getting forecast: {e}")
+            return []
+    
+    def get_hourly_forecast(self, lat: float, lon: float, hours: int = 48) -> List[Dict]:
+        """Get hourly forecast"""
+        try:
+            point_info = self.get_point_info(lat, lon)
+            if not point_info.get('office'):
+                return []
+            
+            office = point_info['office']
+            grid_x = point_info['grid_x']
+            grid_y = point_info['grid_y']
+            
+            url = f"{self.base_url}/gridpoints/{office}/{grid_x},{grid_y}/forecast/hourly"
+            response = self.session.get(url, timeout=10)
+            response.raise_for_status()
+            
+            data = response.json()
+            periods = data.get('properties', {}).get('periods', [])
+            
+            return periods[:hours]
+        except Exception as e:
+            logger.error(f"Error getting hourly forecast: {e}")
+            return []
+    
+    def get_current_observations(self, lat: float, lon: float) -> Dict:
+        """Get current weather observations"""
+        try:
+            # Find nearest observation station
+            stations_url = f"{self.base_url}/points/{lat},{lon}/stations"
+            response = self.session.get(stations_url, timeout=10)
+            response.raise_for_status()
+            
+            stations_data = response.json()
+            stations = stations_data.get('features', [])
+            
+            if not stations:
+                return {}
+            
+            # Get observations from first station
+            station_id = stations[0]['properties']['stationIdentifier']
+            obs_url = f"{self.base_url}/stations/{station_id}/observations/latest"
+            
+            response = self.session.get(obs_url, timeout=10)
+            response.raise_for_status()
+            
+            obs_data = response.json()
+            props = obs_data.get('properties', {})
+            
+            return {
+                'timestamp': props.get('timestamp'),
+                'temperature': props.get('temperature', {}).get('value'),
+                'dewpoint': props.get('dewpoint', {}).get('value'),
+                'windDirection': props.get('windDirection', {}).get('value'),
+                'windSpeed': props.get('windSpeed', {}).get('value'),
+                'windGust': props.get('windGust', {}).get('value'),
+                'barometricPressure': props.get('barometricPressure', {}).get('value'),
+                'visibility': props.get('visibility', {}).get('value'),
+                'relativeHumidity': props.get('relativeHumidity', {}).get('value'),
+                'heatIndex': props.get('heatIndex', {}).get('value'),
+                'windChill': props.get('windChill', {}).get('value')
+            }
+        except Exception as e:
+            logger.error(f"Error getting current observations: {e}")
+            return {}
+    
+    def get_alerts(self, lat: float = None, lon: float = None) -> List[Dict]:
+        """Get weather alerts"""
+        try:
+            if lat and lon:
+                url = f"{self.base_url}/alerts/active?point={lat},{lon}"
+            else:
+                url = f"{self.base_url}/alerts/active"
+            
+            response = self.session.get(url, timeout=10)
+            response.raise_for_status()
+            
+            data = response.json()
+            alerts = []
+            
+            for feature in data.get('features', [])[:15]:
+                props = feature.get('properties', {})
+                alerts.append({
+                    'id': props.get('id'),
+                    'event': props.get('event'),
+                    'headline': props.get('headline'),
+                    'description': props.get('description'),
+                    'severity': props.get('severity'),
+                    'urgency': props.get('urgency'),
+                    'certainty': props.get('certainty'),
+                    'areas': props.get('areaDesc'),
+                    'effective': props.get('effective'),
+                    'expires': props.get('expires'),
+                    'senderName': props.get('senderName'),
+                    'category': props.get('category')
+                })
+            
+            return alerts
+        except Exception as e:
+            logger.error(f"Error getting alerts: {e}")
+            return []
+    
+    def get_radar_data(self, lat: float, lon: float) -> Dict:
+        """Get radar station information"""
+        try:
+            point_info = self.get_point_info(lat, lon)
+            radar_station = point_info.get('radar_station')
+            
+            if not radar_station:
+                return {}
+            
+            return {
+                'station': radar_station,
+                'base_url': f"https://radar.weather.gov/ridge/lite/{radar_station}_loop.gif"
+            }
+        except Exception as e:
+            logger.error(f"Error getting radar data: {e}")
+            return {}
+
+def create_weather_client() -> WeatherClient:
+    """Factory function to create weather client"""
+    return WeatherClient()
+

src/chatbot/climate_expert_nlp.py

@@ -0,0 +1,608 @@
+"""
+Enhanced Climate Expert NLP Processor
+Advanced natural language processing for climate expertise and intelligent weather analysis
+"""
+
+import re
+import logging
+from typing import List, Dict, Set, Tuple, Optional
+from datetime import datetime, date
+import calendar
+
+logger = logging.getLogger(__name__)
+
+class ClimateExpertNLP:
+    """Advanced NLP processor with climate expertise and meteorological intelligence"""
+    
+    def __init__(self):
+        # Enhanced weather patterns with meteorological expertise
+        self.climate_keywords = {
+            'temperature_analysis': {
+                'primary': ['temperature', 'temp', 'thermal', 'heat', 'warmth', 'cold', 'chill', 'frost', 'freeze'],
+                'patterns': ['heat wave', 'cold snap', 'thermal gradient', 'temperature inversion', 'diurnal range'],
+                'expert': ['heat index', 'wind chill', 'feels like', 'apparent temperature', 'dewpoint'],
+                'anomalies': ['record', 'unusual', 'extreme', 'abnormal', 'unprecedented', 'rare']
+            },
+            'precipitation_analysis': {
+                'primary': ['rain', 'precipitation', 'moisture', 'wet', 'dry', 'storm', 'shower', 'drizzle'],
+                'patterns': ['drought', 'flooding', 'monsoon', 'rainy season', 'dry spell', 'accumulation'],
+                'expert': ['precipitation rate', 'intensity', 'convective', 'stratiform', 'orographic'],
+                'types': ['thunderstorm', 'snow', 'sleet', 'hail', 'freezing rain', 'virga']
+            },
+            'atmospheric_dynamics': {
+                'primary': ['pressure', 'atmospheric', 'barometric', 'high pressure', 'low pressure'],
+                'patterns': ['front', 'cold front', 'warm front', 'occluded front', 'cyclone', 'anticyclone'],
+                'expert': ['isobar', 'gradient', 'ridge', 'trough', 'convergence', 'divergence'],
+                'phenomena': ['inversion', 'subsidence', 'convection', 'advection']
+            },
+            'wind_analysis': {
+                'primary': ['wind', 'breeze', 'gust', 'air movement', 'circulation'],
+                'patterns': ['jet stream', 'trade winds', 'westerlies', 'polar easterlies', 'monsoon'],
+                'expert': ['wind shear', 'turbulence', 'downdraft', 'updraft', 'vorticity'],
+                'local': ['sea breeze', 'land breeze', 'mountain breeze', 'valley breeze', 'chinook']
+            },
+            'climate_patterns': {
+                'primary': ['climate', 'pattern', 'cycle', 'oscillation', 'trend', 'variability'],
+                'global': ['el nino', 'la nina', 'enso', 'pdo', 'amo', 'nao', 'ao'],
+                'regional': ['monsoon', 'mediterranean', 'continental', 'maritime', 'subtropical'],
+                'temporal': ['seasonal', 'annual', 'decadal', 'interannual', 'long-term']
+            },
+            'extreme_weather': {
+                'primary': ['extreme', 'severe', 'dangerous', 'hazardous', 'warning', 'watch'],
+                'types': ['tornado', 'hurricane', 'typhoon', 'blizzard', 'derecho', 'microburst'],
+                'conditions': ['severe thunderstorm', 'flash flood', 'ice storm', 'heat dome'],
+                'impacts': ['damage', 'destruction', 'flooding', 'power outage', 'travel disruption']
+            },
+            'seasonal_intelligence': {
+                'primary': ['season', 'seasonal', 'spring', 'summer', 'fall', 'autumn', 'winter'],
+                'transitions': ['equinox', 'solstice', 'onset', 'retreat', 'shift'],
+                'phenomena': ['indian summer', 'polar vortex', 'heat dome', 'arctic blast'],
+                'agriculture': ['growing season', 'frost date', 'planting', 'harvest']
+            },
+            'climate_change': {
+                'primary': ['climate change', 'warming', 'trend', 'shift', 'change'],
+                'indicators': ['rising temperatures', 'sea level', 'ice melt', 'precipitation change'],
+                'impacts': ['drought frequency', 'storm intensity', 'heat events', 'cold events'],
+                'adaptation': ['resilience', 'mitigation', 'adaptation', 'sustainability']
+            },
+            'meteorological_analysis': {
+                'primary': ['analysis', 'forecast', 'model', 'prediction', 'simulation'],
+                'models': ['gfs', 'ecmwf', 'nam', 'ensemble', 'numerical model'],
+                'data': ['satellite', 'radar', 'observation', 'sounding', 'mesonet'],
+                'uncertainty': ['confidence', 'uncertainty', 'probability', 'likelihood', 'chance']
+            }
+        }
+        
+        # Expert-level weather relationships and correlations
+        self.weather_relationships = {
+            'temperature_pressure': {
+                'high_pressure': 'generally associated with clear skies and stable temperatures',
+                'low_pressure': 'often brings clouds, precipitation, and temperature changes',
+                'pressure_gradient': 'strong gradients indicate windy conditions'
+            },
+            'moisture_temperature': {
+                'dewpoint_spread': 'narrow spread indicates high humidity or fog potential',
+                'relative_humidity': 'temperature-dependent moisture content',
+                'heat_index': 'combines temperature and humidity for apparent temperature'
+            },
+            'wind_patterns': {
+                'thermal_circulation': 'temperature differences drive local wind patterns',
+                'pressure_gradient': 'wind flows from high to low pressure areas',
+                'coriolis_effect': 'Earth\'s rotation affects wind direction'
+            }
+        }
+        
+        # Regional climate knowledge
+        self.regional_climate_patterns = {
+            'pacific_northwest': {
+                'characteristics': ['marine west coast', 'wet winters', 'dry summers', 'moderate temperatures'],
+                'phenomena': ['atmospheric rivers', 'rain shadow', 'marine layer'],
+                'seasons': ['wet season (oct-apr)', 'dry season (may-sep)']
+            },
+            'southwest_desert': {
+                'characteristics': ['arid', 'hot summers', 'mild winters', 'low humidity'],
+                'phenomena': ['monsoon season', 'heat island effect', 'dust storms'],
+                'extremes': ['extreme heat', 'flash flooding', 'drought']
+            },
+            'great_plains': {
+                'characteristics': ['continental', 'temperature extremes', 'variable precipitation'],
+                'phenomena': ['severe thunderstorms', 'tornadoes', 'chinook winds'],
+                'seasons': ['severe weather season (spring-summer)']
+            },
+            'southeast': {
+                'characteristics': ['humid subtropical', 'hot summers', 'mild winters'],
+                'phenomena': ['hurricanes', 'afternoon thunderstorms', 'heat index'],
+                'seasons': ['hurricane season (jun-nov)']
+            },
+            'northeast': {
+                'characteristics': ['four-season continental', 'nor\'easters', 'lake effect'],
+                'phenomena': ['coastal storms', 'lake effect snow', 'heat waves'],
+                'seasons': ['distinct four seasons', 'winter storms']
+            }
+        }
+        
+        # Enhanced cities database with regional climate context
+        self.cities_with_climate_context = self._build_climate_aware_cities()
+        
+        # Meteorological expertise patterns
+        self.expert_analysis_patterns = {
+            'synoptic_scale': ['large scale', 'continental', 'hemispheric', 'global pattern'],
+            'mesoscale': ['regional', 'local', 'thunderstorm scale', 'sea breeze'],
+            'microscale': ['turbulence', 'surface layer', 'boundary layer'],
+            'forecast_confidence': ['high confidence', 'low confidence', 'uncertain', 'model disagreement'],
+            'climatology': ['normal', 'average', 'typical', 'anomaly', 'departure from normal']
+        }
+    
+    def _build_climate_aware_cities(self) -> Dict[str, Dict]:
+        """Build cities database with climate context"""
+        cities = {
+            # Pacific Northwest
+            'seattle': {'region': 'pacific_northwest', 'climate': 'oceanic', 'elevation': 56},
+            'portland': {'region': 'pacific_northwest', 'climate': 'oceanic', 'elevation': 50},
+            'spokane': {'region': 'pacific_northwest', 'climate': 'continental', 'elevation': 1843},
+            
+            # Southwest Desert
+            'phoenix': {'region': 'southwest_desert', 'climate': 'desert', 'elevation': 1086},
+            'tucson': {'region': 'southwest_desert', 'climate': 'desert', 'elevation': 2389},
+            'las vegas': {'region': 'southwest_desert', 'climate': 'desert', 'elevation': 2001},
+            'albuquerque': {'region': 'southwest_desert', 'climate': 'high_desert', 'elevation': 5312},
+            
+            # Great Plains
+            'kansas city': {'region': 'great_plains', 'climate': 'continental', 'elevation': 910},
+            'omaha': {'region': 'great_plains', 'climate': 'continental', 'elevation': 1090},
+            'oklahoma city': {'region': 'great_plains', 'climate': 'continental', 'elevation': 1201},
+            'denver': {'region': 'great_plains', 'climate': 'semi_arid', 'elevation': 5280},
+            
+            # Southeast
+            'miami': {'region': 'southeast', 'climate': 'tropical', 'elevation': 6},
+            'atlanta': {'region': 'southeast', 'climate': 'humid_subtropical', 'elevation': 1050},
+            'new orleans': {'region': 'southeast', 'climate': 'humid_subtropical', 'elevation': -6},
+            'charlotte': {'region': 'southeast', 'climate': 'humid_subtropical', 'elevation': 751},
+            
+            # Northeast
+            'new york': {'region': 'northeast', 'climate': 'humid_continental', 'elevation': 33},
+            'boston': {'region': 'northeast', 'climate': 'humid_continental', 'elevation': 141},
+            'philadelphia': {'region': 'northeast', 'climate': 'humid_subtropical', 'elevation': 39},
+            'buffalo': {'region': 'northeast', 'climate': 'humid_continental', 'elevation': 585},
+            
+            # Additional major cities
+            'chicago': {'region': 'great_lakes', 'climate': 'humid_continental', 'elevation': 594},
+            'detroit': {'region': 'great_lakes', 'climate': 'humid_continental', 'elevation': 574},
+            'minneapolis': {'region': 'upper_midwest', 'climate': 'humid_continental', 'elevation': 830},
+            'milwaukee': {'region': 'great_lakes', 'climate': 'humid_continental', 'elevation': 634},
+            
+            # Mountain West
+            'salt lake city': {'region': 'mountain_west', 'climate': 'semi_arid', 'elevation': 4226},
+            'boise': {'region': 'mountain_west', 'climate': 'semi_arid', 'elevation': 2730},
+            'billings': {'region': 'mountain_west', 'climate': 'semi_arid', 'elevation': 3123},
+            
+            # California - diverse climates
+            'los angeles': {'region': 'california', 'climate': 'mediterranean', 'elevation': 285},
+            'san francisco': {'region': 'california', 'climate': 'mediterranean', 'elevation': 52},
+            'san diego': {'region': 'california', 'climate': 'mediterranean', 'elevation': 62},
+            'sacramento': {'region': 'california', 'climate': 'mediterranean', 'elevation': 30},
+            'fresno': {'region': 'california', 'climate': 'semi_arid', 'elevation': 335},
+            
+            # Texas - varied climates
+            'houston': {'region': 'texas_gulf', 'climate': 'humid_subtropical', 'elevation': 80},
+            'dallas': {'region': 'texas_plains', 'climate': 'humid_subtropical', 'elevation': 430},
+            'san antonio': {'region': 'texas_hill_country', 'climate': 'humid_subtropical', 'elevation': 650},
+            'austin': {'region': 'texas_hill_country', 'climate': 'humid_subtropical', 'elevation': 489},
+            'el paso': {'region': 'texas_desert', 'climate': 'desert', 'elevation': 3740},
+            
+            # Florida
+            'orlando': {'region': 'florida_central', 'climate': 'humid_subtropical', 'elevation': 82},
+            'tampa': {'region': 'florida_gulf', 'climate': 'humid_subtropical', 'elevation': 48},
+            'jacksonville': {'region': 'florida_northeast', 'climate': 'humid_subtropical', 'elevation': 16}
+        }
+        
+        return cities
+    
+    def extract_climate_intelligence(self, text: str) -> Dict:
+        """Extract advanced climate and meteorological intelligence from query"""
+        text_lower = text.lower()
+        
+        intelligence = {
+            'expertise_level': self._assess_expertise_level(text),
+            'climate_phenomena': self._detect_climate_phenomena(text),
+            'meteorological_concepts': self._detect_meteorological_concepts(text),
+            'seasonal_context': self._extract_seasonal_intelligence(text),
+            'regional_patterns': self._detect_regional_patterns(text),
+            'expert_analysis_needed': self._requires_expert_analysis(text),
+            'climate_relationships': self._identify_climate_relationships(text),
+            'forecast_complexity': self._assess_forecast_complexity(text)
+        }
+        
+        return intelligence
+    
+    def _assess_expertise_level(self, text: str) -> str:
+        """Assess the expertise level required for the query"""
+        text_lower = text.lower()
+        
+        expert_indicators = [
+            'meteorological', 'atmospheric', 'synoptic', 'mesoscale', 'climatology',
+            'pressure gradient', 'thermal gradient', 'convective', 'advection',
+            'el nino', 'la nina', 'jet stream', 'vorticity', 'geostrophic'
+        ]
+        
+        intermediate_indicators = [
+            'heat index', 'dewpoint', 'wind chill', 'pressure system', 'front',
+            'high pressure', 'low pressure', 'atmospheric river', 'heat dome'
+        ]
+        
+        expert_count = sum(1 for term in expert_indicators if term in text_lower)
+        intermediate_count = sum(1 for term in intermediate_indicators if term in text_lower)
+        
+        if expert_count > 0:
+            return 'expert'
+        elif intermediate_count > 0:
+            return 'intermediate'
+        else:
+            return 'basic'
+    
+    def _detect_climate_phenomena(self, text: str) -> List[str]:
+        """Detect mention of specific climate phenomena"""
+        text_lower = text.lower()
+        
+        phenomena = {
+            'el_nino': ['el nino', 'el niño', 'enso warm phase'],
+            'la_nina': ['la nina', 'la niña', 'enso cold phase'],
+            'heat_dome': ['heat dome', 'high pressure ridge', 'persistent high'],
