app.py

from flask import Flask, request, jsonify, render_template
import json
import requests
import random
import math
import os
from dotenv import load_dotenv

load_dotenv()  # Load environment variables from .env file

app = Flask(__name__)

GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
NOMINATIM_USER_AGENT = "GeoSafeConstruct/1.0 ([email protected])"  # IMPORTANT: Change to your app name and contact email


# --- Nominatim API Functions ---
def get_location_name_from_coords(lat, lon):
    """Fetches a location name/address from coordinates using Nominatim."""
    headers = {"User-Agent": NOMINATIM_USER_AGENT}
    url = f"https://nominatim.openstreetmap.org/reverse?format=json&lat={lat}&lon={lon}&zoom=16&addressdetails=1"
    try:
        response = requests.get(url, headers=headers, timeout=10)
        response.raise_for_status()
        data = response.json()
        # Construct a readable address, prioritizing certain fields
        address = data.get('address', {})
        parts = [
            address.get('road'), address.get('neighbourhood'), address.get('suburb'),
            address.get('city_district'), address.get('city'), address.get('town'),
            address.get('village'), address.get('county'), address.get('state'),
            address.get('postcode'), address.get('country')
        ]
        # Filter out None values and join
        display_name = data.get('display_name', "Unknown Location")
        filtered_parts = [part for part in parts if part]
        if len(filtered_parts) > 3:  # If many parts, use display_name for brevity
            return display_name
        elif filtered_parts:
            return ", ".join(filtered_parts[:3])  # Take first few relevant parts
        return display_name  # Fallback to full display_name
    except requests.exceptions.RequestException as e:
        print(f"Nominatim reverse geocoding error: {e}")
        return f"Area around {lat:.3f}, {lon:.3f}"
    except (json.JSONDecodeError, KeyError) as e:
        print(f"Nominatim response parsing error: {e}")
        return f"Area around {lat:.3f}, {lon:.3f}"


def get_coords_from_location_name(query):
    """Fetches coordinates from a location name/address using Nominatim."""
    headers = {"User-Agent": NOMINATIM_USER_AGENT}
    url = f"https://nominatim.openstreetmap.org/search?q={requests.utils.quote(query)}&format=json&limit=1"
    try:
        response = requests.get(url, headers=headers, timeout=10)
        response.raise_for_status()
        data = response.json()
        if data and isinstance(data, list) and len(data) > 0:
            return {
                "latitude": float(data[0].get("lat")),
                "longitude": float(data[0].get("lon")),
                "display_name": data[0].get("display_name")
            }
        return None
    except requests.exceptions.RequestException as e:
        print(f"Nominatim geocoding error: {e}")
        return None
    except (json.JSONDecodeError, KeyError, IndexError) as e:
        print(f"Nominatim geocoding response parsing error: {e}")
        return None


# --- Gemini API Functions ---
def call_gemini_api(prompt_text):
    """Generic function to call Gemini API."""
    if not GEMINI_API_KEY:
        print("GEMINI_API_KEY not found in environment variables.")
        return None, "Gemini API key not configured."

    url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent"
    headers = {
        "Content-Type": "application/json",
        "x-goog-api-key": GEMINI_API_KEY
    }
    payload = {
        "contents": [{"parts": [{"text": prompt_text}]}],
        "generationConfig": {
            "response_mime_type": "application/json",
            "temperature": 0.6,  # Adjust for creativity vs. factuality
            "topP": 0.9,
            "topK": 40
        },
        "safetySettings": [  # Add safety settings if needed
            {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
            {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
            {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
            {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}
        ]
    }

    try:
        response = requests.post(url, json=payload, headers=headers, timeout=60)  # Increased timeout
        response.raise_for_status()
        gemini_response_data = response.json()

        if 'candidates' not in gemini_response_data or not gemini_response_data['candidates']:
            print("Gemini API Error: No candidates in response.")
            print("Full Gemini Response:", gemini_response_data)
            if 'promptFeedback' in gemini_response_data and 'blockReason' in gemini_response_data['promptFeedback']:
                return None, f"Content blocked by API: {gemini_response_data['promptFeedback']['blockReason']}"
            return None, "Gemini API returned no content."

        json_text_content = gemini_response_data['candidates'][0]['content']['parts'][0]['text']

