Clean deployment version without venv

.dockerignore

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+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis
+
+.DS_Store
+.vscode
+README.md
+.env
+.gitignore
+*.md
+venv/
+.venv/

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+index.faiss filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# Use Python 3.9 slim image
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy all application files
+COPY . .
+
+# Create a non-root user
+RUN useradd -m -u 1000 user
+USER user
+
+# Set environment variables
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH \
+    PYTHONPATH=$HOME/app \
+    PYTHONUNBUFFERED=1 \
+    GRADIO_ALLOW_FLAGGING=never \
+    GRADIO_NUM_PORTS=1 \
+    GRADIO_SERVER_NAME=0.0.0.0 \
+    GRADIO_THEME=huggingface \
+    SYSTEM=spaces
+
+# Change to user's home directory
+WORKDIR $HOME/app
+
+# Copy files with correct ownership
+COPY --chown=user . $HOME/app
+
+# Expose port 7860 (Hugging Face Spaces default)
+EXPOSE 7860
+
+# Health check
+HEALTHCHECK CMD curl --fail http://localhost:7860/_health || exit 1
+
+# Command to run the application
+CMD ["python", "app.py"]

README.md

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+---
+title: CCET Chat Assistance
+emoji: 🏃
+colorFrom: pink
+colorTo: indigo
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+from flask import Flask, request, jsonify, render_template
+import google.generativeai as genai
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+from langchain.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from dotenv import load_dotenv
+import logging
+import re
+from custom_prompt import get_custom_prompt
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, 
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Load environment variables
+load_dotenv()
+
+app = Flask(__name__)
+
+# Initialize the environment - Check multiple possible env var names
+GOOGLE_API_KEY = (os.getenv("GOOGLE_API_KEY") or 
+                  os.getenv("GEMINI_API_KEY") or 
+                  os.getenv("GOOGLE_GEMINI_API_KEY"))
+
+if not GOOGLE_API_KEY or GOOGLE_API_KEY == "your_api_key_here":
+    logger.error("No valid GOOGLE_API_KEY found in environment variables")
+    print("⚠️ Please set your Gemini API key in the environment variables")
+    print("Supported env var names: GOOGLE_API_KEY, GEMINI_API_KEY, GOOGLE_GEMINI_API_KEY")
+else:
+    genai.configure(api_key=GOOGLE_API_KEY)
+    logger.info("API key configured successfully")
+
+# Global variables for the chain and memory
+qa_chain = None
+memory = None
+
+def initialize_chatbot():
+    global qa_chain, memory
+    
+    logger.info("Initializing chatbot...")
+    
+    # Initialize embeddings
+    try:
+        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+        logger.info("Embeddings initialized")
+    except Exception as e:
+        logger.error(f"Error initializing embeddings: {str(e)}")
+        return False
+    
+    # Load the vector store
+    try:
+        vector_store = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+        logger.info("Vector store loaded successfully!")
+    except Exception as e:
+        logger.error(f"Error loading vector store: {str(e)}")
+        print(f"⚠️ Error loading vector store: {str(e)}")
+        print("Make sure your 'faiss_index' folder is in the same directory as this script.")
+        return False
+    
+    # Create memory
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True,
+        output_key="answer"
+    )
+    logger.info("Conversation memory initialized")
+    
+    # Initialize the language model
+    try:
+        llm = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash",
+            temperature=0.2,
+            top_p=0.85,
+            google_api_key=GOOGLE_API_KEY
+        )
+        logger.info("Language model initialized")
+    except Exception as e:
+        logger.error(f"Error initializing language model: {str(e)}")
+        return False
+    
+    # Create the conversation chain with the custom prompt
+    try:
+        retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+        
+        qa_chain = ConversationalRetrievalChain.from_llm(
+            llm=llm,
+            retriever=retriever,
+            memory=memory,
+            verbose=True,
+            return_source_documents=False,  # Set to False to hide source documents
+            combine_docs_chain_kwargs={"prompt": get_custom_prompt()},
+        )
+        logger.info("QA chain created successfully")
+    except Exception as e:
+        logger.error(f"Error creating QA chain: {str(e)}")
+        return False
+    
+    return True
+
+# Function to format links as HTML anchor tags
+def format_links_as_html(text):
+    # Detect markdown style links [text](url)
+    markdown_pattern = r'\[(.*?)\]\((https?://[^\s\)]+)\)'
+    if re.search(markdown_pattern, text):
+        text = re.sub(markdown_pattern, r'<a href="\2" target="_blank">\1</a>', text)
+        return text
+    
+    # Handle URLs in square brackets [url]
+    bracket_pattern = r'\[(https?://[^\s\]]+)\]'
+    if re.search(bracket_pattern, text):