+            'polar_vortex': ['polar vortex', 'arctic blast', 'polar air mass'],
+            'atmospheric_river': ['atmospheric river', 'pineapple express', 'moisture plume'],
+            'monsoon': ['monsoon', 'monsoonal', 'monsoon season'],
+            'drought': ['drought', 'dry conditions', 'precipitation deficit'],
+            'heat_wave': ['heat wave', 'extreme heat', 'prolonged heat'],
+            'cold_snap': ['cold snap', 'arctic outbreak', 'extreme cold']
+        }
+        
+        detected = []
+        for phenomenon, keywords in phenomena.items():
+            if any(keyword in text_lower for keyword in keywords):
+                detected.append(phenomenon)
+        
+        return detected
+    
+    def _detect_meteorological_concepts(self, text: str) -> List[str]:
+        """Detect meteorological concepts and terminology"""
+        text_lower = text.lower()
+        
+        concepts = {
+            'pressure_systems': ['high pressure', 'low pressure', 'pressure system', 'anticyclone', 'cyclone'],
+            'frontal_systems': ['cold front', 'warm front', 'occluded front', 'stationary front'],
+            'atmospheric_layers': ['troposphere', 'stratosphere', 'boundary layer', 'inversion layer'],
+            'circulation_patterns': ['jet stream', 'trade winds', 'westerlies', 'polar easterlies'],
+            'thermodynamics': ['adiabatic', 'latent heat', 'sensible heat', 'convection', 'radiation'],
+            'moisture_processes': ['evaporation', 'condensation', 'precipitation', 'sublimation'],
+            'wind_phenomena': ['wind shear', 'turbulence', 'downdraft', 'updraft', 'convergence']
+        }
+        
+        detected = []
+        for concept_group, keywords in concepts.items():
+            if any(keyword in text_lower for keyword in keywords):
+                detected.append(concept_group)
+        
+        return detected
+    
+    def _extract_seasonal_intelligence(self, text: str) -> Dict:
+        """Extract sophisticated seasonal weather intelligence"""
+        text_lower = text.lower()
+        current_date = datetime.now()
+        
+        # Determine current season and seasonal transitions
+        month = current_date.month
+        if month in [12, 1, 2]:
+            current_season = 'winter'
+            transition_info = self._get_winter_transition_info(month)
+        elif month in [3, 4, 5]:
+            current_season = 'spring'
+            transition_info = self._get_spring_transition_info(month)
+        elif month in [6, 7, 8]:
+            current_season = 'summer'
+            transition_info = self._get_summer_transition_info(month)
+        else:
+            current_season = 'fall'
+            transition_info = self._get_fall_transition_info(month)
+        
+        # Detect seasonal phenomena mentions
+        seasonal_phenomena = {
+            'winter': ['polar vortex', 'blizzard', 'ice storm', 'lake effect', 'arctic blast'],
+            'spring': ['severe weather', 'tornado season', 'flooding', 'rapid warming'],
+            'summer': ['heat wave', 'monsoon', 'hurricane season', 'heat dome', 'drought'],
+            'fall': ['hurricane season', 'leaf change', 'first frost', 'indian summer']
+        }
+        
+        detected_phenomena = []
+        for season, phenomena in seasonal_phenomena.items():
+            for phenomenon in phenomena:
+                if phenomenon in text_lower:
+                    detected_phenomena.append((season, phenomenon))
+        
+        return {
+            'current_season': current_season,
+            'transition_info': transition_info,
+            'seasonal_phenomena': detected_phenomena,
+            'climatological_context': self._get_climatological_context(current_season, month)
+        }
+    
+    def _get_winter_transition_info(self, month: int) -> Dict:
+        """Get winter season transition information"""
+        if month == 12:
+            return {'phase': 'early_winter', 'characteristics': ['winter solstice approaching', 'temperature decline']}
+        elif month == 1:
+            return {'phase': 'mid_winter', 'characteristics': ['coldest period', 'arctic air masses']}
+        else:  # February
+            return {'phase': 'late_winter', 'characteristics': ['spring transition beginning', 'daylight increasing']}
+    
+    def _get_spring_transition_info(self, month: int) -> Dict:
+        """Get spring season transition information"""
+        if month == 3:
+            return {'phase': 'early_spring', 'characteristics': ['spring equinox', 'warming trend']}
+        elif month == 4:
+            return {'phase': 'mid_spring', 'characteristics': ['severe weather season', 'rapid warming']}
+        else:  # May
+            return {'phase': 'late_spring', 'characteristics': ['summer transition', 'thunderstorm activity']}
+    
+    def _get_summer_transition_info(self, month: int) -> Dict:
+        """Get summer season transition information"""
+        if month == 6:
+            return {'phase': 'early_summer', 'characteristics': ['summer solstice', 'heat building']}
+        elif month == 7:
+            return {'phase': 'mid_summer', 'characteristics': ['peak heat', 'monsoon activity']}
+        else:  # August
+            return {'phase': 'late_summer', 'characteristics': ['hurricane peak', 'heat dome potential']}
+    
+    def _get_fall_transition_info(self, month: int) -> Dict:
+        """Get fall season transition information"""
+        if month == 9:
+            return {'phase': 'early_fall', 'characteristics': ['autumn equinox', 'cooling trend']}
+        elif month == 10:
+            return {'phase': 'mid_fall', 'characteristics': ['temperature drop', 'first frost potential']}
+        else:  # November
+            return {'phase': 'late_fall', 'characteristics': ['winter transition', 'storm systems']}
+    
+    def _get_climatological_context(self, season: str, month: int) -> Dict:
+        """Get climatological context for current conditions"""
+        climatology = {
+            'winter': {
+                'temperature_patterns': 'coldest period with arctic air masses',
+                'precipitation_patterns': 'snow in northern regions, rain in south',
+                'storm_systems': 'nor\'easters, alberta clippers, arctic fronts'
+            },
+            'spring': {
+                'temperature_patterns': 'rapid warming with daily temperature swings',
+                'precipitation_patterns': 'increased thunderstorm activity',
+                'storm_systems': 'severe thunderstorms, tornadoes, flooding'
+            },
+            'summer': {
+                'temperature_patterns': 'peak heat with heat dome potential',
+                'precipitation_patterns': 'monsoons, afternoon thunderstorms',
+                'storm_systems': 'hurricanes, derechos, heat waves'
+            },
+            'fall': {
+                'temperature_patterns': 'cooling trend with first frost',
+                'precipitation_patterns': 'transitional weather patterns',
+                'storm_systems': 'late hurricanes, early winter storms'
+            }
+        }
+        
+        return climatology.get(season, {})
+    
+    def _detect_regional_patterns(self, text: str) -> List[str]:
+        """Detect regional climate pattern references"""
+        text_lower = text.lower()
+        
+        regional_patterns = [
+            'marine layer', 'lake effect', 'orographic lift', 'rain shadow',
+            'urban heat island', 'sea breeze', 'land breeze', 'valley breeze',
+            'mountain breeze', 'chinook winds', 'santa ana winds',
+            'gulf stream', 'jet stream', 'bermuda high', 'pacific high'
+        ]
+        
+        detected = []
+        for pattern in regional_patterns:
+            if pattern in text_lower:
+                detected.append(pattern)
+        
+        return detected
+    
+    def _requires_expert_analysis(self, text: str) -> bool:
+        """Determine if query requires expert meteorological analysis"""
+        text_lower = text.lower()
+        
+        expert_indicators = [
+            'why', 'how', 'explain', 'analysis', 'pattern', 'trend', 'anomaly',
+            'unusual', 'abnormal', 'record', 'extreme', 'unprecedented',
+            'climate change', 'long term', 'statistical', 'probability'
+        ]
+        
+        return any(indicator in text_lower for indicator in expert_indicators)
+    
+    def _identify_climate_relationships(self, text: str) -> List[str]:
+        """Identify climate variable relationships mentioned"""
+        text_lower = text.lower()
+        
+        relationships = {
+            'temperature_humidity': ['heat index', 'feels like', 'apparent temperature'],
+            'temperature_wind': ['wind chill', 'cooling effect'],
+            'pressure_weather': ['high pressure clear', 'low pressure storms'],
+            'elevation_temperature': ['altitude effect', 'elevation cooling'],
+            'ocean_climate': ['sea surface temperature', 'coastal climate'],
+            'latitude_climate': ['polar', 'tropical', 'temperate zones']
+        }
+        
+        detected = []
+        for relationship, keywords in relationships.items():
+            if any(keyword in text_lower for keyword in keywords):
+                detected.append(relationship)
+        
+        return detected
+    
+    def _assess_forecast_complexity(self, text: str) -> str:
+        """Assess the complexity level needed for forecast response"""
+        text_lower = text.lower()
+        
+        # Complex forecast indicators
+        complex_indicators = [
+            'detailed', 'hourly', 'hour by hour', 'breakdown', 'analysis',
+            'trend', 'pattern', 'model', 'ensemble', 'uncertainty'
+        ]
+        
+        # Extended forecast indicators
+        extended_indicators = [
+            'week', 'month', 'season', 'long term', 'extended', 'outlook'
+        ]
+        
+        if any(indicator in text_lower for indicator in complex_indicators):
+            return 'complex'
+        elif any(indicator in text_lower for indicator in extended_indicators):
+            return 'extended'
+        else:
+            return 'standard'
+    
+    def enhance_query_processing(self, basic_analysis: Dict, text: str) -> Dict:
+        """Enhance basic query processing with climate expertise"""
+        climate_intelligence = self.extract_climate_intelligence(text)
+        
+        # Add climate expertise to basic analysis
+        enhanced_analysis = basic_analysis.copy()
+        enhanced_analysis.update({
+            'climate_intelligence': climate_intelligence,
+            'expert_context': self._build_expert_context(basic_analysis, climate_intelligence),
+            'response_sophistication': self._determine_response_sophistication(climate_intelligence),
+            'regional_climate_context': self._add_regional_context(basic_analysis.get('cities', [])),
+            'meteorological_insights': self._generate_meteorological_insights(basic_analysis, climate_intelligence)
+        })
+        
+        return enhanced_analysis
+    
+    def _build_expert_context(self, basic_analysis: Dict, climate_intelligence: Dict) -> Dict:
+        """Build expert context for response generation"""
+        cities = basic_analysis.get('cities', [])
+        query_type = basic_analysis.get('query_type', 'general')
+        
+        expert_context = {
+            'requires_technical_explanation': climate_intelligence.get('expertise_level') in ['intermediate', 'expert'],
+            'include_meteorological_background': climate_intelligence.get('expert_analysis_needed', False),
+            'seasonal_considerations': climate_intelligence.get('seasonal_context', {}),
+            'regional_climate_factors': self._get_regional_factors(cities),
+            'phenomenon_explanations': climate_intelligence.get('climate_phenomena', []),
+            'forecast_confidence_level': self._assess_forecast_confidence(basic_analysis, climate_intelligence)
+        }
+        
+        return expert_context
+    
+    def _determine_response_sophistication(self, climate_intelligence: Dict) -> str:
+        """Determine the sophistication level needed for response"""
+        expertise_level = climate_intelligence.get('expertise_level', 'basic')
+        phenomena_count = len(climate_intelligence.get('climate_phenomena', []))
+        concepts_count = len(climate_intelligence.get('meteorological_concepts', []))
+        
+        if expertise_level == 'expert' or phenomena_count > 2 or concepts_count > 2:
+            return 'expert'
+        elif expertise_level == 'intermediate' or phenomena_count > 0 or concepts_count > 0:
+            return 'intermediate'
+        else:
+            return 'basic'
+    
+    def _add_regional_context(self, cities: List[str]) -> Dict:
+        """Add regional climate context for cities"""
+        regional_context = {}
+        
+        for city in cities:
+            city_lower = city.lower()
+            if city_lower in self.cities_with_climate_context:
+                city_info = self.cities_with_climate_context[city_lower]
+                regional_context[city] = {
+                    'climate_type': city_info.get('climate', 'unknown'),
+                    'regional_patterns': self.regional_climate_patterns.get(city_info.get('region', ''), {}),
+                    'elevation_effects': self._get_elevation_effects(city_info.get('elevation', 0)),
+                    'seasonal_characteristics': self._get_city_seasonal_characteristics(city_info)
+                }
+        
+        return regional_context
+    
+    def _get_elevation_effects(self, elevation: int) -> Dict:
+        """Get elevation effects on weather"""
+        if elevation > 5000:
+            return {
+                'temperature_effect': 'significantly cooler due to elevation',
+                'precipitation_effect': 'orographic enhancement possible',
+                'pressure_effect': 'lower atmospheric pressure'
+            }
+        elif elevation > 2000:
+            return {
+                'temperature_effect': 'moderately cooler due to elevation',
+                'precipitation_effect': 'some orographic effects',
+                'pressure_effect': 'slightly lower pressure'
+            }
+        else:
+            return {
+                'temperature_effect': 'minimal elevation effects',
+                'precipitation_effect': 'standard precipitation patterns',
+                'pressure_effect': 'near sea level pressure'
+            }
+    
+    def _get_city_seasonal_characteristics(self, city_info: Dict) -> Dict:
+        """Get seasonal characteristics for a city"""
+        region = city_info.get('region', '')
+        climate = city_info.get('climate', '')
+        
+        if region in self.regional_climate_patterns:
+            return self.regional_climate_patterns[region]
+        else:
+            return {'characteristics': [climate], 'phenomena': [], 'seasons': []}
+    
+    def _generate_meteorological_insights(self, basic_analysis: Dict, climate_intelligence: Dict) -> List[str]:
+        """Generate meteorological insights for expert responses"""
+        insights = []
+        
+        # Add insights based on query type and climate intelligence
+        query_type = basic_analysis.get('query_type', 'general')
+        phenomena = climate_intelligence.get('climate_phenomena', [])
+        concepts = climate_intelligence.get('meteorological_concepts', [])
+        
+        if 'temperature_analysis' in query_type:
+            insights.append('Consider thermal gradient effects and heat/cold transport mechanisms')
+        
+        if 'el_nino' in phenomena or 'la_nina' in phenomena:
+            insights.append('ENSO phase impacts global weather patterns and regional climate')
+        
+        if 'pressure_systems' in concepts:
+            insights.append('Pressure gradient forces drive wind patterns and weather system movement')
+        
+        if climate_intelligence.get('expertise_level') == 'expert':
+            insights.append('Provide detailed meteorological explanation with atmospheric dynamics')
+        
+        return insights
+    
+    def _get_regional_factors(self, cities: List[str]) -> Dict:
+        """Get regional climate factors for cities"""
+        factors = {}
+        
+        for city in cities:
+            city_lower = city.lower()
+            if city_lower in self.cities_with_climate_context:
+                city_info = self.cities_with_climate_context[city_lower]
+                region = city_info.get('region', '')
+                
+                if region in self.regional_climate_patterns:
+                    factors[city] = self.regional_climate_patterns[region]
+        
+        return factors
+    
+    def _assess_forecast_confidence(self, basic_analysis: Dict, climate_intelligence: Dict) -> str:
+        """Assess forecast confidence based on complexity"""
+        forecast_context = basic_analysis.get('forecast_context', {})
+        complexity = climate_intelligence.get('forecast_complexity', 'standard')
+        
+        if complexity == 'complex' or len(climate_intelligence.get('climate_phenomena', [])) > 1:
+            return 'moderate'  # Complex phenomena reduce confidence
+        elif forecast_context.get('timeline') == 'long_term':
+            return 'low'  # Long-term forecasts have lower confidence
+        else:
+            return 'high'  # Standard forecasts have high confidence
+
+def create_climate_expert_nlp() -> ClimateExpertNLP:
+    """Factory function to create climate expert NLP processor"""
+    return ClimateExpertNLP()

src/chatbot/enhanced_chatbot.py

@@ -0,0 +1,962 @@
+"""
+Enhanced AI-Powered Chatbot with LlamaIndex and Gemini Integration
+Full LLM integration for intelligent weather conversations
+"""
+
+import os
+import logging
+import asyncio
+from typing import Dict, List, Optional, Any
+from datetime import datetime
+
+# Import enhanced climate expert components
+from .climate_expert_nlp import ClimateExpertNLP, create_climate_expert_nlp
+from .weather_knowledge_base import WeatherKnowledgeBase, create_weather_knowledge_base
+
+# LlamaIndex imports
+try:
+    from llama_index.core import VectorStoreIndex, Document, Settings
+    from llama_index.core.memory import ChatMemoryBuffer
+    from llama_index.core.chat_engine import SimpleChatEngine
+    from llama_index.llms.gemini import Gemini
+    from llama_index.embeddings.gemini import GeminiEmbedding
+    LLAMA_INDEX_AVAILABLE = True
+except ImportError:
+    LLAMA_INDEX_AVAILABLE = False
+
+# Google Gemini imports
+try:
+    import google.generativeai as genai
+    GEMINI_AVAILABLE = True
+except ImportError:
+    GEMINI_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+class EnhancedWeatherChatbot:
+    """AI-powered weather chatbot with LlamaIndex and Gemini integration"""
+    
+    def __init__(self, weather_client, nlp_processor, gemini_api_key: str = None):
+        """Initialize the enhanced chatbot with climate expertise"""
+        self.weather_client = weather_client
+        self.nlp_processor = nlp_processor
+        self.gemini_api_key = gemini_api_key or os.getenv("GEMINI_API_KEY")
+        
+        # Initialize climate expert components
+        self.climate_expert_nlp = create_climate_expert_nlp()
+        self.weather_knowledge_base = create_weather_knowledge_base()
+        
+        # Initialize LlamaIndex components
+        self.llm = None
+        self.chat_engine = None
+        self.memory = ChatMemoryBuffer.from_defaults(token_limit=3000)
+        self.weather_knowledge_base_docs = []
+        
+        # Enhanced weather context for AI with climate expertise
+        self.weather_context = """
+        You are an expert climate scientist and meteorologist with comprehensive knowledge of weather patterns, 
+        atmospheric dynamics, and climate science. You have access to real-time weather data from the National Weather Service 
+        and extensive meteorological expertise.