        # Attempt to clean JSON string if it's wrapped in markdown
        if json_text_content.strip().startswith("```json"):
            json_text_content = json_text_content.strip()[7:]
        if json_text_content.strip().endswith("```"):
            json_text_content = json_text_content.strip()[:-3]

        return json.loads(json_text_content.strip()), None
    except requests.exceptions.Timeout:
        print("Gemini API call timed out.")
        return None, "Analysis request timed out. Please try again."
    except requests.exceptions.RequestException as e:
        print(f"Error calling Gemini API (RequestException): {e}")
        return None, f"Could not connect to analysis service: {e}"
    except json.JSONDecodeError as e:
        print(f"Error decoding JSON from Gemini API: {e}")
        print(f"Received text for JSON parsing: {json_text_content if 'json_text_content' in locals() else 'N/A'}")
        return None, "AI returned an invalid response format. Please try again."
    except (KeyError, IndexError) as e:
        print(f"Error parsing Gemini API response structure: {e}")
        print(f"Gemini response: {gemini_response_data if 'gemini_response_data' in locals() else 'N/A'}")
        return None, "AI returned an unexpected response structure."
    except Exception as e:
        print(f"An unexpected error occurred during Gemini call: {e}")
        return None, f"An unexpected error occurred: {e}"


def get_safety_analysis_with_gemini(latitude, longitude, location_address):
    """
    Calls Gemini API to analyze construction safety.
    """
    prompt = f"""
    **Objective:** Provide a detailed and realistic construction site safety analysis for the location: {location_address} (Latitude: {latitude}, Longitude: {longitude}).

    **Output Format:** STRICTLY JSON. Do NOT include any text outside the JSON structure (e.g., no '```json' or '```' wrappers).

    **JSON Structure:**
    {{
        "location_name": "{location_address}", // Use the provided address
        "safety_score": "integer (0-100, overall safety score, be critical and realistic)",
        "suitability_statement": "string (e.g., 'Generally Suitable with Mitigations', 'High Caution Advised', 'Not Recommended without Major Intervention')",
        "summary_assessment": "string (3-4 sentences summarizing key findings, suitability, and major concerns. Be specific.)",
        "geological_risks": {{
            "earthquake_risk": {{
                "level": "string ('Low', 'Medium', 'High', 'Very High', 'Not Assessed')",
                "details": "string (Specifics like proximity to faults, historical activity, soil liquefaction potential if known. If not assessed, state why.)"
            }},
            "landslide_risk": {{
                "level": "string ('Low', 'Medium', 'High', 'Very High', 'Not Assessed')",
                "details": "string (Slope stability, soil type, vegetation, historical incidents. If not assessed, state why.)"
            }},
            "soil_stability": {{
                "type": "string ('Stable', 'Moderately Stable', 'Unstable', 'Variable', 'Requires Investigation')",
                "concerns": ["string array (e.g., 'Expansive clays present', 'High water table', 'Poor drainage', 'Subsidence risk')"]
            }}
        }},
        "hydrological_risks": {{
            "flood_risk": {{
                "level": "string ('Low', 'Medium', 'High', 'Very High', 'Not Assessed')",
                "details": "string (Proximity to water bodies, floodplain maps, historical flooding, drainage issues. If not assessed, state why.)"
            }},
            "tsunami_risk": {{ // Only include if geographically relevant (coastal)
                "level": "string ('Negligible', 'Low', 'Medium', 'High')",
                "details": "string (Distance from coast, elevation, historical data.)"
            }}
        }},
        "other_environmental_risks": [ // Array of objects, include if relevant
            {{ "type": "Wildfire", "level": "string ('Low', 'Medium', 'High')", "details": "string (Vegetation, climate, fire history)" }},
            {{ "type": "Extreme Weather (Hurricanes/Tornadoes/High Winds)", "level": "string ('Low', 'Medium', 'High')", "details": "string (Regional patterns, building code requirements)" }},
            {{ "type": "Industrial Hazards", "level": "string ('Low', 'Medium', 'High')", "details": "string (Proximity to industrial plants, pipelines, hazardous material routes)" }}
        ],
        "key_risk_factors_summary": ["string array (Bulleted list of 3-5 most critical risk factors for this specific site)"],
        "mitigation_recommendations": ["string array (Actionable, specific recommendations, e.g., 'Conduct Level 2 Geotechnical Survey focusing on shear strength', 'Implement earthquake-resistant design to Zone IV standards', 'Elevate foundation by 1.5m above base flood elevation')"],
        "further_investigations_needed": ["string array (e.g., 'Detailed hydrological study', 'Environmental Impact Assessment', 'Traffic impact study')"],
        "alternative_locations_analysis": [ // Up to 2-3 alternatives. If none are significantly better, state that.
            {{
                "name_suggestion": "string (Suggest a conceptual name like 'North Ridge Site' or 'Valley View Plot')",
                "latitude": "float (as string, e.g., '18.5234')",
                "longitude": "float (as string, e.g., '73.8567')",
                "estimated_distance_km": "float (as string, e.g., '8.5')",
                "brief_justification": "string (Why is this a potential alternative? E.g., 'Appears to be on more stable ground', 'Further from flood plain')",
                "potential_pros": ["string array"],
                "potential_cons": ["string array"],
                "comparative_safety_score_estimate": "integer (0-100, relative to primary site)"
            }}
        ],
        "data_confidence_level": "string ('Low', 'Medium', 'High' - based on assumed availability of public data for this general region, not specific site data which is unknown to you)",
        "disclaimer": "This AI-generated analysis is for preliminary informational purposes only and not a substitute for professional engineering, geological, and environmental assessments. On-site investigations are crucial."
    }}

