from fastapi import FastAPI, HTTPException, Query, Request, Response
from fastapi.responses import HTMLResponse, JSONResponse, FileResponse
from fastapi.middleware.cors import CORSMiddleware
import pandas as pd
import numpy as np
from geopy.distance import geodesic
import folium
from folium import plugins
import osmnx as ox
import networkx as nx
from datetime import datetime
import json
import matplotlib.pyplot as plt
import plotly.express as px
import os
import time
from functools import lru_cache
from rtree import index
from pydantic import BaseModel, Field
from typing import List, Dict, Any, Optional

# Create app instance
app = FastAPI(title="Store Locator API")

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Create temp directory for files
os.makedirs('temp', exist_ok=True)

# Load and prepare the store data
stores_df = pd.read_csv('dataset of 50 stores.csv')

# Custom JSON encoder for numpy types
class NumpyJSONEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        return super().default(obj)

# Pydantic models for response validation
class StoreLocation(BaseModel):
    lat: float
    lon: float

class Store(BaseModel):
    store_name: str
    address: str
    contact: str
    distance: float
    estimated_delivery_time: int
    product_categories: str
    location: StoreLocation

class StoresResponse(BaseModel):
    status: str
    stores: List[Store]

class ErrorResponse(BaseModel):
    status: str
    message: str
    
class StoreLocator:
    def __init__(self, stores_dataframe):
        self.stores_df = stores_dataframe
        self.network_graph = None
        self.graph_cache = {}  # Cache for network graphs
        self.spatial_index = self._build_spatial_index()

    @lru_cache(maxsize=50)     
    def initialize_graph(self, center_point, dist=20000):
        """Initialize road network graph with caching"""
        cache_key = f"{center_point[0]}_{center_point[1]}"
        if cache_key in self.graph_cache:
            self.network_graph = self.graph_cache[cache_key]
            return True
        try:
            self.network_graph = ox.graph_from_point(center_point, dist=dist, network_type="drive")
            self.network_graph = ox.add_edge_speeds(self.network_graph)
            self.network_graph = ox.add_edge_travel_times(self.network_graph)
            return True
        except Exception as e:
            print(f"Error initializing graph: {str(e)}")
            return False
        
    def _build_spatial_index(self):
        idx = index.Index()
        for i, row in self.stores_df.iterrows():
            idx.insert(i, (row['Latitude'], row['Longitude'], 
                          row['Latitude'], row['Longitude']))
        return idx

    def calculate_distance(self, lat1, lon1, lat2, lon2):
        """Calculate direct distance between two points"""
        return geodesic((lat1, lon1), (lat2, lon2)).kilometers

    def estimate_delivery_time(self, distance, current_time=None):
        """Estimate delivery time based on distance and current time"""
        if current_time is None:
            current_time = datetime.now()

        # Base time: 5 mins base + 2 mins per km
        base_minutes = 5 + (distance * 2)
        
        # Apply traffic multiplier based on time of day
        hour = current_time.hour
        if hour in [8, 9, 10, 17, 18, 19]:  # Peak hours
            multiplier = 1.5
        elif hour in [23, 0, 1, 2, 3, 4]:   # Off-peak hours
            multiplier = 0.8
        else:                                # Normal hours
            multiplier = 1.0
            
        return round(base_minutes * multiplier)
    
    def find_nearby_stores(self, lat, lon, radius=5):
        """Find stores within radius using spatial index"""
        nearby_stores = []
        bbox = (lat - radius/111.0, lon - radius/111.0, 
               lat + radius/111.0, lon + radius/111.0)
        
        for store_id in self.spatial_index.intersection(bbox):
            store = self.stores_df.iloc[store_id]
            distance = self.calculate_distance(lat, lon, 
                                            store['Latitude'], 
                                            store['Longitude'])
            if distance <= radius:
                delivery_time = self.estimate_delivery_time(distance)
                nearby_stores.append({
                    'store_name': store['Store Name'],
                    'address': store['Address'],
                    'contact': str(store['Contact Number']),  # Convert to string to avoid int64 issues
                    'distance': round(distance, 2),
                    'estimated_delivery_time': int(delivery_time),  # Ensure integer type
                    'product_categories': store['Product Categories'],
                    'location': {
                        'lat': float(store['Latitude']),  # Ensure float type
                        'lon': float(store['Longitude'])  # Ensure float type
                    }
                })
        
        return sorted(nearby_stores, key=lambda x: x['distance'])

    def create_store_map(self, center_lat, center_lon, radius=5):
        """Create an interactive map with store locations"""
        # Create base map
        m = folium.Map(location=[center_lat, center_lon], 
                      zoom_start=13,
                      tiles="cartodbpositron",
                      prefer_canvas=True
                      )
        
        # Create marker cluster for better performance with many markers
        marker_cluster = plugins.MarkerCluster().add_to(m)
        # Add stores to map
        nearby_stores = self.find_nearby_stores(center_lat, center_lon, radius)
        
        for store in nearby_stores:
            # Prepare popup content
            popup_content = f"""
            <div style='width: 200px'>
                <b>{store['store_name']}</b><br>
                Address: {store['address']}<br>
                Distance: {store['distance']} km<br>
                Est. Delivery: {store['estimated_delivery_time']} mins<br>
                Categories: {store['product_categories']}
            </div>
            """
            