+        
+        Your capabilities include:
+        - Advanced weather analysis and interpretation of atmospheric phenomena
+        - Expert-level explanations of meteorological processes and climate patterns
+        - Regional climate expertise and seasonal pattern recognition
+        - Climate change impacts and long-term trend analysis
+        - Severe weather forecasting and risk assessment
+        - Agricultural and seasonal weather impacts
+        - Historical weather pattern analysis and climatological context
+        - ENSO (El Niño/La Niña) and other climate oscillation impacts
+        - Activity-specific weather advice with safety considerations
+        
+        When responding to weather queries:
+        1. Provide accurate, real-time weather information
+        2. Include relevant meteorological explanations and context
+        3. Explain atmospheric processes and weather phenomena
+        4. Consider regional climate patterns and seasonal effects
+        5. Relate current conditions to historical norms and climate patterns
+        6. Provide expert insights on weather relationships and causes
+        7. Include seasonal and climatological context when relevant
+        8. Assess and communicate weather-related risks and safety considerations
+        9. For complex queries, provide detailed meteorological analysis
+        10. Connect local weather to larger-scale atmospheric patterns
+        
+        Expertise Areas:
+        - Synoptic and mesoscale meteorology
+        - Climate patterns and oscillations (ENSO, PDO, NAO, etc.)
+        - Extreme weather events and their formation
+        - Regional climatology and seasonal patterns
+        - Weather forecasting uncertainty and model interpretation
+        - Climate change impacts on weather patterns
+        - Agricultural meteorology and seasonal planning
+        - Weather safety and preparedness
+        
+        Always maintain scientific accuracy while making complex concepts accessible to users.
+        """
+        
+        # Setup AI components
+        self._setup_ai_components()
+    
+    def _setup_ai_components(self):
+        """Setup LlamaIndex and Gemini components"""
+        try:
+            if not self.gemini_api_key:
+                logger.warning("No Gemini API key provided. Using basic mode.")
+                return
+            
+            if GEMINI_AVAILABLE:
+                # Configure Gemini
+                genai.configure(api_key=self.gemini_api_key)
+                
+            if LLAMA_INDEX_AVAILABLE and self.gemini_api_key:
+                # Setup LlamaIndex with Gemini 2.0 Flash
+                self.llm = Gemini(
+                    api_key=self.gemini_api_key,
+                    model="models/gemini-2.0-flash-exp",
+                    temperature=0.7,
+                    max_tokens=1000
+                )
+                
+                # Configure global settings
+                Settings.llm = self.llm
+                Settings.embed_model = GeminiEmbedding(
+                    api_key=self.gemini_api_key,
+                    model_name="models/embedding-001"
+                )
+                
+                # Create weather knowledge base
+                self._build_weather_knowledge_base()
+                
+                logger.info("LlamaIndex with Gemini configured successfully")
+            else:
+                logger.warning("LlamaIndex not available. Using direct Gemini API.")
+                
+        except Exception as e:
+            logger.error(f"Error setting up AI components: {e}")
+    
+    def _build_weather_knowledge_base(self):
+        """Build comprehensive weather knowledge base for LlamaIndex"""
+        try:
+            weather_docs = [
+                Document(text=self.weather_context),
+                Document(text="""
+                Advanced Weather Forecast Interpretation:
+                - Temperature: Measured in Fahrenheit for US locations, consider heat index and wind chill
+                - Precipitation Probability: Percentage chance of measurable precipitation (≥0.01 inches)
+                - Wind Speed: Measured in mph, includes sustained winds and gusts
+                - Wind Direction: From direction (e.g., SW means wind coming from southwest)
+                - Conditions: Detailed forecast including cloud cover, precipitation type, visibility
+                - Pressure: Atmospheric pressure in inches of mercury, indicates weather system movement
+                
+                Meteorological Analysis Guidelines:
+                - Temperature differences of 5°F+ are noticeable, 15°F+ are significant
+                - Precipitation probability >30% indicates likely precipitation
+                - Wind speeds >15 mph are noticeable, >25 mph can affect activities
+                - Pressure falling indicates approaching weather systems
+                - Always consider seasonal and regional climatological context
+                """),
+                Document(text="""
+                Regional Climate Patterns and Characteristics:
+                
+                Pacific Northwest: Marine west coast climate
+                - Wet winters (Oct-Apr) with frequent Pacific storms
+                - Dry summers (May-Sep) with pleasant temperatures
+                - Orographic precipitation enhancement in mountains
+                - Rain shadow effects east of Cascades
+                - Marine influence moderates temperatures
+                
+                Southwest Desert: Hot arid climate
+                - Extreme heat in summer with low humidity
+                - Mild winters with minimal precipitation
+                - Summer monsoon season (Jul-Sep) brings thunderstorms
+                - Large diurnal temperature ranges
+                - Flash flood risks during monsoon
+                
+                Great Plains: Continental climate
+                - Large seasonal temperature ranges
+                - Severe weather season (Mar-Jun) with tornadoes
+                - Variable precipitation with drought potential
+                - Chinook winds in northern areas
+                - Strong pressure gradients drive weather systems
+                
+                Southeast: Humid subtropical climate
+                - Hot, humid summers with afternoon thunderstorms
+                - Mild winters with occasional cold snaps
+                - Hurricane season (Jun-Nov) peak activity Aug-Oct
+                - High humidity increases heat index values
+                - Frontal systems bring weather changes
+                
+                Northeast: Humid continental climate
+                - Four distinct seasons with variable weather
+                - Nor'easter storms in winter bring heavy snow
+                - Lake effect snow in Great Lakes region
+                - Tropical systems can affect area in late summer
+                - Strong seasonal temperature contrasts
+                """),
+                Document(text="""
+                Climate Oscillations and Their Weather Impacts:
+                
+                El Niño Southern Oscillation (ENSO):
+                El Niño Phase:
+                - Warmer, wetter winters in southern US
+                - Reduced Atlantic hurricane activity
+                - Milder winters in northern US
+                - Increased precipitation in California
+                - Warmer global temperatures
+                
+                La Niña Phase:
+                - Cooler, drier winters in southern US
+                - Increased Atlantic hurricane activity
+                - Harsher winters in northern US
+                - Drought conditions in Southwest
+                - Cooler global temperatures
+                
+                Pacific Decadal Oscillation (PDO):
+                - 20-30 year cycles affecting Pacific Basin
+                - Influences long-term precipitation patterns
+                - Affects marine ecosystems and weather
+                
+                North Atlantic Oscillation (NAO):
+                - Affects European and eastern US weather
+                - Positive phase: mild, wet European winters
+                - Negative phase: cold European winters, eastern US storms
+                
+                These oscillations interact to create complex weather patterns.
+                Always consider ENSO phase when analyzing seasonal forecasts.
+                """),
+                Document(text="""
+                Severe Weather Formation and Safety:
+                
+                Thunderstorms:
+                Formation: Instability + moisture + lifting mechanism
+                Types: Single-cell, multi-cell, supercell
+                Hazards: Lightning, hail, damaging winds, flooding, tornadoes
+                Safety: Seek substantial shelter, avoid windows, wait 30 minutes after last thunder
+                
+                Tornadoes:
+                Formation: Wind shear + instability + low-level rotation
+                Scale: EF0 (light damage) to EF5 (incredible destruction)
+                Peak season: April-June in late afternoon/early evening
+                Safety: Lowest floor, interior room, cover yourself
+                
+                Hurricanes:
+                Formation: Warm ocean water >80°F + low wind shear + disturbance
+                Hazards: Storm surge (primary killer), winds, flooding, tornadoes
+                Categories: 1 (minimal) to 5 (catastrophic) based on wind speed
+                Safety: Evacuate if ordered, never drive through flooded roads
+                
+                Winter Storms:
+                Types: Blizzards, ice storms, nor'easters
+                Hazards: Heavy snow, ice accumulation, wind chill, power outages
+                Formation: Cold air + moisture + storm system
+                Safety: Emergency supplies, avoid travel, carbon monoxide awareness
+                """),
+                Document(text="""
+                Seasonal Weather Patterns and Climate Context:
+                
+                Spring (March-May):
+                - Rapid warming with large temperature swings
+                - Peak severe weather season due to strong temperature contrasts
+                - Increasing thunderstorm activity
+                - Transition from winter to summer circulation patterns
+                - Flooding potential from snowmelt and spring rains
+                
+                Summer (June-August):
+                - Peak heat with minimal daily temperature variation
+                - Convective afternoon/evening thunderstorms
+                - Hurricane season development
+                - Monsoon activity in Southwest
+                - Heat dome and heat wave potential
+                
+                Fall (September-November):
+                - Gradual cooling with increasing day-to-day variability
+                - Peak hurricane season (August-October)
+                - First frost and end of growing season
+                - Beautiful foliage season in deciduous regions
+                - Transition to winter storm patterns
+                
+                Winter (December-February):
+                - Coldest temperatures with arctic air mass intrusions
+                - Snow season in northern regions
+                - Nor'easters and other winter storm systems
+                - Polar vortex displacement events
+                - Shortest days with limited solar heating
+                
+                Each season has distinct weather patterns influenced by solar angle,
+                jet stream position, and large-scale circulation patterns.
+                """)
+            ]
+            
+            # Add knowledge base content as documents
+            phenomenal_knowledge = self.weather_knowledge_base.meteorological_knowledge
+            climate_patterns = self.weather_knowledge_base.climate_patterns
+            
+            # Convert knowledge base content to documents
+            for category, content in phenomenal_knowledge.items():
+                weather_docs.append(Document(text=f"Meteorological Knowledge - {category}: {str(content)}"))
+            
+            for pattern_type, patterns in climate_patterns.items():
+                weather_docs.append(Document(text=f"Climate Patterns - {pattern_type}: {str(patterns)}"))
+            
+            # Create vector index
+            if Settings.embed_model:
+                self.index = VectorStoreIndex.from_documents(weather_docs)
+                self.chat_engine = self.index.as_chat_engine(
+                    chat_mode="context",
+                    memory=self.memory,
+                    system_prompt=self.weather_context
+                )
+                logger.info("Enhanced weather knowledge base created successfully")
+            
+        except Exception as e:
+            logger.error(f"Error building enhanced knowledge base: {e}")
+    
+    async def process_weather_query(self, user_message: str, chat_history: List = None) -> Dict:
+        """Process weather query with enhanced climate expertise and AI integration"""
+        try:
+            # Update conversation memory with chat history if provided
+            if chat_history and self.memory:
+                await self._update_conversation_memory(chat_history)
+            
+            # Parse the query using basic NLP
+            basic_analysis = self.nlp_processor.process_query(user_message)
+            
+            # Enhance with climate expert analysis
+            enhanced_analysis = self.climate_expert_nlp.enhance_query_processing(basic_analysis, user_message)
+            
+            cities = enhanced_analysis.get('cities', [])
+            query_type = enhanced_analysis.get('query_type', 'general')
+            is_comparison = enhanced_analysis.get('comparison_info', {}).get('is_comparison', False)
+            climate_intelligence = enhanced_analysis.get('climate_intelligence', {})
+            
+            # Get weather data for mentioned cities
+            weather_data = {}
+            climate_analysis_data = {}
+            
+            for city in cities:
+                coords = self.weather_client.geocode_location(city)
+                if coords:
+                    lat, lon = coords
+                    forecast = self.weather_client.get_forecast(lat, lon)
+                    current_obs = self.weather_client.get_current_observations(lat, lon)
+                    
+                    weather_data[city] = {
+                        'coordinates': coords,
+                        'forecast': forecast,
+                        'current': current_obs
+                    }
+                    
+                    # Add climate analysis if needed
+                    if enhanced_analysis.get('expert_context', {}).get('requires_technical_explanation'):
+                        climate_analysis_data[city] = await self._get_climate_analysis(city, enhanced_analysis)
+            
+            # Generate enhanced AI response with climate expertise
+            ai_response = await self._generate_enhanced_ai_response(
+                user_message, weather_data, enhanced_analysis, climate_analysis_data, chat_history
+            )
+            
+            # Prepare map data
+            map_data = self._prepare_map_data(cities, weather_data)
+            
+            return {
+                'response': ai_response,
+                'cities': cities,
+                'weather_data': weather_data,
+                'climate_analysis': climate_analysis_data,
+                'map_data': map_data,
+                'query_analysis': enhanced_analysis,
+                'climate_intelligence': climate_intelligence,
+                'map_update_needed': len(cities) > 0,
+                'comparison_mode': is_comparison,
+                'expertise_level': climate_intelligence.get('expertise_level', 'basic'),
+                'requires_expert_explanation': enhanced_analysis.get('expert_context', {}).get('requires_technical_explanation', False)
+            }
+            
+        except Exception as e:
+            logger.error(f"Error processing weather query: {e}")
+            return {
+                'response': f"I apologize, but I encountered an error processing your weather query: {str(e)}",
+                'cities': [],
+                'weather_data': {},
+                'climate_analysis': {},
+                'map_data': [],
+                'query_analysis': {},
+                'climate_intelligence': {},
+                'map_update_needed': False,
+                'comparison_mode': False,
+                'expertise_level': 'basic',
+                'requires_expert_explanation': False
+            }
+    
+    async def _update_conversation_memory(self, chat_history: List) -> None:
+        """Update LlamaIndex memory with recent chat history"""
+        try:
+            if not self.memory or not chat_history:
+                return
+            
+            # Process recent conversation history (last 6 messages to avoid token limits)
+            recent_history = chat_history[-6:]
+            
+            for entry in recent_history:
+                if isinstance(entry, dict):
+                    # Handle messages format: {"role": "user/assistant", "content": "..."}
+                    role = entry.get('role', '')
+                    content = entry.get('content', '')
+                    
+                    if role == 'user' and content:
+                        # Add user message to memory
+                        self.memory.put(f"User: {content}")
+                    elif role == 'assistant' and content:
+                        # Add assistant message to memory
+                        self.memory.put(f"Assistant: {content}")
+                        
+                elif isinstance(entry, list) and len(entry) >= 2:
+                    # Handle legacy format: [user_message, assistant_response]
+                    user_msg, assistant_msg = entry[0], entry[1]
+                    if user_msg:
+                        self.memory.put(f"User: {user_msg}")
+                    if assistant_msg:
+                        self.memory.put(f"Assistant: {assistant_msg}")
+            
+            logger.debug(f"Updated conversation memory with {len(recent_history)} recent messages")
+            
+        except Exception as e:
+            logger.error(f"Error updating conversation memory: {e}")
+
+    def _extract_conversation_context(self, chat_history: List) -> str:
+        """Extract relevant context from conversation history for continuity"""
+        try:
+            if not chat_history:
+                return ""
+            
+            # Look for recent cities and topics in conversation
+            recent_cities = []
+            recent_topics = []
+            
+            # Check last few messages for context
+            for entry in reversed(chat_history[-4:]):
+                if isinstance(entry, dict):
+                    content = entry.get('content', '')
+                elif isinstance(entry, list) and len(entry) >= 1:
+                    content = entry[0]  # User message
+                else:
+                    continue
+                
+                # Extract cities mentioned
+                analysis = self.nlp_processor.process_query(content)
+                cities = analysis.get('cities', [])
+                if cities:
+                    recent_cities.extend(cities)
+                
+                # Extract weather topics
+                content_lower = content.lower()
+                if any(topic in content_lower for topic in ['temperature', 'rain', 'weather', 'forecast', 'conditions']):
+                    recent_topics.append(content[:100])  # First 100 chars
+            
+            # Build context summary
+            context_parts = []
+            
+            if recent_cities:
+                unique_cities = []
+                for city in recent_cities:
+                    if city not in unique_cities:
+                        unique_cities.append(city)
+                context_parts.append(f"Recently discussed cities: {', '.join(unique_cities[:3])}")
+            
+            if recent_topics:
+                context_parts.append(f"Recent conversation topics: {'; '.join(recent_topics[-2:])}")
+            
+            return '; '.join(context_parts) if context_parts else ""
+            
+        except Exception as e:
+            logger.error(f"Error extracting conversation context: {e}")
+            return ""
+
+    def _format_weather_context(self, weather_data: Dict, query_analysis: Dict) -> str:
+        """Format weather data for AI context with rich details"""
+        if not weather_data:
+            return "No weather data available."
+        
+        context_parts = []
+        
+        for city, data in weather_data.items():
+            forecast = data.get('forecast', [])
+            current = data.get('current', {})
+            
+            city_context = f"\n{city.title()}:"
+            
+            if forecast:
+                current_period = forecast[0]
+                temp = current_period.get('temperature', 'N/A')
+                temp_unit = current_period.get('temperatureUnit', 'F')
+                conditions = current_period.get('shortForecast', 'N/A')
+                wind_speed = current_period.get('windSpeed', 'N/A')
+                wind_dir = current_period.get('windDirection', '')
+                precip = current_period.get('precipitationProbability', 0)
+                
+                city_context += f"""
+                - Current Temperature: {temp}°{temp_unit}
+                - Conditions: {conditions}
+                - Wind: {wind_speed} {wind_dir}
+                - Precipitation Chance: {precip}%
+                - Detailed Forecast: {current_period.get('detailedForecast', 'N/A')[:200]}...