    **Guidelines for Content:**
    - make sumre rnaodmness in score generation not same view ppijn t everytime
    - Be realistic. If data for a specific risk is unlikely to be publicly available for a random coordinate, mark it 'Not Assessed' and explain.
    - Focus on actionable insights.
    - For alternative locations, provide *different* lat/lon coordinates that are plausibly nearby (within 5-20km).
    - Ensure all string values are properly quoted. Ensure latitudes and longitudes for alternatives are strings representing floats.
    - The safety_score should reflect a comprehensive evaluation of all risks.
    - proper spacing and upo down margins shoudl be there
    """
    return call_gemini_api(prompt)


# --- Flask Routes ---
@app.route('/')
def index():
    return render_template('index.html')


@app.route('/api/geocode', methods=['POST'])
def geocode_location():
    data = request.json
    query = data.get('query')
    if not query:
        return jsonify({"error": "Query parameter is required."}), 400

    coords_data = get_coords_from_location_name(query)
    if coords_data:
        return jsonify(coords_data), 200
    else:
        return jsonify({"error": "Location not found or geocoding failed."}), 404


@app.route('/api/analyze_location', methods=['POST'])
def analyze_location_route():
    data = request.json
    try:
        latitude = float(data.get('latitude'))
        longitude = float(data.get('longitude'))
    except (TypeError, ValueError):
        return jsonify({"error": "Invalid latitude or longitude provided."}), 400

    # Get a human-readable name for the primary location
    primary_location_address = get_location_name_from_coords(latitude, longitude)

    # Call Gemini for the main analysis
    analysis_data, error = get_safety_analysis_with_gemini(latitude, longitude, primary_location_address)

    if error:
        # Fallback to a simpler mock-like structure if Gemini fails critically
        return jsonify({
            "error_message": error,
            "location_name": primary_location_address,
            "safety_score": random.randint(20, 50),  # Low score to indicate issue
            "summary_assessment": f"Could not perform detailed analysis due to: {error}. Basic location: {primary_location_address}.",
            "suitability_statement": "Analysis Incomplete",
            "geological_risks": {"earthquake_risk": {"level": "Not Assessed", "details": error}},
            "hydrological_risks": {"flood_risk": {"level": "Not Assessed", "details": error}},
            "disclaimer": "A technical issue prevented full analysis."
        }), 500

    # Post-process alternative locations to get their names
    if analysis_data.get("alternative_locations_analysis"):
        for alt_loc in analysis_data["alternative_locations_analysis"]:
            try:
                alt_lat = float(alt_loc.get("latitude"))
                alt_lon = float(alt_loc.get("longitude"))
                alt_loc["actual_name"] = get_location_name_from_coords(alt_lat, alt_lon)
                # Ensure distance is calculated if not provided by Gemini or if it's nonsensical
                if not alt_loc.get("estimated_distance_km") or float(alt_loc.get("estimated_distance_km", 0)) == 0:
                    alt_loc["estimated_distance_km"] = str(
                        calculate_haversine_distance(latitude, longitude, alt_lat, alt_lon))

            except (TypeError, ValueError, KeyError) as e:
                print(f"Error processing alternative location coords: {e}, data: {alt_loc}")
                alt_loc["actual_name"] = "Nearby Area (details unavailable)"
                # Fallback if lat/lon are missing or invalid for an alternative
                if "latitude" not in alt_loc or "longitude" not in alt_loc:
                    alt_loc["latitude"] = str(latitude + (random.random() - 0.5) * 0.05)  # Small offset
                    alt_loc["longitude"] = str(longitude + (random.random() - 0.5) * 0.05)

    return jsonify(analysis_data), 200


def calculate_haversine_distance(lat1, lon1, lat2, lon2):
    R = 6371  # Radius of Earth in kilometers
    try:
        lat1_rad, lon1_rad = math.radians(float(lat1)), math.radians(float(lon1))
        lat2_rad, lon2_rad = math.radians(float(lat2)), math.radians(float(lon2))
    except (ValueError, TypeError):
        print(f"Error converting lat/lon to float for Haversine: {lat1}, {lon1}, {lat2}, {lon2}")
        return 0.0  # Fallback
    dlon = lon2_rad - lon1_rad
    dlat = lat2_rad - lat1_rad
    a = math.sin(dlat / 2) ** 2 + math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(dlon / 2) ** 2
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
    distance = R * c
    return round(distance, 1)


if __name__ == '__main__':
    if not GEMINI_API_KEY:
        print("CRITICAL ERROR: GEMINI_API_KEY is not set in the environment.")
        print("Please create a .env file with GEMINI_API_KEY=YOUR_API_KEY or set it as an environment variable.")
    app.run(debug=True, port=5000)