            # Add store marker
            folium.Marker(
                location=[store['location']['lat'], store['location']['lon']],
                popup=folium.Popup(popup_content, max_width=300),
                icon=folium.Icon(color='red', icon='info-sign')
            ).add_to(marker_cluster)
            
            # Add line to show distance from center
            folium.PolyLine(
                locations=[[center_lat, center_lon], 
                          [store['location']['lat'], store['location']['lon']]],
                weight=2,
                color='blue',
                opacity=0.3
            ).add_to(m)
        
        # Add current location marker
        folium.Marker(
            location=[center_lat, center_lon],
            popup='Your Location',
            icon=folium.Icon(color='green', icon='home')
        ).add_to(m)
        
        # Add fullscreen option
        plugins.Fullscreen().add_to(m)
        
        # Add layer control
        folium.LayerControl().add_to(m)
        
        return m
    
    # Initialize store locator
store_locator = StoreLocator(stores_df)

def create_animated_route(G, path, color, weight=3):
    """Create an animated route visualization"""
    features = []
    timestamps = []
        
    # Convert path nodes to coordinates
    route_coords = [
        (G.nodes[node]['y'], G.nodes[node]['x']) 
        for node in path
    ]
        
    # Create features for each segment of the route
    for i in range(len(route_coords) - 1):
        segment = {
                'type': 'Feature',
                'geometry': {
                    'type': 'LineString',
                    'coordinates': [
                        [route_coords[i][1], route_coords[i][0]],
                        [route_coords[i+1][1], route_coords[i+1][0]]
                    ]
                },
                'properties': {
                    'times': [datetime.now().isoformat()],
                    'style': {
                        'color': color,
                        'weight': weight,
                        'opacity': 0.8
                    }
                }
            }
        features.append(segment)
        timestamps.append(datetime.now().isoformat())
        
    return features

def create_route_animation_data(G, path_time, path_length):
    """Create animation data for route visualization"""
    lst_start, lst_end = [], []
    start_x, start_y = [], []
    end_x, end_y = [], []
    lst_length, lst_time = [], []
    
    # Process time-based path
    for a, b in zip(path_time[:-1], path_time[1:]):
        lst_start.append(a)
        lst_end.append(b)
        lst_length.append(round(G.edges[(a,b,0)]['length']))
        lst_time.append(round(G.edges[(a,b,0)]['travel_time']))
        start_x.append(G.nodes[a]['x'])
        start_y.append(G.nodes[a]['y'])
        end_x.append(G.nodes[b]['x'])
        end_y.append(G.nodes[b]['y'])
    
    # Create DataFrame
    df = pd.DataFrame(
        list(zip(lst_start, lst_end, start_x, start_y, end_x, end_y, 
                 lst_length, lst_time)),
        columns=["start", "end", "start_x", "start_y", "end_x", "end_y",
                "length", "travel_time"]
    ).reset_index().rename(columns={"index": "id"})
    
    return df

# Function to clean up temporary files
@app.middleware("http")
async def cleanup_temp_files(request: Request, call_next):
    temp_dir = 'temp'
    if os.path.exists(temp_dir):
        for file in os.listdir(temp_dir):
            file_path = os.path.join(temp_dir, file)
            try:
                if os.path.isfile(file_path) and file.endswith('.html'):
                    # Delete files older than 1 hour
                    if os.path.getmtime(file_path) < time.time() - 3600:
                        os.remove(file_path)
            except Exception as e:
                print(f"Error cleaning up temp files: {e}")
    
    response = await call_next(request)
    return response

@app.get("/", response_class=HTMLResponse)
async def home():
    """API documentation homepage"""
    html_content = f"""
    <!DOCTYPE html>
    <html lang="en">
    <head>
        <meta charset="UTF-8">
        <meta name="viewport" content="width=device-width, initial-scale=1.0">
        <title>falcao-maps API Documentation</title>
        <style>
            body {{
                font-family: Arial, sans-serif;
                margin: 20px;
            }}
            h1, h2 {{
                color: #333;
            }}
            pre {{
                background-color: #f4f4f4;
                padding: 10px;
                border: 1px solid #ddd;
                border-radius: 5px;
            }}
        </style>
    </head>
    <body>
        <h1>Welcome to falcao-maps</h1>
        <button><a href="https://lucifer7210-maps.hf.space" target="_blank" >Link</a></button>
        <p>Based on your uploaded dataset and deployed API, here are example API calls for your client:</p>
        <h2>1. Find Nearby Stores (JSON Response)</h2>
        <pre>
            <a href="https://lucifer7210-maps.hf.space/api/stores/nearby?lat=18.9695&lon=72.8320&radius=0.5">api/stores/nearby?lat=18.9695&lon=72.8320&radius=0.5</a>
        </pre>
        <p>Use this to get store details near Market Road area within 1km</p>