+                """
+                
+                # Add next few periods for context
+                if len(forecast) > 1:
+                    city_context += "\n  Next periods:"
+                    for i, period in enumerate(forecast[1:4], 1):
+                        name = period.get('name', f'Period {i+1}')
+                        temp = period.get('temperature', 'N/A')
+                        conditions = period.get('shortForecast', 'N/A')
+                        city_context += f"\n    - {name}: {temp}°F, {conditions}"
+            
+            if current:
+                temp_c = current.get('temperature')
+                if temp_c:
+                    temp_f = (temp_c * 9/5) + 32
+                    city_context += f"\n- Observed Temperature: {temp_f:.1f}°F"
+                
+                humidity = current.get('relativeHumidity', {})
+                if isinstance(humidity, dict):
+                    humidity_val = humidity.get('value')
+                    if humidity_val:
+                        city_context += f"\n- Humidity: {humidity_val}%"
+            
+            context_parts.append(city_context)
+        
+        return "\n".join(context_parts)
+
+    async def _generate_ai_response(self, user_message: str, weather_data: Dict, query_analysis: Dict, chat_history: List = None) -> str:
+        """Generate intelligent AI response using LlamaIndex and Gemini"""
+        try:
+            # Prepare context with weather data
+            weather_context = self._format_weather_context(weather_data, query_analysis)
+            
+            # Extract enhanced context
+            activity_context = query_analysis.get('activity_context', {})
+            forecast_context = query_analysis.get('forecast_context', {})
+            historical_context = query_analysis.get('historical_context', {})
+            response_hints = query_analysis.get('response_hints', {})
+            
+            # Build enhanced prompt based on query type
+            enhanced_prompt = f"""
+            User Query: {user_message}
+            
+            Weather Data Available:
+            {weather_context}
+            
+            Query Analysis:
+            - Cities mentioned: {query_analysis.get('cities', [])}
+            - Query type: {query_analysis.get('query_type', 'general')}
+            - Is comparison: {query_analysis.get('comparison_info', {}).get('is_comparison', False)}
+            
+            """
+            
+            # Add conversation context if available
+            if chat_history:
+                context_summary = self._extract_conversation_context(chat_history)
+                if context_summary:
+                    enhanced_prompt += f"""
+            Conversation Context:
+            {context_summary}
+            
+            """
+            
+            # Add activity-specific guidance
+            if activity_context.get('has_activity'):
+                enhanced_prompt += f"""
+            Activity Context:
+            - Activity type: {activity_context.get('activity_type', 'general')}
+            - Specific activity: {activity_context.get('specific_activity', 'N/A')}
+            - Advice intent: {activity_context.get('advice_intent', False)}
+            
+            Please provide weather-based advice for this activity, considering:
+            - Safety factors (precipitation, wind, visibility, temperature extremes)
+            - Comfort factors (temperature, humidity, wind chill/heat index)
+            - Timing recommendations if conditions vary throughout the day
+            - Alternative suggestions if conditions are unfavorable
+            """
+            
+            # Add forecast-specific guidance
+            if forecast_context.get('has_forecast_intent'):
+                enhanced_prompt += f"""
+            Forecast Context:
+            - Timeline: {forecast_context.get('timeline', 'short_term')}
+            - Forecast type: {forecast_context.get('forecast_type', 'summary')}
+            - Detailed request: {forecast_context.get('detailed_request', False)}
+            
+            Please provide forecast information focusing on the requested timeline.
+            Include trends, changes, and key weather events expected.
+            """
+            
+            # Add historical context guidance
+            if historical_context.get('has_historical_intent'):
+                enhanced_prompt += f"""
+            Historical Context:
+            - Period: {historical_context.get('period', 'recent')}
+            - Comparison intent: {historical_context.get('comparison_intent', False)}
+            - Data request: {historical_context.get('data_request', False)}
+            
+            Please provide context about how current conditions compare to historical patterns.
+            Mention if conditions are typical, unusual, or record-setting for this time of year.
+            """
+            
+            enhanced_prompt += """
+            
+            Response Guidelines:
+            - Be conversational and engaging
+            - Include specific data from the weather information provided
+            - If comparing cities, highlight key differences
+            - Offer practical advice or insights when relevant
+            - For activity queries, prioritize safety and comfort recommendations
+            - Use clear, actionable language
+            - Consider conversation history for context and continuity
+            """
+            
+            # Use LlamaIndex chat engine if available (it has conversation memory built-in)
+            if self.chat_engine and LLAMA_INDEX_AVAILABLE:
+                response = await self._get_llamaindex_response(enhanced_prompt)
+            elif self.llm and LLAMA_INDEX_AVAILABLE:
+                response = await self._get_direct_llm_response(enhanced_prompt)
+            elif GEMINI_AVAILABLE and self.gemini_api_key:
+                response = await self._get_gemini_response(enhanced_prompt)
+            else:
+                response = self._generate_basic_expert_response(user_message, weather_data, query_analysis)
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error generating AI response: {e}")
+            return self._generate_basic_expert_response(user_message, weather_data, query_analysis)
+    
+    async def _get_llamaindex_response(self, prompt: str) -> str:
+        """Get response using LlamaIndex chat engine"""
+        try:
+            response = await self.chat_engine.achat(prompt)
+            return str(response)
+        except Exception as e:
+            logger.error(f"LlamaIndex chat error: {e}")
+            raise
+    
+    async def _get_direct_llm_response(self, prompt: str) -> str:
+        """Get response using direct LLM call"""
+        try:
+            response = await self.llm.acomplete(prompt)
+            return str(response)
+        except Exception as e:
+            logger.error(f"Direct LLM error: {e}")
+            raise
+    
+    async def _get_gemini_response(self, prompt: str) -> str:
+        """Get response using direct Gemini API"""
+        try:
+            model = genai.GenerativeModel('gemini-2.0-flash-exp')
+            response = await model.generate_content_async(prompt)
+            return response.text
+        except Exception as e:
+            logger.error(f"Gemini API error: {e}")
+            raise
+    
+    async def _get_climate_analysis(self, city: str, enhanced_analysis: Dict) -> Dict:
+        """Get advanced climate analysis for a city"""
+        try:
+            from ..analysis.climate_analyzer import create_climate_analyzer
+            
+            climate_analyzer = create_climate_analyzer(self.weather_client)
+            analysis_results = {}
+            
+            # Get the analysis type based on query complexity
+            climate_intelligence = enhanced_analysis.get('climate_intelligence', {})
+            query_type = enhanced_analysis.get('query_type', 'general')
+            
+            # Temperature trend analysis
+            if 'temperature' in query_type.lower():
+                analysis_results['temperature_trends'] = climate_analyzer.analyze_temperature_trends(city, days=30)
+            
+            # Pattern detection for complex queries
+            if climate_intelligence.get('expertise_level') in ['intermediate', 'expert']:
+                analysis_results['weather_patterns'] = climate_analyzer.detect_weather_patterns(city, 'seasonal')
+            
+            # Anomaly prediction for expert queries
+            if climate_intelligence.get('expert_analysis_needed'):
+                analysis_results['anomaly_prediction'] = climate_analyzer.predict_weather_anomalies(city, days_ahead=7)
+            
+            # City comparison if multiple cities
+            cities = enhanced_analysis.get('cities', [])
+            if len(cities) > 1:
+                analysis_results['city_comparison'] = climate_analyzer.compare_cities_climate(cities[:3])  # Limit to 3 cities
+            
+            return analysis_results
+            
+        except Exception as e:
+            logger.error(f"Error getting climate analysis for {city}: {e}")
+            return {}
+
+    async def _generate_enhanced_ai_response(self, user_message: str, weather_data: Dict, 
+                                           enhanced_analysis: Dict, climate_analysis_data: Dict, 
+                                           chat_history: List = None) -> str:
+        """Generate enhanced AI response with climate expertise"""
+        try:
+            # Get climate intelligence and expert context
+            climate_intelligence = enhanced_analysis.get('climate_intelligence', {})
+            expert_context = enhanced_analysis.get('expert_context', {})
+            regional_context = enhanced_analysis.get('regional_climate_context', {})
+            
+            # Prepare comprehensive weather context
+            weather_context = self._format_enhanced_weather_context(
+                weather_data, enhanced_analysis, climate_analysis_data, regional_context
+            )
+            
+            # Add climate phenomenon explanations if needed
+            phenomena_explanations = self._get_phenomenon_explanations(climate_intelligence)
+            
+            # Build expert prompt based on sophistication level
+            expert_prompt = self._build_expert_prompt(
+                enhanced_analysis, climate_intelligence, phenomena_explanations
+            )
+            
+            # Extract conversation context
+            conversation_context = ""
+            if chat_history:
+                conversation_context = self._extract_conversation_context(chat_history)
+            
+            # Use LlamaIndex chat engine if available
+            if self.chat_engine and LLAMA_INDEX_AVAILABLE:
+                full_query = f"""
+                {expert_prompt}
+                
+                Weather Data Context:
+                {weather_context}
+                
+                {conversation_context}
+                
+                User Query: {user_message}
+                
+                Please provide a comprehensive response that includes:
+                1. Direct answer to the user's question
+                2. Relevant meteorological explanation
+                3. Regional climate context where applicable
+                4. Safety considerations if relevant
+                5. Expert insights based on the data provided
+                """
+                
+                response = await asyncio.to_thread(self.chat_engine.chat, full_query)
+                
+                # Handle different response types from LlamaIndex
+                if hasattr(response, 'response'):
+                    return str(response.response)
+                elif hasattr(response, 'content'):
+                    return str(response.content)
+                else:
+                    return str(response)
+            
+            # Fallback to direct Gemini API
+            elif GEMINI_AVAILABLE and self.gemini_api_key:
+                return await self._generate_direct_gemini_response(
+                    user_message, weather_context, expert_prompt, conversation_context
+                )
+            
+            # Basic fallback response
+            return self._generate_basic_expert_response(
+                user_message, weather_data, enhanced_analysis
+            )
+            
+        except Exception as e:
+            logger.error(f"Error generating enhanced AI response: {e}")
+            return self._generate_basic_expert_response(user_message, weather_data, enhanced_analysis)
+
+    def _format_enhanced_weather_context(self, weather_data: Dict, enhanced_analysis: Dict, 
+                                       climate_analysis_data: Dict, regional_context: Dict) -> str:
+        """Format comprehensive weather context with climate expertise"""
+        context_parts = []
+        
+        # Current weather data
+        for city, data in weather_data.items():
+            forecast = data.get('forecast', [])
+            if forecast:
+                current = forecast[0]
+                context_parts.append(f"""
+                📍 {city.title()} Current Conditions:
+                🌡️ Temperature: {current.get('temperature', 'N/A')}°F
+                🌤️ Conditions: {current.get('shortForecast', 'N/A')}
+                🌧️ Precipitation Chance: {current.get('precipitationProbability', 0)}%
+                💨 Wind: {current.get('windSpeed', 'N/A')} {current.get('windDirection', '')}
+                💧 Humidity: {current.get('relativeHumidity', {}).get('value', 'N/A')}%
+                """)
+        
+        # Climate analysis data
+        for city, analysis in climate_analysis_data.items():
+            if analysis:
+                context_parts.append(f"\n🔬 Climate Analysis for {city.title()}:")
+                
+                if 'temperature_trends' in analysis:
+                    trends = analysis['temperature_trends']
+                    if 'statistics' in trends:
+                        stats = trends['statistics']
+                        context_parts.append(f"📈 30-day temperature trend: {stats.get('trend_direction', 'stable')}")
+                        context_parts.append(f"📊 Average: {stats.get('mean_temp', 'N/A'):.1f}°F")
+                
+                if 'weather_patterns' in analysis:
+                    patterns = analysis['weather_patterns']
+                    confidence = patterns.get('confidence', 0)
+                    context_parts.append(f"🔍 Weather pattern confidence: {confidence:.2f}")
+                
+                if 'anomaly_prediction' in analysis:
+                    anomalies = analysis['anomaly_prediction']
+                    risk_level = anomalies.get('risk_level', 'low')
+                    context_parts.append(f"⚠️ Weather anomaly risk: {risk_level}")
+        
+        # Regional climate context
+        for city, region_info in regional_context.items():
+            if region_info:
+                climate_type = region_info.get('climate_type', 'unknown')
+                context_parts.append(f"\n🌍 {city.title()} Regional Climate: {climate_type}")
+                
+                seasonal_chars = region_info.get('seasonal_characteristics', {})
+                if seasonal_chars:
+                    context_parts.append(f"🗓️ Seasonal characteristics: {seasonal_chars.get('characteristics', [])}")
+        
+        return "\n".join(context_parts)
+
+    def _get_phenomenon_explanations(self, climate_intelligence: Dict) -> str:
+        """Get explanations for detected climate phenomena"""
+        explanations = []
+        
+        phenomena = climate_intelligence.get('climate_phenomena', [])
+        for phenomenon in phenomena:
+            explanation = self.weather_knowledge_base.get_phenomenon_explanation(phenomenon)
+            if explanation:
+                explanations.append(f"🌡️ {phenomenon.replace('_', ' ').title()}: {explanation}")
+        
+        return "\n".join(explanations) if explanations else ""
+
+    def _build_expert_prompt(self, enhanced_analysis: Dict, climate_intelligence: Dict, 
+                           phenomena_explanations: str) -> str:
+        """Build expert-level prompt based on query sophistication"""
+        expertise_level = climate_intelligence.get('expertise_level', 'basic')
+        seasonal_context = climate_intelligence.get('seasonal_context', {})
+        
+        base_prompt = f"""
+        You are responding as an expert meteorologist and climate scientist with access to comprehensive weather data 
+        and advanced climate analysis. The user's query requires a {expertise_level}-level response.
+        """
+        
+        if expertise_level == 'expert':
+            base_prompt += """
+            Provide detailed meteorological explanations including:
+            - Atmospheric dynamics and physical processes
+            - Synoptic and mesoscale weather patterns
+            - Climate oscillations and their impacts
+            - Statistical analysis and confidence levels
+            - Regional climatology and seasonal patterns
+            """
+        elif expertise_level == 'intermediate':
+            base_prompt += """
+            Include moderate technical detail with:
+            - Weather system explanations
+            - Regional climate factors
+            - Seasonal considerations
+            - Safety and impact information
+            """
+        else:
+            base_prompt += """
+            Provide clear, accessible explanations with:
+            - Current conditions and forecasts
+            - Basic weather reasoning
+            - Practical implications
+            - Safety considerations
+            """
+        
+        if phenomena_explanations:
+            base_prompt += f"\n\nRelevant Climate Phenomena:\n{phenomena_explanations}"
+        
+        if seasonal_context:
+            current_season = seasonal_context.get('current_season', '')
+            base_prompt += f"\n\nSeasonal Context: Currently in {current_season} season."
+        
+        return base_prompt
+
+    async def _generate_direct_gemini_response(self, user_message: str, weather_context: str, 
+                                             expert_prompt: str, conversation_context: str) -> str:
+        """Generate response using direct Gemini API"""
+        try:
+            model = genai.GenerativeModel('gemini-2.0-flash-exp')
+            
+            full_prompt = f"""
+            {expert_prompt}
+            
+            Weather Data:
+            {weather_context}
+            
+            {conversation_context}
+            
+            User Question: {user_message}
+            
+            Provide a comprehensive, expert-level weather response.
+            """
+            
+            response = await asyncio.to_thread(model.generate_content, full_prompt)
+            return response.text
+            
+        except Exception as e:
+            logger.error(f"Error with direct Gemini API: {e}")
+            return f"I can provide the weather information, but I'm having trouble with the detailed analysis right now. {weather_context}"
+
+    def _generate_basic_expert_response(self, user_message: str, weather_data: Dict, 
+                                      enhanced_analysis: Dict) -> str:
+        """Generate basic expert response as fallback"""
+        cities = enhanced_analysis.get('cities', [])
+        query_type = enhanced_analysis.get('query_type', 'general')
+        
+        if not cities:
+            return "I'd be happy to help with weather information! Please specify a city or location for detailed analysis."
+        
+        response_parts = []
+        
+        for city in cities:
+            if city in weather_data:
+                forecast = weather_data[city].get('forecast', [])
+                if forecast:
+                    current = forecast[0]
+                    temp = current.get('temperature', 'N/A')
+                    conditions = current.get('shortForecast', 'N/A')
+                    
+                    response_parts.append(f"""
+                    📍 **{city.title()} Weather Analysis:**
+                    🌡️ Current: {temp}°F
+                    🌤️ Conditions: {conditions}
+                    
+                    **Meteorological Context:** The current conditions in {city.title()} are typical for this time of year. 
+                    {conditions.lower()} conditions are influenced by regional climate patterns and seasonal atmospheric circulation.
+                    """)
+        
+        if len(cities) > 1:
+            response_parts.append("\n**Regional Comparison:** These cities show the typical climate diversity across different regions, influenced by latitude, elevation, and local geographic features.")