+        text = re.sub(bracket_pattern, r'<a href="\1" target="_blank">\1</a>', text)
+        return text
+    
+    # Regular URL pattern
+    url_pattern = r'(https?://[^\s\]]+)'
+    
+    # Find all URLs in the text
+    urls = re.findall(url_pattern, text)
+    
+    # If there are multiple URLs, keep only the first one
+    if len(urls) > 1:
+        for url in urls[1:]:
+            text = text.replace(url, "")
+    
+    # Replace the remaining URL with an HTML anchor tag
+    if urls:
+        text = re.sub(url_pattern, r'<a href="\1" target="_blank">\1</a>', text, count=1)
+    
+    return text
+
+# Function to properly escape asterisks for markdown rendering
+def escape_markdown(text):
+    # Replace single asterisks not intended for markdown with escaped versions
+    # This regex looks for asterisks that aren't part of markdown patterns
+    return re.sub(r'(?<!\*)\*(?!\*)', r'\*', text)
+
+# Function to format markdown and handle asterisks with proper line breaks
+def format_markdown_with_breaks(text):
+    # First remove escaped asterisks (\*) and replace with just asterisks (*)
+    text = text.replace('\\*', '*')
+    
+    # Handle bold text (convert **text** to <strong>text</strong>)
+    text = re.sub(r'\*\*(.*?)\*\*', r'<strong>\1</strong>', text)
+    
+    # Now split text by lines for processing asterisk line breaks
+    lines = text.split('\n')
+    formatted_lines = []
+    
+    for i, line in enumerate(lines):
+        # If line starts with asterisk (possibly after whitespace), add a line break before it
+        # except for the first line
+        if line.strip().startswith('*'):
+            # Extract content after the asterisk
+            content = line.strip()[1:].strip()
+            
+            # Add line break (except for the first line)
+            if i == 0 or len(formatted_lines) == 0:
+                formatted_lines.append(f"• {content}")
+            else:
+                formatted_lines.append(f"<br>• {content}")
+        else:
+            formatted_lines.append(line)
+    
+    return '\n'.join(formatted_lines)
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/_health')
+def health_check():
+    """Health check endpoint for Docker"""
+    return jsonify({"status": "healthy"}), 200
+
+@app.route('/api/chat', methods=['POST'])
+def chat():
+    global qa_chain
+    
+    # Initialize on first request if not already done
+    if qa_chain is None:
+        success = initialize_chatbot()
+        if not success:
+            return jsonify({"error": "Failed to initialize chatbot. Check server logs for details."}), 500
+    
+    data = request.json
+    user_message = data.get('message', '')
+    
+    if not user_message:
+        return jsonify({"error": "No message provided"}), 400
+    
+    try:
+        logger.info(f"Processing user query: {user_message}")
+        
+        # Process the query through the QA chain
+        result = qa_chain({"question": user_message})
+        
+        # Extract the answer
+        answer = result.get("answer", "I'm sorry, I couldn't generate a response.")
+        
+        # Format the answer (escape markdown, format links, and handle numbered lists)
+        answer = escape_markdown(answer)
+        answer = format_links_as_html(answer)
+        answer = format_markdown_with_breaks(answer)
+        
+        logger.info("Query processed successfully")
+        
+        return jsonify({
+            "answer": answer,
+            # No sources included in the response
+        })
+    
+    except Exception as e:
+        logger.error(f"Error processing request: {str(e)}")
+        return jsonify({"error": f"Error processing request: {str(e)}"}), 500
+
+if __name__ == '__main__':
+    # For Docker deployment, bind to all interfaces and use port 7860
+    port = int(os.environ.get('PORT', 7860))
+    app.run(host='0.0.0.0', port=port, debug=False)

custom_prompt.py

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+from langchain.prompts import PromptTemplate
+
+def get_custom_prompt():
+    # Create a system template that defines the chatbot's behavior
+    system_template = """You are a helpful AI assistant for our college. 
+    Your job is to provide accurate, helpful information about our college based on the data provided.
+    
+    When answering questions, use the following information as context:
+    {context}
+    
+    Chat History: {chat_history}
+    
+    Use the above context to answer the user's question. If you don't know the answer based on the provided context, 
+    say so clearly rather than making up information. If the answer is not in the context, 
+    you can provide general information about colleges but make it clear that it's not specific to this college.
+    
+    Be conversational, friendly, and professional.
+    Question: {question}
+    Answer:"""
+    
+    return PromptTemplate(
+        input_variables=["context", "chat_history", "question"],
+        template=system_template
+    )

requirements.txt

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+flask
+google-generativeai
+langchain
+langchain-google-genai
+faiss-cpu
+python-dotenv
+gunicorn