+        
+        return "\n".join(response_parts)
+
+    def _prepare_map_data(self, cities: List[str], weather_data: Dict) -> List[Dict]:
+        """Prepare data for map visualization"""
+        map_data = []
+        
+        for city in cities:
+            if city in weather_data:
+                coords = weather_data[city].get('coordinates')
+                forecast = weather_data[city].get('forecast', [])
+                
+                if coords and forecast:
+                    lat, lon = coords
+                    map_data.append({
+                        'name': city,
+                        'lat': lat,
+                        'lon': lon,
+                        'forecast': forecast
+                    })
+        
+        return map_data
+
+def create_enhanced_chatbot(weather_client, nlp_processor, gemini_api_key: str = None) -> EnhancedWeatherChatbot:
+    """Factory function to create enhanced chatbot"""
+    return EnhancedWeatherChatbot(weather_client, nlp_processor, gemini_api_key)
+

src/chatbot/nlp_processor.py

@@ -0,0 +1,515 @@
+"""
+Natural Language Processing for Weather Queries
+Advanced query understanding and city extraction
+"""
+
+import re
+import logging
+from typing import List, Dict, Set, Tuple
+from datetime import datetime
+
+logger = logging.getLogger(__name__)
+
+class WeatherNLPProcessor:
+    """Advanced NLP processor for weather queries"""
+    
+    def __init__(self):
+        self.weather_keywords = {
+            'temperature': {
+                'primary': ['temperature', 'temp', 'degrees', 'hot', 'cold', 'warm', 'cool', 'heat', 'chill'],
+                'modifiers': ['high', 'low', 'maximum', 'minimum', 'average', 'current']
+            },
+            'precipitation': {
+                'primary': ['rain', 'precipitation', 'shower', 'drizzle', 'downpour', 'wet', 'storm', 'snow'],
+                'modifiers': ['chance', 'probability', 'amount', 'heavy', 'light']
+            },
+            'wind': {
+                'primary': ['wind', 'windy', 'breeze', 'gust', 'mph', 'speed'],
+                'modifiers': ['strong', 'light', 'direction', 'gusts']
+            },
+            'conditions': {
+                'primary': ['weather', 'conditions', 'forecast', 'outlook', 'sunny', 'cloudy', 'clear', 'overcast'],
+                'modifiers': ['current', 'today', 'tomorrow', 'this week']
+            },
+            'comparison': {
+                'primary': ['difference', 'compare', 'versus', 'vs', 'between', 'than', 'against'],
+                'modifiers': ['warmer', 'colder', 'wetter', 'drier', 'windier']
+            },
+            'time': {
+                'primary': ['today', 'tomorrow', 'yesterday', 'week', 'weekend', 'now', 'currently'],
+                'modifiers': ['this', 'next', 'last', 'morning', 'afternoon', 'evening', 'night']
+            },
+            'activity_advice': {
+                'primary': ['should', 'can', 'good', 'bad', 'safe', 'bike', 'biking', 'cycling', 'walk', 'walking', 
+                          'run', 'running', 'jog', 'jogging', 'picnic', 'bbq', 'barbecue', 'outdoor', 'outside',
+                          'drive', 'driving', 'travel', 'trip', 'swim', 'swimming', 'beach', 'park', 'hike', 'hiking'],
+                'modifiers': ['go', 'plan', 'planning', 'advisable', 'recommended', 'suitable', 'ideal']
+            },
+            'forecast': {
+                'primary': ['forecast', 'prediction', 'future', 'upcoming', 'next', 'later', 'will', 'going to',
+                          'expect', 'expected', 'outlook', 'trend', 'pattern'],
+                'modifiers': ['week', 'month', 'days', 'hours', 'extended', 'long term', 'short term']
+            },
+            'historical': {
+                'primary': ['past', 'previous', 'history', 'historical', 'ago', 'before', 'last', 'earlier',
+                          'trend', 'pattern', 'average', 'normal', 'typical', 'record'],
+                'modifiers': ['year', 'month', 'week', 'data', 'records', 'climate']
+            }
+        }
+        
+        # Keep a core set of major cities for pattern recognition and aliases
+        # This is used for pattern matching, not limiting geocoding
+        self.major_cities_patterns = {
+            'new york', 'los angeles', 'chicago', 'houston', 'phoenix', 'philadelphia',
+            'san antonio', 'san diego', 'dallas', 'san jose', 'austin', 'jacksonville',
+            'fort worth', 'columbus', 'charlotte', 'san francisco', 'indianapolis',
+            'seattle', 'denver', 'washington', 'boston', 'el paso', 'detroit',
+            'nashville', 'portland', 'memphis', 'oklahoma city', 'las vegas',
+            'louisville', 'baltimore', 'milwaukee', 'albuquerque', 'tucson',
+            'atlanta', 'miami', 'tampa', 'orlando', 'cleveland', 'pittsburgh',
+            'cincinnati', 'kansas city', 'raleigh', 'omaha', 'virginia beach',
+            'oakland', 'minneapolis', 'tulsa', 'new orleans', 'buffalo',
+            'lincoln', 'madison', 'boise', 'birmingham', 'spokane', 'columbia'
+        }
+        
+        # Common city abbreviations and aliases for better user experience
+        self.city_aliases = {
+            'nyc': 'New York City',
+            'la': 'Los Angeles',
+            'sf': 'San Francisco',
+            'dc': 'Washington DC',
+            'philly': 'Philadelphia',
+            'chi': 'Chicago',
+            'vegas': 'Las Vegas',
+            'nola': 'New Orleans',
+            'atx': 'Austin',
+            'h-town': 'Houston',
+            'pdx': 'Portland',
+            'the bay': 'San Francisco Bay Area',
+            'south beach': 'Miami Beach',
+            'motor city': 'Detroit',
+            'space city': 'Houston',
+            'city of angels': 'Los Angeles',
+            'windy city': 'Chicago',
+            'big apple': 'New York City',
+            'music city': 'Nashville',
+            'silicon valley': 'San Jose',
+            'bean town': 'Boston',
+            'mile high city': 'Denver',
+            'emerald city': 'Seattle',
+            'magic city': 'Miami',
+            'charm city': 'Baltimore',
+        }
+    
+    def _extract_potential_locations(self, text: str) -> List[str]:
+        """
+        Extract potential location names from text using multiple strategies.
+        This method identifies location candidates that will be validated via geocoding.
+        """
+        text_lower = text.lower().strip()
+        potential_locations = []
+        
+        # Strategy 1: Check for known aliases first
+        for alias, full_name in self.city_aliases.items():
+            if re.search(r'\b' + re.escape(alias) + r'\b', text_lower):
+                potential_locations.append(full_name)
+                logger.info(f"Found city alias: {alias} -> {full_name}")
+        
+        # Strategy 2: Look for known major city patterns
+        for city in self.major_cities_patterns:
+            pattern = r'\b' + re.escape(city) + r'\b'
+            if re.search(pattern, text_lower):
+                potential_locations.append(city.title())
+                logger.info(f"Found major city pattern: {city}")
+        
+        # Strategy 3: Extract capitalized words that could be city names
+        # Look for capitalized words (potential proper nouns)
+        words = re.findall(r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b', text)
+        for word in words:
+            word_lower = word.lower()
+            # Skip common non-location words
+            if word_lower not in {'weather', 'today', 'tomorrow', 'forecast', 'temperature', 
+                                'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 
+                                'saturday', 'sunday', 'morning', 'afternoon', 'evening', 
+                                'night', 'celsius', 'fahrenheit', 'america', 'united', 'states'}:
+                if len(word) >= 3:  # City names are usually at least 3 characters
+                    potential_locations.append(word)
+                    logger.info(f"Found potential location from capitalization: {word}")
+        
+        # Strategy 4: Look for patterns like "in [location]", "at [location]", "[location] weather"
+        location_patterns = [
+            r'(?:in|at|for|from)\s+([A-Za-z\s]+?)(?:\s+weather|\s+forecast|\s+temperature|$|[,.])',
+            r'([A-Za-z\s]+?)\s+(?:weather|forecast|temperature|rain|snow|wind)',
+            r'weather\s+(?:in|at|for)\s+([A-Za-z\s]+?)(?:$|[,.])',
+            r'how.*(?:in|at)\s+([A-Za-z\s]+?)(?:$|[,.\?])'
+        ]
+        
+        for pattern in location_patterns:
+            matches = re.finditer(pattern, text, re.IGNORECASE)
+            for match in matches:
+                location = match.group(1).strip()
+                if len(location) >= 3 and location.lower() not in {'the', 'and', 'for', 'with'}:
+                    potential_locations.append(location.title())
+                    logger.info(f"Found location from pattern: {location}")
+        
+        # Remove duplicates while preserving order
+        unique_locations = []
+        seen = set()
+        for location in potential_locations:
+            location_clean = location.strip()
+            if location_clean and location_clean.lower() not in seen:
+                unique_locations.append(location_clean)
+                seen.add(location_clean.lower())
+        
+        return unique_locations
+    
+    def extract_cities(self, text: str) -> List[str]:
+        """
+        Extract and validate city names from text using dynamic approach.
+        This method extracts potential locations and returns them for geocoding validation.
+        """
+        logger.info(f"Extracting cities from: '{text}'")
+        
+        # Extract potential locations using multiple strategies
+        potential_locations = self._extract_potential_locations(text)
+        
+        if not potential_locations:
+            logger.info("No potential locations found in text")
+            return []
+        
+        logger.info(f"Found potential locations: {potential_locations}")
+        
+        # Return all potential locations - the geocoding will validate them
+        # This allows for any city to be processed, not just those in predefined lists
+        return potential_locations
+    
+    def identify_query_type(self, text: str) -> str:
+        """Identify the primary intent of the weather query"""
+        text_lower = text.lower()
+        
+        # Score each category
+        scores = {}
+        
+        for category, keywords in self.weather_keywords.items():
+            score = 0
+            
+            # Primary keywords get higher weight
+            for keyword in keywords['primary']:
+                if keyword in text_lower:
+                    score += 3
+            
+            # Modifier keywords get lower weight
+            for keyword in keywords['modifiers']:
+                if keyword in text_lower:
+                    score += 1
+            
+            scores[category] = score
+        
+        # Return category with highest score
+        if not scores or max(scores.values()) == 0:
+            return 'general'
+        
+        return max(scores, key=scores.get)
+    
+    def extract_time_context(self, text: str) -> Dict:
+        """Extract temporal context from query"""
+        text_lower = text.lower()
+        
+        time_indicators = {
+            'now': ['now', 'current', 'currently', 'right now', 'at the moment'],
+            'today': ['today', 'this day'],
+            'tomorrow': ['tomorrow', 'next day'],
+            'this_week': ['this week', 'week', 'weekly'],
+            'weekend': ['weekend', 'saturday', 'sunday'],
+            'future': ['forecast', 'prediction', 'will be', 'going to be']
+        }
+        
+        detected_times = []
+        for time_type, indicators in time_indicators.items():
+            for indicator in indicators:
+                if indicator in text_lower:
+                    detected_times.append(time_type)
+                    break
+        
+        return {
+            'time_context': detected_times[0] if detected_times else 'general',
+            'all_contexts': detected_times
+        }
+    
+    def extract_comparison_intent(self, text: str) -> Dict:
+        """Extract comparison intent and structure"""
+        text_lower = text.lower()
+        
+        comparison_patterns = [
+            r'compare\s+(.+?)\s+(?:and|with|to|vs)\s+(.+)',
+            r'difference\s+between\s+(.+?)\s+and\s+(.+)',
+            r'(.+?)\s+(?:vs|versus)\s+(.+)',
+            r'(.+?)\s+compared\s+to\s+(.+)',
+            r'how\s+(?:different|similar)\s+(?:is|are)\s+(.+?)\s+(?:and|from)\s+(.+)'
+        ]
+        
+        for pattern in comparison_patterns:
+            match = re.search(pattern, text_lower)
+            if match:
+                entity1 = match.group(1).strip()
+                entity2 = match.group(2).strip()
+                
+                # Extract cities from each entity
+                cities1 = self.extract_cities(entity1)
+                cities2 = self.extract_cities(entity2)
+                
+                return {
+                    'is_comparison': True,
+                    'entities': [entity1, entity2],
+                    'cities': cities1 + cities2,
+                    'comparison_type': self.identify_query_type(text)
+                }
+        
+        return {'is_comparison': False}
+    
+    def extract_weather_attributes(self, text: str) -> List[str]:
+        """Extract specific weather attributes mentioned"""
+        text_lower = text.lower()
+        attributes = []
+        
+        attribute_patterns = {
+            'temperature': r'\b(?:temp|temperature|degrees?|hot|cold|warm|cool)\b',
+            'precipitation': r'\b(?:rain|precipitation|shower|storm|wet|dry)\b',
+            'wind': r'\b(?:wind|windy|breeze|gust)\b',
+            'humidity': r'\b(?:humid|humidity|moisture)\b',
+            'pressure': r'\b(?:pressure|barometric)\b',
+            'visibility': r'\b(?:visibility|fog|clear)\b',
+            'conditions': r'\b(?:sunny|cloudy|overcast|clear|stormy)\b'
+        }
+        
+        for attribute, pattern in attribute_patterns.items():
+            if re.search(pattern, text_lower):
+                attributes.append(attribute)
+        
+        return attributes
+    
+    def extract_activity_context(self, text: str) -> Dict:
+        """Extract activity and advice context from query"""
+        text_lower = text.lower()
+        
+        # Activity patterns
+        activity_patterns = {
+            'biking': ['bike', 'biking', 'cycling', 'bicycle', 'cycle'],
+            'walking': ['walk', 'walking', 'stroll', 'hike', 'hiking'],
+            'running': ['run', 'running', 'jog', 'jogging', 'exercise'],
+            'outdoor_events': ['picnic', 'bbq', 'barbecue', 'outdoor', 'outside', 'park'],
+            'travel': ['drive', 'driving', 'travel', 'trip', 'road trip'],
+            'water_activities': ['swim', 'swimming', 'beach', 'pool', 'lake'],
+            'sports': ['game', 'match', 'tournament', 'sports', 'baseball', 'football', 'soccer', 'tennis']
+        }
+        
+        # Advice patterns
+        advice_patterns = [
+            'should i', 'can i', 'is it good', 'is it safe', 'would it be',
+            'advisable', 'recommended', 'good idea', 'bad idea', 'suitable',
+            'ideal for', 'perfect for', 'okay to', 'fine to'
+        ]
+        
+        detected_activities = []
+        for activity_type, keywords in activity_patterns.items():
+            for keyword in keywords:
+                if keyword in text_lower:
+                    detected_activities.append(activity_type)
+                    break
+        
+        # Check for advice intent
+        advice_intent = any(pattern in text_lower for pattern in advice_patterns)
+        
+        # Extract specific activity mentioned
+        specific_activity = None
+        for activity_type, keywords in activity_patterns.items():
+            for keyword in keywords:
+                if keyword in text_lower:
+                    specific_activity = keyword
+                    break
+            if specific_activity:
+                break
+        
+        return {
+            'has_activity': bool(detected_activities),
+            'activities': detected_activities,
+            'specific_activity': specific_activity,
+            'advice_intent': advice_intent,
+            'activity_type': detected_activities[0] if detected_activities else None
+        }
+    
+    def extract_forecast_context(self, text: str) -> Dict:
+        """Extract forecast and timeline context"""
+        text_lower = text.lower()
+        
+        # Timeline patterns
+        timeline_patterns = {
+            'short_term': ['today', 'tonight', 'tomorrow', 'next few hours', 'this afternoon', 'this evening'],
+            'medium_term': ['this week', 'next week', 'weekend', 'next few days', 'coming days'],
+            'long_term': ['next month', 'next season', 'long term', 'extended forecast', 'monthly outlook']
+        }
+        
+        # Forecast types
+        forecast_types = {
+            'detailed': ['detailed', 'hourly', 'hour by hour', 'breakdown'],
+            'summary': ['summary', 'overview', 'general', 'brief'],
+            'trends': ['trend', 'pattern', 'outlook', 'change', 'changing']
+        }
+        
+        detected_timeline = []
+        detected_types = []
+        
+        for timeline, keywords in timeline_patterns.items():
+            for keyword in keywords:
+                if keyword in text_lower:
+                    detected_timeline.append(timeline)
+                    break
+        
+        for forecast_type, keywords in forecast_types.items():
+            for keyword in keywords:
+                if keyword in text_lower:
+                    detected_types.append(forecast_type)
+                    break
+        
+        return {
+            'has_forecast_intent': any(word in text_lower for word in ['forecast', 'future', 'will be', 'going to', 'expect', 'prediction']),
+            'timeline': detected_timeline[0] if detected_timeline else 'short_term',
+            'all_timelines': detected_timeline,
+            'forecast_type': detected_types[0] if detected_types else 'summary',
+            'detailed_request': 'detailed' in detected_types or 'hourly' in text_lower
+        }
+    
+    def extract_historical_context(self, text: str) -> Dict:
+        """Extract historical data context"""
+        text_lower = text.lower()
+        
+        # Historical patterns
+        historical_patterns = {
+            'recent': ['yesterday', 'last week', 'past week', 'recent', 'lately'],
+            'seasonal': ['last month', 'past month', 'last season', 'past season'],
+            'yearly': ['last year', 'past year', 'previous year', 'annual'],
+            'long_term': ['historical', 'history', 'records', 'archive', 'past data', 'climate data']
+        }
+        
+        # Comparison with historical data
+        historical_comparison = any(word in text_lower for word in [
+            'compared to', 'versus last', 'than usual', 'than normal', 'average',
+            'typical', 'record', 'above normal', 'below normal'
+        ])
+        
+        detected_periods = []
+        for period, keywords in historical_patterns.items():
+            for keyword in keywords:
+                if keyword in text_lower:
+                    detected_periods.append(period)
+                    break
+        
+        return {
+            'has_historical_intent': any(word in text_lower for word in ['past', 'history', 'historical', 'ago', 'before', 'last', 'previous']),
+            'period': detected_periods[0] if detected_periods else 'recent',
+            'all_periods': detected_periods,
+            'comparison_intent': historical_comparison,
+            'data_request': any(word in text_lower for word in ['data', 'records', 'statistics', 'stats'])
+        }
+
+    def process_query(self, text: str) -> Dict:
+        """Comprehensive query processing"""
+        # Basic extraction
+        cities = self.extract_cities(text)
+        query_type = self.identify_query_type(text)
+        time_context = self.extract_time_context(text)
+        comparison_info = self.extract_comparison_intent(text)
+        weather_attributes = self.extract_weather_attributes(text)
+        activity_context = self.extract_activity_context(text)
+        forecast_context = self.extract_forecast_context(text)
+        historical_context = self.extract_historical_context(text)
+        
+        # Determine complexity
+        complexity = 'simple'
+        if len(cities) > 1 or comparison_info['is_comparison']:
+            complexity = 'comparison'
+        elif len(weather_attributes) > 2:
+            complexity = 'complex'
+        
+        # Generate response hints
+        response_hints = self._generate_response_hints(
+            query_type, cities, comparison_info, weather_attributes,
+            activity_context, forecast_context, historical_context
+        )
+        
+        return {
+            'original_text': text,
+            'cities': cities,
+            'query_type': query_type,
+            'time_context': time_context,
+            'comparison_info': comparison_info,
+            'weather_attributes': weather_attributes,
+            'activity_context': activity_context,
+            'forecast_context': forecast_context,
+            'historical_context': historical_context,
+            'complexity': complexity,
+            'response_hints': response_hints,
+            'has_multiple_cities': len(cities) > 1,
+            'confidence': self._calculate_confidence(cities, query_type, weather_attributes)
+        }
+    
+    def _generate_response_hints(self, query_type: str, cities: List[str], 
+                                comparison_info: Dict, attributes: List[str],
+                                activity_context: Dict, forecast_context: Dict, 
+                                historical_context: Dict) -> Dict:
+        """Generate hints for response generation"""
+        hints = {
+            'focus_on': query_type,
+            'include_map_update': len(cities) > 0,
+            'comparison_mode': comparison_info['is_comparison'],
+            'detailed_attributes': attributes,
+            'response_style': 'detailed' if len(attributes) > 1 else 'concise',
+            'activity_advice': activity_context.get('advice_intent', False),
+            'specific_activity': activity_context.get('specific_activity'),
+            'forecast_timeline': forecast_context.get('timeline', 'short_term'),
+            'historical_period': historical_context.get('period', 'recent')
+        }
+        
+        if comparison_info['is_comparison']:
+            hints['comparison_type'] = comparison_info.get('comparison_type', 'general')
+        
+        # Add activity-specific hints
+        if activity_context.get('has_activity'):
+            hints['activity_type'] = activity_context.get('activity_type')
+            hints['needs_weather_advice'] = True
+            
+        # Add forecast-specific hints
+        if forecast_context.get('has_forecast_intent'):
+            hints['forecast_type'] = forecast_context.get('forecast_type', 'summary')
+            hints['detailed_forecast'] = forecast_context.get('detailed_request', False)
+            
+        # Add historical-specific hints
+        if historical_context.get('has_historical_intent'):
+            hints['historical_comparison'] = historical_context.get('comparison_intent', False)
+            hints['data_focus'] = historical_context.get('data_request', False)
+        
+        return hints
+    
+    def _calculate_confidence(self, cities: List[str], query_type: str, 
+                            attributes: List[str]) -> float:
+        """Calculate confidence in query understanding"""
+        confidence = 0.5  # Base confidence
+        
+        # Boost confidence for recognized cities
+        if cities:
+            confidence += 0.3
+        
+        # Boost confidence for clear query type
+        if query_type != 'general':
+            confidence += 0.2
+        
+        # Boost confidence for specific attributes
+        if attributes:
+            confidence += 0.1 * min(len(attributes), 3)
+        
+        return min(1.0, confidence)
+
+def create_nlp_processor() -> WeatherNLPProcessor:
+    """Factory function to create NLP processor"""
+    return WeatherNLPProcessor()
+

src/chatbot/weather_knowledge_base.py

@@ -0,0 +1,587 @@
+"""
+Enhanced Weather Knowledge Base
+Comprehensive meteorological and climate expertise for intelligent weather analysis
+"""
+
+import json
+import logging
+from typing import Dict, List, Optional, Any
+from datetime import datetime, timedelta
+
+logger = logging.getLogger(__name__)
+
+class WeatherKnowledgeBase:
+    """Comprehensive weather and climate knowledge base for expert-level responses"""
+    
+    def __init__(self):
+        self.meteorological_knowledge = self._build_meteorological_knowledge()
+        self.climate_patterns = self._build_climate_patterns()
+        self.weather_phenomena = self._build_weather_phenomena()
+        self.seasonal_patterns = self._build_seasonal_patterns()
+        self.regional_climatology = self._build_regional_climatology()
+        self.extreme_weather_knowledge = self._build_extreme_weather_knowledge()
+        
+    def _build_meteorological_knowledge(self) -> Dict:
+        """Build comprehensive meteorological knowledge"""
+        return {
+            'atmospheric_dynamics': {
+                'pressure_systems': {
+                    'high_pressure': {
+                        'characteristics': 'Clockwise circulation, subsiding air, clear skies',
+                        'weather_effects': 'Generally fair weather, light winds, stable conditions',
+                        'formation': 'Cooling aloft, surface divergence, subsidence',
+                        'movement': 'Generally eastward in mid-latitudes'
+                    },
+                    'low_pressure': {
+                        'characteristics': 'Counterclockwise circulation, rising air, cloud formation',
+                        'weather_effects': 'Clouds, precipitation, stronger winds, unstable conditions',
+                        'formation': 'Surface convergence, lifting mechanisms, instability',
+                        'movement': 'Generally eastward with steering flow'
+                    }
+                },
+                'frontal_systems': {
+                    'cold_front': {
+                        'characteristics': 'Sharp temperature gradient, steep slope, rapid movement',
+                        'weather_effects': 'Thunderstorms, brief heavy rain, temperature drop, wind shift',
+                        'approach_signs': 'Towering cumulus, wind shift, pressure drop',
+                        'passage_signs': 'Temperature drop, wind shift to northwest, clearing'
+                    },
+                    'warm_front': {
+                        'characteristics': 'Gradual temperature rise, shallow slope, slow movement',
+                        'weather_effects': 'Stratiform clouds, light precipitation, gradual warming',
+                        'approach_signs': 'Cirrus clouds, gradual cloud thickening, pressure fall',
+                        'passage_signs': 'Temperature rise, wind shift to southwest, clearing'
+                    },
+                    'occluded_front': {
+                        'characteristics': 'Complex structure, cold air overtaking warm sector',
+                        'weather_effects': 'Mixed precipitation, complex weather patterns',
+                        'formation': 'Cold front catches up to warm front',
+                        'evolution': 'Eventually weakens as temperature contrast decreases'
+                    }
+                },
+                'wind_patterns': {
+                    'jet_stream': {
+                        'description': 'High-altitude river of fast-moving air',
+                        'altitude': '30,000-40,000 feet (9-12 km)',
+                        'speed': '80-200+ mph (130-320+ km/h)',
+                        'weather_impact': 'Steers storm systems, influences temperature patterns',
+                        'seasonal_variation': 'Stronger and farther south in winter'
+                    },
+                    'trade_winds': {
+                        'description': 'Consistent easterly winds in tropical regions',
+                        'location': '0-30 degrees latitude',
+                        'characteristics': 'Steady, moderate speed, moisture transport',
+                        'climate_impact': 'Tropical weather patterns, ocean currents'
+                    },
+                    'westerlies': {
+                        'description': 'Prevailing westerly winds in mid-latitudes',
+                        'location': '30-60 degrees latitude',
+                        'characteristics': 'Variable strength, storm track guidance',
+                        'weather_impact': 'Primary storm steering mechanism'
+                    }
+                }
+            },
+            'thermodynamics': {
+                'temperature_processes': {
+                    'radiative_heating': 'Solar radiation warming Earth\'s surface',
+                    'advective_warming': 'Warm air transport from other regions',
+                    'adiabatic_warming': 'Compression warming of descending air',
+                    'latent_heat_release': 'Warming from water vapor condensation'
+                },
+                'cooling_processes': {
+                    'radiative_cooling': 'Heat loss to space, especially at night',
+                    'advective_cooling': 'Cold air transport from other regions',
+                    'adiabatic_cooling': 'Expansion cooling of rising air',
+                    'evaporative_cooling': 'Cooling from water evaporation'
+                },
+                'heat_index': {
+                    'purpose': 'Apparent temperature combining temperature and humidity',
+                    'calculation': 'Complex formula considering human heat stress',
+                    'danger_levels': {
+                        '80-90°F': 'Caution - fatigue possible',
+                        '90-105°F': 'Extreme caution - heat exhaustion possible',
+                        '105-130°F': 'Danger - heat stroke likely',
+                        '130°F+': 'Extreme danger - heat stroke imminent'
+                    }
+                },
+                'wind_chill': {
+                    'purpose': 'Apparent temperature from wind cooling effect',
+                    'mechanism': 'Increased heat loss from exposed skin',
+                    'danger_levels': {
+                        '32-16°F': 'Little danger for properly clothed individuals',
+                        '15-(-5)°F': 'Increasing danger of frostbite',
+                        '(-6)-(-25)°F': 'Great danger of frostbite',
+                        'Below -25°F': 'Extreme danger of frostbite'
+                    }
+                }
+            },
+            'moisture_processes': {
+                'evaporation': {
+                    'process': 'Liquid water to water vapor',
+                    'factors': 'Temperature, humidity, wind speed, surface area',
+                    'energy': 'Requires latent heat, cooling effect'
+                },
+                'condensation': {
+                    'process': 'Water vapor to liquid water',
+                    'requirements': 'Saturation, condensation nuclei',
+                    'energy': 'Releases latent heat, warming effect'
+                },
+                'sublimation': {
+                    'process': 'Ice directly to water vapor',
+                    'conditions': 'Low pressure, dry air',
+                    'examples': 'Snow disappearing without melting'
+                },
+                'deposition': {
+                    'process': 'Water vapor directly to ice',
+                    'conditions': 'Below-freezing surfaces, high humidity',
+                    'examples': 'Frost formation, ice crystal growth'
+                }
+            }
+        }
+    
+    def _build_climate_patterns(self) -> Dict:
+        """Build climate pattern knowledge"""
+        return {
+            'enso': {
+                'el_nino': {
+                    'definition': 'Warming of eastern Pacific sea surface temperatures',
+                    'frequency': 'Every 2-7 years',
+                    'duration': '9-12 months typically',
+                    'global_effects': {
+                        'temperature': 'Generally warmer global temperatures',
+                        'precipitation': 'Increased rainfall in southern US, drier in northern regions',
+                        'storms': 'Reduced Atlantic hurricane activity, increased Pacific activity'
+                    },
+                    'regional_impacts': {
+                        'southwest_us': 'Increased precipitation, cooler temperatures',
+                        'southeast_us': 'Warmer, drier conditions',
+                        'pacific_northwest': 'Warmer, drier winter conditions',
+                        'great_plains': 'Variable impacts, potential for severe weather changes'
+                    }
+                },
+                'la_nina': {
+                    'definition': 'Cooling of eastern Pacific sea surface temperatures',
+                    'frequency': 'Every 2-7 years',
+                    'duration': '9-24 months typically',
+                    'global_effects': {
+                        'temperature': 'Generally cooler global temperatures',
+                        'precipitation': 'Drier conditions in southern US, wetter in northern regions',
+                        'storms': 'Increased Atlantic hurricane activity'
+                    },
+                    'regional_impacts': {
+                        'southwest_us': 'Decreased precipitation, hotter temperatures',
+                        'southeast_us': 'Increased hurricane risk',
+                        'pacific_northwest': 'Cooler, wetter winter conditions',
+                        'great_plains': 'Increased tornado activity, temperature extremes'
+                    }
+                }
+            },
+            'oscillations': {
+                'pdo': {
+                    'name': 'Pacific Decadal Oscillation',
+                    'timescale': '20-30 year cycles',
+                    'impacts': 'Long-term precipitation patterns, marine ecosystems'
+                },
+                'amo': {
+                    'name': 'Atlantic Multidecadal Oscillation',
+                    'timescale': '60-80 year cycles',
+                    'impacts': 'Atlantic hurricane activity, drought patterns'
+                },
+                'nao': {
+                    'name': 'North Atlantic Oscillation',
+                    'timescale': 'Weekly to decadal',
+                    'impacts': 'European and eastern US weather patterns'
+                },
+                'ao': {
+                    'name': 'Arctic Oscillation',
+                    'timescale': 'Weekly to seasonal',
+                    'impacts': 'Northern hemisphere temperature patterns'
+                }
+            },
+            'climate_zones': {
+                'tropical': {
+                    'characteristics': 'High temperatures year-round, distinct wet/dry seasons',
+                    'precipitation': 'Heavy rainfall, monsoon patterns',
+                    'temperature_range': 'Small annual variation, high daily variation'
+                },
+                'subtropical': {
+                    'characteristics': 'Hot summers, mild winters, variable precipitation',
+                    'precipitation': 'Summer-dominant or winter-dominant patterns',
+                    'temperature_range': 'Moderate seasonal variation'
+                },
+                'temperate': {
+                    'characteristics': 'Four distinct seasons, moderate temperatures',
+                    'precipitation': 'Even distribution or winter-dominant',
+                    'temperature_range': 'Large seasonal variation'
+                },
+                'continental': {
+                    'characteristics': 'Large temperature ranges, dry conditions',
+                    'precipitation': 'Summer-dominant, low total amounts',
+                    'temperature_range': 'Extreme seasonal variation'
+                },
+                'polar': {
+                    'characteristics': 'Cold year-round, short summers',
+                    'precipitation': 'Low amounts, mostly snow',
+                    'temperature_range': 'Extreme seasonal variation in daylight'
+                }
+            }
+        }
+    
+    def _build_weather_phenomena(self) -> Dict:
+        """Build weather phenomena knowledge"""
+        return {
+            'thunderstorms': {
+                'formation': {
+                    'requirements': 'Instability, moisture, lifting mechanism',
+                    'types': 'Single-cell, multi-cell, supercell',
+                    'energy_source': 'Latent heat release from condensation'
+                },
+                'hazards': {
+                    'lightning': 'Electrical discharge, fire and electrocution risk',
+                    'hail': 'Ice stones, crop and property damage',
+                    'straight_line_winds': 'Downburst winds, tree and structure damage',
+                    'flooding': 'Heavy rainfall rates exceeding drainage capacity'
+                },
+                'severity_indicators': {
+                    'weak': 'Light rain, occasional thunder, minimal wind',
+                    'moderate': 'Heavy rain, frequent lightning, gusty winds',
+                    'severe': 'Damaging winds >58 mph, hail >1 inch, tornadoes'
+                }
+            },
+            'tornadoes': {
+                'formation': {
+                    'requirements': 'Wind shear, instability, low-level convergence',
+                    'mechanism': 'Rotation from horizontal to vertical orientation',
+                    'supercell_association': 'Most strong tornadoes from supercell thunderstorms'
+                },
+                'classification': {
+                    'ef0': 'Light damage, 65-85 mph winds',
+                    'ef1': 'Moderate damage, 86-110 mph winds',
+                    'ef2': 'Considerable damage, 111-135 mph winds',
+                    'ef3': 'Severe damage, 136-165 mph winds',
+                    'ef4': 'Devastating damage, 166-200 mph winds',
+                    'ef5': 'Incredible damage, >200 mph winds'
+                },
+                'safety': {
+                    'warnings': 'Tornado warning issued when tornado spotted or indicated',
+                    'shelter': 'Lowest floor, interior room, away from windows',
+                    'mobile_homes': 'Evacuate to substantial structure if possible'
+                }
+            },
+            'hurricanes': {
+                'formation': {
+                    'requirements': 'Warm ocean water >80°F, low wind shear, disturbance',
+                    'seasons': 'Atlantic: June-November, Pacific: May-November',
+                    'development_stages': 'Tropical disturbance → depression → storm → hurricane'
+                },
+                'classification': {
+                    'category_1': '74-95 mph, minimal damage',
+                    'category_2': '96-110 mph, moderate damage',
+                    'category_3': '111-129 mph, extensive damage',
+                    'category_4': '130-156 mph, extreme damage',
+                    'category_5': '>157 mph, catastrophic damage'
+                },
+                'hazards': {
+                    'storm_surge': 'Ocean water pushed ashore, primary killer',
+                    'wind': 'Sustained high winds, structural damage',
+                    'flooding': 'Heavy rainfall, inland flooding',
+                    'tornadoes': 'Embedded tornadoes in rainbands'
+                }
+            },
+            'winter_storms': {
+                'types': {
+                    'blizzard': 'Heavy snow, strong winds, low visibility',
+                    'ice_storm': 'Freezing rain, ice accumulation',
+                    'nor_easter': 'Coastal storm, heavy snow/rain, strong winds'
+                },
+                'formation': {
+                    'requirements': 'Cold air, moisture, lifting mechanism',
+                    'temperature_profile': 'Critical for precipitation type',
+                    'storm_tracks': 'Determine snow amounts and areas affected'
+                },
+                'hazards': {
+                    'heavy_snow': 'Transportation disruption, roof collapse',
+                    'ice_accumulation': 'Power outages, tree damage',
+                    'wind_chill': 'Dangerous cold, frostbite risk',
+                    'visibility': 'Whiteout conditions, travel hazards'
+                }
+            }
+        }
+    
+    def _build_seasonal_patterns(self) -> Dict:
+        """Build seasonal weather pattern knowledge"""
+        return {
+            'spring': {
+                'temperature_patterns': {
+                    'characteristics': 'Rapid warming, large daily temperature swings',
+                    'processes': 'Increasing solar angle, longer days',
+                    'variability': 'High day-to-day variability, late cold snaps possible'
+                },
+                'precipitation_patterns': {
+                    'characteristics': 'Increasing thunderstorm activity',
+                    'mechanisms': 'Strong temperature contrasts, jet stream position',
+                    'distribution': 'Often wettest season in many regions'
+                },
+                'severe_weather': {
+                    'peak_season': 'April-June for tornadoes',
+                    'reasons': 'Strong temperature contrasts, wind shear',
+                    'regions': 'Great Plains, Southeast most active'
+                }
+            },
+            'summer': {
+                'temperature_patterns': {
+                    'characteristics': 'Peak heat, least daily variation',
+                    'processes': 'Maximum solar angle, longest days',
+                    'extremes': 'Heat waves, urban heat island effects'
+                },
+                'precipitation_patterns': {
+                    'characteristics': 'Convective thunderstorms, monsoons',
+                    'timing': 'Afternoon/evening thunderstorm peaks',
+                    'distribution': 'Heavy rainfall in short periods'
+                },
+                'circulation_patterns': {
+                    'bermuda_high': 'Dominant high pressure over Atlantic',
+                    'monsoon': 'Seasonal wind reversal, moisture transport',
+                    'heat_domes': 'Persistent high pressure, extreme heat'
+                }
+            },
+            'fall': {
+                'temperature_patterns': {
+                    'characteristics': 'Gradual cooling, first frost',
+                    'processes': 'Decreasing solar angle, shorter days',
+                    'variability': 'Indian summer periods, early cold snaps'
+                },
+                'precipitation_patterns': {
+                    'characteristics': 'Transitional weather patterns',
+                    'systems': 'Early winter storms, tropical systems',
+                    'distribution': 'More organized storm systems'
+                },
+                'transition_indicators': {
+                    'first_frost': 'End of growing season',
+                    'leaf_change': 'Temperature and daylight response',
+                    'storm_patterns': 'Shift to winter-type systems'
+                }
+            },
+            'winter': {
+                'temperature_patterns': {
+                    'characteristics': 'Coldest temperatures, arctic air masses',
+                    'processes': 'Minimum solar angle, shortest days',
+                    'extremes': 'Polar vortex events, arctic blasts'
+                },
+                'precipitation_patterns': {
+                    'characteristics': 'Snow in northern regions, winter storms',
+                    'storm_types': 'Nor\'easters, alberta clippers, pacific storms',
+                    'temperature_dependence': 'Snow vs. rain based on temperature profile'
+                },
+                'circulation_patterns': {
+                    'polar_vortex': 'Arctic circulation, occasional breakdowns',
+                    'storm_tracks': 'More southern storm tracks',
+                    'blocking_patterns': 'Persistent weather patterns'
+                }
+            }
+        }
+    
+    def _build_regional_climatology(self) -> Dict:
+        """Build regional climate knowledge"""
+        return {
+            'pacific_northwest': {
+                'climate_type': 'Marine West Coast',
+                'temperature_regime': 'Mild temperatures, small annual range',
+                'precipitation_regime': 'Wet winters, dry summers',
+                'dominant_features': {
+                    'marine_influence': 'Pacific Ocean moderates temperatures',
+                    'orographic_effects': 'Mountains enhance precipitation',
+                    'rain_shadow': 'Eastern areas much drier'
+                },
+                'seasonal_characteristics': {
+                    'winter': 'Frequent rain, mild temperatures, storms from Pacific',
+                    'spring': 'Gradual drying, wildflower season',
+                    'summer': 'Dry, pleasant, occasional heat waves',
+                    'fall': 'Return of rain, beautiful foliage'
+                }
+            },
+            'southwest_desert': {
+                'climate_type': 'Hot Desert',
+                'temperature_regime': 'Hot summers, mild winters, large daily range',
+                'precipitation_regime': 'Very low precipitation, summer monsoon',
+                'dominant_features': {
+                    'aridity': 'Low humidity, high evaporation rates',
+                    'temperature_extremes': 'Very hot days, cool nights',
+                    'monsoon_season': 'July-September moisture from Gulf of Mexico'
+                },
+                'seasonal_characteristics': {
+                    'winter': 'Mild, pleasant, occasional rain',
+                    'spring': 'Warm, dry, windy',
+                    'summer': 'Extreme heat, monsoon thunderstorms',
+                    'fall': 'Gradual cooling, pleasant'
+                }
+            },
+            'great_plains': {
+                'climate_type': 'Continental',
+                'temperature_regime': 'Large annual range, hot summers, cold winters',
+                'precipitation_regime': 'Moderate precipitation, summer maximum',
+                'dominant_features': {
+                    'continental_influence': 'Large temperature swings',
+                    'severe_weather': 'Tornado alley, severe thunderstorms',
+                    'drought_prone': 'Variable precipitation, drought cycles'
+                },
+                'seasonal_characteristics': {
+                    'winter': 'Cold, dry, occasional blizzards',
+                    'spring': 'Severe weather season, rapid warming',
+                    'summer': 'Hot, humid, thunderstorms',
+                    'fall': 'Pleasant, variable'
+                }
+            }
+        }
+    
+    def _build_extreme_weather_knowledge(self) -> Dict:
+        """Build extreme weather event knowledge"""
+        return {
+            'heat_waves': {
+                'definition': 'Period of excessively hot weather',
+                'criteria': 'Temperature >90°F for 2+ consecutive days',
+                'formation': 'High pressure ridge, subsidence, clear skies',
+                'health_impacts': {
+                    'heat_exhaustion': 'Heavy sweating, weakness, nausea',
+                    'heat_stroke': 'Body temperature >103°F, organ failure risk',
+                    'vulnerable_populations': 'Elderly, children, chronic health conditions'
+                },
+                'mitigation': {
+                    'cooling_centers': 'Public facilities with air conditioning',
+                    'hydration': 'Increased fluid intake, avoid alcohol',
+                    'activity_modification': 'Limit outdoor activities during peak heat'
+                }
+            },
+            'cold_waves': {
+                'definition': 'Period of excessively cold weather',
+                'criteria': 'Temperature significantly below normal for 2+ days',
+                'formation': 'Arctic air mass, polar vortex displacement',
+                'health_impacts': {
+                    'hypothermia': 'Body temperature <95°F, organ failure risk',
+                    'frostbite': 'Tissue freezing, permanent damage possible',
+                    'increased_mortality': 'Heart attacks, accidents, carbon monoxide'
+                },
+                'mitigation': {
+                    'heating': 'Adequate home heating, generator safety',
+                    'clothing': 'Layered clothing, wind protection',
+                    'travel': 'Emergency kits, avoid unnecessary travel'
+                }
+            },
+            'drought': {
+                'types': {
+                    'meteorological': 'Below-normal precipitation',
+                    'agricultural': 'Soil moisture deficiency',
+                    'hydrological': 'Reduced water supply',
+                    'socioeconomic': 'Water shortage affecting human activities'
+                },
+                'impacts': {
+                    'agriculture': 'Crop failure, livestock stress',
+                    'water_supply': 'Reservoir depletion, well failure',
+                    'environment': 'Wildfire risk, ecosystem stress',
+                    'economy': 'Agricultural losses, increased costs'
+                }
+            },
+            'flooding': {
+                'types': {
+                    'flash_flood': 'Rapid onset, small watersheds',
+                    'river_flood': 'Gradual onset, large watersheds',
+                    'coastal_flood': 'Storm surge, high tides',
+                    'urban_flood': 'Overwhelmed drainage systems'
+                },
+                'causes': {
+                    'heavy_rainfall': 'Intense precipitation rates',
+                    'snowmelt': 'Rapid snow and ice melt',
+                    'dam_failure': 'Infrastructure failure',
+                    'storm_surge': 'Hurricane/storm-driven water'
+                },
+                'safety': {
+                    'awareness': 'Never drive through flooded roads',
+                    'evacuation': 'Move to higher ground immediately',
+                    'preparation': 'Emergency kits, evacuation plans'
+                }
+            }
+        }
+    
+    def get_phenomenon_explanation(self, phenomenon: str) -> Optional[Dict]:
+        """Get detailed explanation of weather phenomenon"""
+        phenomenon_lower = phenomenon.lower().replace(' ', '_')
+        
+        # Search through all knowledge bases
+        for knowledge_base in [self.meteorological_knowledge, self.climate_patterns, 
+                              self.weather_phenomena, self.extreme_weather_knowledge]:
+            explanation = self._search_knowledge_base(knowledge_base, phenomenon_lower)
+            if explanation:
+                return explanation
+        
+        return None
+    
+    def get_seasonal_context(self, season: str, month: Optional[int] = None) -> Dict:
+        """Get seasonal weather context"""
+        if season.lower() in self.seasonal_patterns:
+            context = self.seasonal_patterns[season.lower()].copy()
+            
+            if month:
+                # Add month-specific details
+                context['month_specific'] = self._get_month_specific_info(season, month)
+            
+            return context
+        
+        return {}
+    
+    def get_regional_climatology(self, region: str) -> Dict:
+        """Get regional climate information"""
+        region_lower = region.lower().replace(' ', '_')
+        return self.regional_climatology.get(region_lower, {})
+    
+    def get_weather_relationship_explanation(self, variables: List[str]) -> Optional[str]:
+        """Get explanation of weather variable relationships"""
+        # Check for known relationships
+        for relationship_key, explanation in self.meteorological_knowledge.get('thermodynamics', {}).items():
+            if any(var.lower() in relationship_key for var in variables):
+                return explanation
+        
+        return None
+    
+    def _search_knowledge_base(self, knowledge_base: Dict, search_term: str) -> Optional[Dict]:
+        """Recursively search knowledge base for term"""
+        if isinstance(knowledge_base, dict):
+            for key, value in knowledge_base.items():
+                if search_term in key.lower():
+                    return {key: value}
+                elif isinstance(value, dict):
+                    result = self._search_knowledge_base(value, search_term)
+                    if result:
+                        return result
+        
+        return None
+    
+    def _get_month_specific_info(self, season: str, month: int) -> Dict:
+        """Get month-specific seasonal information"""
+        month_info = {
+            'spring': {
+                3: {'phase': 'early', 'characteristics': 'Spring equinox, rapid warming begins'},
+                4: {'phase': 'mid', 'characteristics': 'Peak severe weather season'},
+                5: {'phase': 'late', 'characteristics': 'Transition to summer patterns'}
+            },
+            'summer': {
+                6: {'phase': 'early', 'characteristics': 'Summer solstice, heat building'},
+                7: {'phase': 'mid', 'characteristics': 'Peak heat, monsoon season'},
+                8: {'phase': 'late', 'characteristics': 'Hurricane season peak'}
+            },
+            'fall': {
+                9: {'phase': 'early', 'characteristics': 'Autumn equinox, cooling begins'},
+                10: {'phase': 'mid', 'characteristics': 'Peak fall foliage, first frost'},
+                11: {'phase': 'late', 'characteristics': 'Winter transition, storm season'}
+            },
+            'winter': {
+                12: {'phase': 'early', 'characteristics': 'Winter solstice, cold deepening'},
+                1: {'phase': 'mid', 'characteristics': 'Coldest period, arctic air masses'},
+                2: {'phase': 'late', 'characteristics': 'Daylight increasing, spring transition'}
+            }
+        }
+        
+        return month_info.get(season.lower(), {}).get(month, {})
+
+def create_weather_knowledge_base() -> WeatherKnowledgeBase:
+    """Factory function to create weather knowledge base"""
+    return WeatherKnowledgeBase()

src/geovisor/map_manager.py

@@ -0,0 +1,667 @@
+"""
+Intelligent Map Management System
+Dynamic zoom, markers, and weather layer visualization
+"""
+
+import folium
+import json
+from typing import List, Dict, Tuple, Optional
+import logging
+import math
+import re
+import re
+
+logger = logging.getLogger(__name__)
+
+class WeatherMapManager:
+    """Advanced map management with intelligent features"""
+    
+    def __init__(self):
+        self.default_center = (39.8283, -98.5795)  # Geographic center of US
+        self.default_zoom = 4
+        
+        # Weather condition to icon mapping
+        self.weather_icons = {
+            # Sunny/Clear conditions
+            'sunny': {'icon': 'sun', 'color': 'orange'},
+            'clear': {'icon': 'sun', 'color': 'orange'},
+            'fair': {'icon': 'sun', 'color': 'orange'},
+            'hot': {'icon': 'sun', 'color': 'red'},
+            'warm': {'icon': 'sun', 'color': 'orange'},
+            
+            # Cloudy conditions
+            'cloudy': {'icon': 'cloud', 'color': 'gray'},
+            'partly cloudy': {'icon': 'cloud-sun', 'color': 'lightblue'},
+            'mostly cloudy': {'icon': 'cloud', 'color': 'gray'},
+            'overcast': {'icon': 'cloud', 'color': 'darkgray'},
+            'partly sunny': {'icon': 'cloud-sun', 'color': 'lightblue'},
+            'mostly sunny': {'icon': 'cloud-sun', 'color': 'orange'},
+            
+            # Rainy conditions
+            'rainy': {'icon': 'cloud-rain', 'color': 'blue'},
+            'rain': {'icon': 'cloud-rain', 'color': 'blue'},
+            'light rain': {'icon': 'cloud-rain', 'color': 'lightblue'},
+            'heavy rain': {'icon': 'cloud-rain', 'color': 'darkblue'},
+            'drizzle': {'icon': 'cloud-drizzle', 'color': 'lightblue'},
+            'showers': {'icon': 'cloud-showers-heavy', 'color': 'blue'},
+            'scattered showers': {'icon': 'cloud-rain', 'color': 'blue'},
+            'isolated showers': {'icon': 'cloud-rain', 'color': 'lightblue'},
+            
+            # Stormy conditions
+            'thunderstorm': {'icon': 'bolt', 'color': 'purple'},
+            'thunderstorms': {'icon': 'bolt', 'color': 'purple'},
+            'severe thunderstorms': {'icon': 'bolt', 'color': 'darkred'},
+            'storm': {'icon': 'bolt', 'color': 'purple'},
+            'storms': {'icon': 'bolt', 'color': 'purple'},
+            'lightning': {'icon': 'bolt', 'color': 'purple'},
+            
+            # Snow conditions
+            'snow': {'icon': 'snowflake', 'color': 'white'},
+            'snowy': {'icon': 'snowflake', 'color': 'white'},
+            'light snow': {'icon': 'snowflake', 'color': 'lightgray'},
+            'heavy snow': {'icon': 'snowflake', 'color': 'gray'},
+            'snow showers': {'icon': 'snowflake', 'color': 'lightgray'},
+            'blizzard': {'icon': 'snowflake', 'color': 'darkgray'},
+            'flurries': {'icon': 'snowflake', 'color': 'lightgray'},
+            
+            # Windy conditions
+            'windy': {'icon': 'wind', 'color': 'green'},
+            'breezy': {'icon': 'wind', 'color': 'lightgreen'},
+            'gusty': {'icon': 'wind', 'color': 'green'},
+            
+            # Foggy/Misty conditions
+            'fog': {'icon': 'smog', 'color': 'gray'},
+            'foggy': {'icon': 'smog', 'color': 'gray'},
+            'mist': {'icon': 'smog', 'color': 'lightgray'},
+            'misty': {'icon': 'smog', 'color': 'lightgray'},
+            'haze': {'icon': 'smog', 'color': 'gray'},
+            'hazy': {'icon': 'smog', 'color': 'gray'},
+            
+            # Mixed conditions
+            'rain and snow': {'icon': 'cloud-rain', 'color': 'blue'},
+            'wintry mix': {'icon': 'snowflake', 'color': 'lightblue'},
+            'sleet': {'icon': 'snowflake', 'color': 'lightblue'},
+            'freezing rain': {'icon': 'icicles', 'color': 'lightblue'},
+            'ice': {'icon': 'icicles', 'color': 'lightblue'},
+            'icy': {'icon': 'icicles', 'color': 'lightblue'},
+            
+            # Temperature extremes
+            'cold': {'icon': 'thermometer-quarter', 'color': 'blue'},
+            'freezing': {'icon': 'thermometer-empty', 'color': 'lightblue'},
+            'cool': {'icon': 'thermometer-half', 'color': 'lightblue'},
+            
+            # Default fallback
+            'default': {'icon': 'cloud', 'color': 'blue'}
+        }
+    
+    def _get_weather_icon(self, weather_condition: str) -> Dict[str, str]:
+        """Determine appropriate icon and color based on weather condition"""
+        if not weather_condition:
+            return self.weather_icons['default']
+        
+        # Convert to lowercase for matching
+        condition_lower = weather_condition.lower()
+        
+        # Direct match first
+        if condition_lower in self.weather_icons:
+            return self.weather_icons[condition_lower]
+        
+        # Partial matching for complex descriptions
+        for key in self.weather_icons:
+            if key in condition_lower:
+                return self.weather_icons[key]
+        
+        # Temperature-based fallback
+        if any(term in condition_lower for term in ['hot', 'warm', 'sunny', 'clear']):
+            return self.weather_icons['sunny']
+        elif any(term in condition_lower for term in ['rain', 'shower', 'drizzle']):
+            return self.weather_icons['rainy']
+        elif any(term in condition_lower for term in ['storm', 'thunder', 'lightning']):
+            return self.weather_icons['thunderstorm']
+        elif any(term in condition_lower for term in ['snow', 'blizzard', 'flurries']):
+            return self.weather_icons['snow']
+        elif any(term in condition_lower for term in ['cloud', 'overcast']):
+            return self.weather_icons['cloudy']
+        elif any(term in condition_lower for term in ['fog', 'mist', 'haze']):
+            return self.weather_icons['fog']
+        elif any(term in condition_lower for term in ['wind', 'breezy', 'gusty']):
+            return self.weather_icons['windy']
+        
+        # Final fallback
+        return self.weather_icons['default']
+    
+    def _get_temperature_icon_enhancement(self, temperature: Optional[int]) -> Dict[str, str]:
+        """Get additional icon styling based on temperature"""
+        if temperature is None:
+            return {}
+        
+        # Temperature-based color adjustments
+        if temperature >= 90:
+            return {'color': 'red'}
+        elif temperature >= 80:
+            return {'color': 'orange'}
+        elif temperature >= 70:
+            return {'color': 'green'}
+        elif temperature >= 60:
+            return {'color': 'lightgreen'}
+        elif temperature >= 50:
+            return {'color': 'lightblue'}
+        elif temperature >= 40:
+            return {'color': 'blue'}
+        elif temperature >= 32:
+            return {'color': 'purple'}
+        else:
+            return {'color': 'darkblue'}
+        
+    def _get_weather_emoji(self, weather_condition: str) -> str:
+        """Get appropriate emoji for weather condition"""
+        if not weather_condition:
+            return "🌤️"
+        
+        condition_lower = weather_condition.lower()
+        
+        # Direct emoji mapping
+        emoji_map = {
+            'sunny': '☀️', 'clear': '☀️', 'fair': '🌤️', 'hot': '🌡️',
+            'cloudy': '☁️', 'partly cloudy': '⛅', 'mostly cloudy': '☁️', 'overcast': '☁️',
+            'partly sunny': '⛅', 'mostly sunny': '🌤️',
+            'rainy': '🌧️', 'rain': '🌧️', 'light rain': '🌦️', 'heavy rain': '🌧️',
+            'drizzle': '🌦️', 'showers': '🌦️', 'scattered showers': '🌦️',
+            'thunderstorm': '⛈️', 'thunderstorms': '⛈️', 'storm': '⛈️', 'storms': '⛈️',
+            'snow': '❄️', 'snowy': '❄️', 'light snow': '🌨️', 'heavy snow': '❄️',
+            'snow showers': '🌨️', 'blizzard': '🌨️', 'flurries': '🌨️',
+            'windy': '💨', 'breezy': '🍃', 'gusty': '💨',
+            'fog': '🌫️', 'foggy': '🌫️', 'mist': '🌫️', 'misty': '🌫️', 'haze': '🌫️',
+            'freezing rain': '🧊', 'sleet': '🧊', 'ice': '🧊', 'icy': '🧊'
+        }
+        
+        # Direct match
+        if condition_lower in emoji_map:
+            return emoji_map[condition_lower]
+        
+        # Partial matching
+        for key, emoji in emoji_map.items():
+            if key in condition_lower:
+                return emoji
+        
+        return "🌤️"  # Default emoji
+    
+    def calculate_optimal_bounds(self, coordinates: List[Tuple[float, float]]) -> Dict:
+        """Calculate optimal map bounds for given coordinates"""
+        if not coordinates:
+            return {'center': self.default_center, 'zoom': self.default_zoom}
+        
+        if len(coordinates) == 1:
+            return {'center': coordinates[0], 'zoom': 10}
+        
+        # Calculate center point
+        lats = [coord[0] for coord in coordinates]
+        lons = [coord[1] for coord in coordinates]
+        
+        center_lat = sum(lats) / len(lats)
+        center_lon = sum(lons) / len(lons)
+        
+        # Calculate zoom based on coordinate spread
+        lat_range = max(lats) - min(lats)
+        lon_range = max(lons) - min(lons)
+        max_range = max(lat_range, lon_range)
+        
+        # Determine appropriate zoom level
+        if max_range < 0.5:
+            zoom = 11
+        elif max_range < 1:
+            zoom = 9
+        elif max_range < 3:
+            zoom = 8
+        elif max_range < 5:
+            zoom = 7
+        elif max_range < 10:
+            zoom = 6
+        elif max_range < 20:
+            zoom = 5
+        else:
+            zoom = 4
+        
+        return {'center': (center_lat, center_lon), 'zoom': zoom}
+    
+    def create_weather_map(self, cities_data: List[Dict], comparison_mode: bool = False, 
+                          show_weather_layers: bool = True) -> str:
+        """Create comprehensive weather map with all features"""
+        try:
+            # Calculate optimal view
+            if cities_data:
+                coordinates = [(city['lat'], city['lon']) for city in cities_data]
+                bounds = self.calculate_optimal_bounds(coordinates)
+            else:
+                bounds = {'center': self.default_center, 'zoom': self.default_zoom}
+            
+            # Create base map with street map as default
+            m = folium.Map(
+                location=bounds['center'],
+                zoom_start=bounds['zoom'],
+                tiles='OpenStreetMap',
+                attr='OpenStreetMap'
+            )
+            
+            # Add alternative tile layers
+            folium.TileLayer(
+                'CartoDB dark_matter',
+                name='Dark Theme',
+                overlay=False,
+                control=True
+            ).add_to(m)
+            
+            folium.TileLayer(
+                'CartoDB positron',
+                name='Light Theme',
+                overlay=False,
+                control=True
+            ).add_to(m)
+            
+            # Add weather layers if requested
+            if show_weather_layers:
+                self._add_weather_layers(m)
+            
+            # Add city markers
+            if cities_data:
+                self._add_city_markers(m, cities_data)
+            
+            # Add comparison features
+            if comparison_mode and len(cities_data) > 1:
+                self._add_comparison_features(m, cities_data)
+            
+            # Add map controls
+            folium.LayerControl().add_to(m)
+            
+            # Add fullscreen button
+            from folium.plugins import Fullscreen
+            Fullscreen().add_to(m)
+            
+            return m._repr_html_()
+            
+        except Exception as e:
+            logger.error(f"Error creating weather map: {e}")
+            return self._create_error_map(str(e))
+    
+    def _add_weather_layers(self, map_obj: folium.Map):
+        """Add weather overlay layers"""
+        try:
+            # Precipitation radar layer
+            precipitation_layer = folium.raster_layers.WmsTileLayer(
+                url='https://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi',
+                layers='nexrad-n0r-900913',
+                name='Precipitation Radar',
+                overlay=True,
+                control=True,
+                transparent=True,
+                format='image/png',
+                opacity=0.6
+            )
+            precipitation_layer.add_to(map_obj)
+            
+            # Temperature layer (simplified)
+            # Note: In production, you'd use actual weather service WMS layers
+            
+        except Exception as e:
+            logger.warning(f"Could not add weather layers: {e}")
+    
+    def _add_city_markers(self, map_obj: folium.Map, cities_data: List[Dict]):
+        """Add markers for cities with weather information"""
+        for i, city_data in enumerate(cities_data):
+            # Get weather condition and temperature from forecast
+            forecast = city_data.get('forecast', [])
+            current_weather = forecast[0] if forecast else {}
+            weather_condition = current_weather.get('shortForecast', '')
+            temperature = current_weather.get('temperature')
+            
+            # Determine weather-appropriate icon
+            weather_icon_info = self._get_weather_icon(weather_condition)
+            icon_name = weather_icon_info['icon']
+            icon_color = weather_icon_info['color']
+            
+            # Apply temperature-based color enhancement if available
+            if temperature:
+                temp_enhancement = self._get_temperature_icon_enhancement(temperature)
+                if temp_enhancement.get('color'):
+                    icon_color = temp_enhancement['color']
+            
+            # Create weather condition tooltip
+            weather_tooltip = f"📍 {city_data['name'].title()}"
+            if weather_condition:
+                weather_tooltip += f" - {weather_condition}"
+            if temperature:
+                weather_tooltip += f" ({temperature}°F)"
+            weather_tooltip += " - Click for details"
+            
+            # Create detailed popup
+            popup_html = self._create_popup_html(city_data)
+            
+            # Add main marker with weather-appropriate icon
+            folium.Marker(
+                location=[city_data['lat'], city_data['lon']],
+                popup=folium.Popup(popup_html, max_width=350),
+                tooltip=weather_tooltip,
+                icon=folium.Icon(
+                    color=icon_color, 
+                    icon=icon_name, 
+                    prefix='fa'
+                )
+            ).add_to(map_obj)
+            
+            # Add wind flow arrow if wind data is available
+            wind_speed = current_weather.get('windSpeed')
+            wind_direction = current_weather.get('windDirection')
+            if wind_speed and wind_direction:
+                self._add_wind_arrow(map_obj, city_data['lat'], city_data['lon'], 
+                                   wind_speed, wind_direction)
+            
+            # Add weather circle indicator
+            if city_data.get('forecast'):
+                current_temp = city_data['forecast'][0].get('temperature', 0)
+                
+                # Color code temperature
+                if current_temp > 80:
+                    circle_color = 'red'
+                elif current_temp > 60:
+                    circle_color = 'orange'
+                elif current_temp > 40:
+                    circle_color = 'yellow'
+                else:
+                    circle_color = 'blue'
+                
+                folium.Circle(
+                    location=[city_data['lat'], city_data['lon']],
+                    radius=30000,  # 30km radius
+                    popup=f"Temperature Zone: {current_temp}°F",
+                    color=circle_color,
+                    fill=True,
+                    fillOpacity=0.2,
+                    weight=2
+                ).add_to(map_obj)
+                
+            # Add wind arrow if wind data is available
+            if current_weather.get('windSpeed') and current_weather.get('windDirection'):
+                self._add_wind_arrow(map_obj, city_data['lat'], city_data['lon'], 
+                                     current_weather['windSpeed'], current_weather['windDirection'])
+    
+    def _add_comparison_features(self, map_obj: folium.Map, cities_data: List[Dict]):
+        """Add comparison lines and features between cities"""
+        if len(cities_data) < 2:
+            return
+        
+        # Add connection lines between all cities
+        coordinates = [[city['lat'], city['lon']] for city in cities_data]
+        
+        # Create comparison route
+        folium.PolyLine(
+            coordinates,
+            color='yellow',
+            weight=4,
+            opacity=0.8,
+            popup="Weather Comparison Route",
+            tooltip="Cities being compared"
+        ).add_to(map_obj)
+        
+        # Add midpoint marker for comparison summary
+        if len(cities_data) == 2:
+            city1, city2 = cities_data[0], cities_data[1]
+            mid_lat = (city1['lat'] + city2['lat']) / 2
+            mid_lon = (city1['lon'] + city2['lon']) / 2
+            
+            comparison_summary = self._create_comparison_summary(city1, city2)
+            
+            folium.Marker(
+                location=[mid_lat, mid_lon],
+                popup=folium.Popup(comparison_summary, max_width=400),
+                tooltip="Comparison Summary",
+                icon=folium.Icon(
+                    color='lightblue',
+                    icon='balance-scale',
+                    prefix='fa'
+                )
+            ).add_to(map_obj)
+    
+    def _create_popup_html(self, city_data: Dict) -> str:
+        """Create detailed HTML popup for city marker"""
+        forecast = city_data.get('forecast', [])
+        if not forecast:
+            return f"""
+            <div style="width: 300px; font-family: Arial, sans-serif;">
+                <h3 style="margin: 0; color: #2c3e50;">📍 {city_data['name'].title()}</h3>
+                <p>No weather data available</p>
+            </div>
+            """
+        
+        current = forecast[0]
+        next_period = forecast[1] if len(forecast) > 1 else {}
+        
+        html = f"""
+        <div style="width: 320px; font-family: Arial, sans-serif; line-height: 1.4;">
+            <h3 style="margin: 0 0 10px 0; color: #2c3e50; border-bottom: 2px solid #3498db; padding-bottom: 5px;">
+                📍 {city_data['name'].title()}
+            </h3>
+            
+            <div style="background: #f8f9fa; padding: 10px; border-radius: 5px; margin-bottom: 10px;">
+                <h4 style="margin: 0 0 8px 0; color: #e74c3c;">🌡️ Current Conditions</h4>
+                <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 5px; font-size: 13px;">
+                    <div><strong>Temperature:</strong></div>
+                    <div>{current.get('temperature', 'N/A')}°{current.get('temperatureUnit', 'F')}</div>
+                    
+                    <div><strong>Conditions:</strong></div>
+                    <div>{current.get('shortForecast', 'N/A')}</div>
+                    
+                    <div><strong>Wind:</strong></div>
+                    <div>{current.get('windSpeed', 'N/A')} {current.get('windDirection', '')}</div>
+                    
+                    <div><strong>Rain Chance:</strong></div>
+                    <div>{current.get('precipitationProbability', 0)}%</div>
+                </div>
+            </div>
+            
+            <div style="background: #e8f4f8; padding: 10px; border-radius: 5px; margin-bottom: 10px;">
+                <h4 style="margin: 0 0 8px 0; color: #2980b9;">📅 Next Period</h4>
+                <div style="font-size: 13px;">
+                    <strong>{next_period.get('name', 'N/A')}:</strong><br>
+                    {next_period.get('temperature', 'N/A')}°{next_period.get('temperatureUnit', 'F')} - 
+                    {next_period.get('shortForecast', 'N/A')}
+                </div>
+            </div>
+            
+            <div style="background: #fff3cd; padding: 8px; border-radius: 5px; font-size: 12px;">
+                <strong>📝 Details:</strong><br>
+                {current.get('detailedForecast', 'No detailed forecast available')[:150]}...
+            </div>
+            
+            <div style="text-align: center; margin-top: 10px; font-size: 11px; color: #6c757d;">
+                📊 Coordinates: {city_data['lat']:.3f}°, {city_data['lon']:.3f}°
+            </div>
+        </div>
+        """
+        
+        return html
+    
+    def _create_comparison_summary(self, city1: Dict, city2: Dict) -> str:
+        """Create comparison summary popup"""
+        name1, name2 = city1['name'].title(), city2['name'].title()
+        forecast1 = city1.get('forecast', [{}])[0]
+        forecast2 = city2.get('forecast', [{}])[0]
+        
+        temp1 = forecast1.get('temperature', 0)
+        temp2 = forecast2.get('temperature', 0)
+        temp_diff = abs(temp1 - temp2)
+        warmer_city = name1 if temp1 > temp2 else name2
+        
+        rain1 = forecast1.get('precipitationProbability', 0)
+        rain2 = forecast2.get('precipitationProbability', 0)
+        rain_diff = abs(rain1 - rain2)
+        rainier_city = name1 if rain1 > rain2 else name2
+        
+        html = f"""
+        <div style="width: 350px; font-family: Arial, sans-serif;">
+            <h3 style="margin: 0 0 15px 0; color: #2c3e50; text-align: center; border-bottom: 2px solid #f39c12; padding-bottom: 8px;">
+                ⚖️ Weather Comparison
+            </h3>
+            
+            <div style="background: linear-gradient(135deg, #667eea, #764ba2); color: white; padding: 12px; border-radius: 8px; margin-bottom: 15px;">
+                <h4 style="margin: 0 0 10px 0; text-align: center;">🌡️ Temperature Comparison</h4>
+                <div style="display: grid; grid-template-columns: 1fr auto 1fr; gap: 10px; align-items: center;">
+                    <div style="text-align: center;">
+                        <div style="font-weight: bold;">{name1}</div>
+                        <div style="font-size: 18px;">{temp1}°F</div>
+                    </div>
+                    <div style="text-align: center; font-size: 20px;">VS</div>
+                    <div style="text-align: center;">
+                        <div style="font-weight: bold;">{name2}</div>
+                        <div style="font-size: 18px;">{temp2}°F</div>
+                    </div>
+                </div>
+                <div style="text-align: center; margin-top: 10px; font-size: 14px;">
+                    <strong>{warmer_city}</strong> is {temp_diff}°F warmer
+                </div>
+            </div>
+            
+            <div style="background: linear-gradient(135deg, #4ecdc4, #44a08d); color: white; padding: 12px; border-radius: 8px; margin-bottom: 15px;">
+                <h4 style="margin: 0 0 10px 0; text-align: center;">🌧️ Precipitation Comparison</h4>
+                <div style="display: grid; grid-template-columns: 1fr auto 1fr; gap: 10px; align-items: center;">
+                    <div style="text-align: center;">
+                        <div style="font-weight: bold;">{name1}</div>
+                        <div style="font-size: 18px;">{rain1}%</div>
+                    </div>
+                    <div style="text-align: center; font-size: 20px;">VS</div>
+                    <div style="text-align: center;">
+                        <div style="font-weight: bold;">{name2}</div>
+                        <div style="font-size: 18px;">{rain2}%</div>
+                    </div>
+                </div>
+                <div style="text-align: center; margin-top: 10px; font-size: 14px;">
+                    <strong>{rainier_city}</strong> has {rain_diff}% higher chance
+                </div>
+            </div>
+            
+            <div style="background: #f8f9fa; padding: 10px; border-radius: 5px; font-size: 13px; text-align: center;">
+                <strong>📏 Distance:</strong> {self._calculate_distance(city1['lat'], city1['lon'], city2['lat'], city2['lon']):.0f} miles
+            </div>
+        </div>
+        """
+        
+        return html
+    
+    def _calculate_distance(self, lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+        """Calculate distance between two points using Haversine formula"""
+        R = 3959  # Earth's radius in miles
+        
+        lat1_rad = math.radians(lat1)
+        lat2_rad = math.radians(lat2)
+        delta_lat = math.radians(lat2 - lat1)
+        delta_lon = math.radians(lon2 - lon1)
+        
+        a = (math.sin(delta_lat / 2) ** 2 + 
+             math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(delta_lon / 2) ** 2)
+        c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
+        
+        return R * c
+    
+    def _create_error_map(self, error_message: str) -> str:
+        """Create error map when main map creation fails"""
+        try:
+            m = folium.Map(
+                location=self.default_center,
+                zoom_start=self.default_zoom,
+                tiles='CartoDB dark_matter'
+            )
+            
+            folium.Marker(
+                location=self.default_center,
+                popup=f"Error: {error_message}",
+                tooltip="Map Error",
+                icon=folium.Icon(color='red', icon='exclamation-triangle', prefix='fa')
+            ).add_to(m)
+            
+            return m._repr_html_()
+        except:
+            return f"""
+            <div style="width: 100%; height: 400px; background: #2c3e50; color: white; 
+                        display: flex; align-items: center; justify-content: center; 
+                        font-family: Arial, sans-serif; border-radius: 10px;">
+                <div style="text-align: center;">
+                    <h3>🗺️ Map Error</h3>
+                    <p>Unable to load map: {error_message}</p>
+                </div>
+            </div>
+            """
+
+    def _add_wind_arrow(self, map_obj: folium.Map, lat: float, lon: float, 
+                       wind_speed: str, wind_direction: str) -> None:
+        """Add wind flow arrow to the map"""
+        try:
+            # Parse wind speed and direction
+            if not wind_speed or not wind_direction or wind_speed == 'N/A':
+                return
+            
+            # Extract numeric wind speed
+            speed_match = re.search(r'(\d+)', str(wind_speed))
+            if not speed_match:
+                return
+            speed = int(speed_match.group(1))
+            
+            # Convert wind direction to degrees
+            direction_map = {
+                'N': 0, 'NNE': 22.5, 'NE': 45, 'ENE': 67.5,
+                'E': 90, 'ESE': 112.5, 'SE': 135, 'SSE': 157.5,
+                'S': 180, 'SSW': 202.5, 'SW': 225, 'WSW': 247.5,
+                'W': 270, 'WNW': 292.5, 'NW': 315, 'NNW': 337.5
+            }
+            
+            direction_deg = direction_map.get(wind_direction.upper(), 0)
+            
+            # Calculate wind arrow properties
+            arrow_length = min(max(speed * 0.001, 0.01), 0.05)  # Scale arrow length
+            arrow_color = self._get_wind_arrow_color(speed)
+            
+            # Calculate arrow endpoint
+            import math
+            # Convert to radians and adjust for map orientation (wind direction is "from")
+            rad = math.radians(direction_deg + 180)  # +180 because wind direction is "from"
+            end_lat = lat + arrow_length * math.cos(rad)
+            end_lon = lon + arrow_length * math.sin(rad)
+            
+            # Create wind arrow as a polyline with arrowhead
+            folium.PolyLine(
+                locations=[(lat, lon), (end_lat, end_lon)],
+                color=arrow_color,
+                weight=4,
+                opacity=0.8,
+                popup=f"Wind: {wind_speed} from {wind_direction}",
+                tooltip=f"💨 {wind_speed} {wind_direction}"
+            ).add_to(map_obj)
+            
+            # Add arrowhead marker
+            folium.Marker(
+                location=[end_lat, end_lon],
+                icon=folium.Icon(
+                    icon='arrow-up',
+                    prefix='fa',
+                    color=arrow_color,
+                    icon_size=(10, 10)
+                ),
+                popup=f"Wind: {wind_speed} from {wind_direction}"
+            ).add_to(map_obj)
+            
+        except Exception as e:
+            logger.warning(f"Could not add wind arrow: {e}")
+    
+    def _get_wind_arrow_color(self, speed: int) -> str:
+        """Get color for wind arrow based on speed"""
+        if speed >= 30:
+            return 'red'      # Strong winds
+        elif speed >= 20:
+            return 'orange'   # Moderate winds
+        elif speed >= 10:
+            return 'yellow'   # Light winds
+        else:
+            return 'green'    # Calm winds
+
+def create_map_manager() -> WeatherMapManager:
+    """Factory function to create map manager"""
+    return WeatherMapManager()
+