        <h2>2. View Basic Store Map</h2>
        <pre>
        <a href="https://lucifer7210-maps.hf.space/api/stores/map?lat=18.9701&lon=72.8330&radius=0.5" target="_blank">
https://lucifer7210-maps.hf.space/api/stores/map?lat=18.9701&lon=72.8330&radius=0.5
        </a>
        </pre>
        <p>Shows map centered at Main Street with 500m radius</p>

        <h2>3. View All Store Locations with Color Coding</h2>
        <pre>
        <a href="https://lucifer7210-maps.hf.space/api/stores/locations?lat=18.9685&lon=72.8325&radius=2" target="_blank">
            https://lucifer7210-maps.hf.space/api/stores/locations?lat=18.9685&lon=72.8325&radius=2
        </a>
        </pre>
        <p>Shows detailed map with color-coded stores within 2km</p>

        <h2>4. Get Route Between Points</h2>
        <p>Example routes:</p>
        <pre>
# Route from Park Avenue to Hill Road stores
https://lucifer7210-maps.hf.space/api/stores/route?user_lat=18.9710&user_lon=72.8335&store_lat=18.9705&store_lon=72.8345&viz_type=simple

# Route from Main Street to Market Road stores
https://lucifer7210-maps.hf.space/api/stores/route?user_lat=18.9701&user_lon=72.8330&store_lat=18.9695&store_lon=72.8320&viz_type=simple
        </pre>

        <h2>Key Location Points in Dataset:</h2>
        <ul>
            <li>Main Street Area: 18.9701, 72.8330</li>
            <li>Park Avenue: 18.9710, 72.8335</li>
            <li>Market Road: 18.9695, 72.8320</li>
            <li>Shopping Center: 18.9670, 72.8300</li>
            <li>Commercial Street: 18.9690, 72.8340</li>
        </ul>
    </body>
    </html>
    """
    return HTMLResponse(content=html_content)

@app.get("/api/stores/nearby", response_model=StoresResponse, responses={400: {"model": ErrorResponse}})
async def get_nearby_stores(
    lat: float = Query(..., description="Latitude of user location"),
    lon: float = Query(..., description="Longitude of user location"),
    radius: float = Query(5, description="Search radius in kilometers")
):
    """
    Get nearby stores based on user location
    """
    try:
        nearby_stores = store_locator.find_nearby_stores(lat, lon, radius)
        return {"status": "success", "stores": nearby_stores}
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

@app.get("/api/stores/map", response_class=HTMLResponse, responses={400: {"model": ErrorResponse}})
async def get_stores_map(
    lat: float = Query(..., description="Latitude of center point"),
    lon: float = Query(..., description="Longitude of center point"),
    radius: float = Query(5, description="Search radius in kilometers")
):
    """
    Get HTML map with store locations
    """
    try:
        store_map = store_locator.create_store_map(lat, lon, radius)
        
        # Create complete HTML content
        html_content = f"""
        <!DOCTYPE html>
        <html>
        <head>
            <meta charset="utf-8">
            <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <title>Stores Map</title>
            <style>
                body {{
                    margin: 0;
                    padding: 0;
                    width: 100vw;
                    height: 100vh;
                    overflow: hidden;
                }}
                #map {{
                    width: 100%;
                    height: 100%;
                }}
            </style>
        </head>
        <body>
            {store_map.get_root().render()}
            <script>
                window.onload = function() {{
                    setTimeout(function() {{
                        window.dispatchEvent(new Event('resize'));
                    }}, 1000);
                }};
            </script>
        </body>
        </html>
        """
        
        # Save the HTML to a file
        file_path = 'temp/stores_map.html'
        with open(file_path, 'w', encoding='utf-8') as f:
            f.write(html_content)
        
        # Return the file
        return FileResponse(file_path, media_type='text/html')
        
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))
    
@app.get("/api/stores/route", response_class=HTMLResponse, responses={400: {"model": ErrorResponse}, 404: {"model": ErrorResponse}})
async def get_store_route(
    user_lat: float = Query(..., description="Latitude of user location"),
    user_lon: float = Query(..., description="Longitude of user location"),
    store_lat: float = Query(..., description="Latitude of store location"),
    store_lon: float = Query(..., description="Longitude of store location")
):
    """
    Get route between user location and store
    """
    try:
        # Initialize graph if not already initialized
        if store_locator.network_graph is None:
            store_locator.initialize_graph((user_lat, user_lon))
        
        # Get nearest nodes
        start_node = ox.distance.nearest_nodes(
            store_locator.network_graph, user_lon, user_lat)
        end_node = ox.distance.nearest_nodes(
            store_locator.network_graph, store_lon, store_lat)
        
        try:
            # Calculate paths
            path_time = nx.shortest_path(
                store_locator.network_graph, 
                start_node, 
                end_node, 
                weight='travel_time'
            )
            
            # Create animation data
            lst_start, lst_end = [], []
            start_x, start_y = [], []
            end_x, end_y = [], []
            lst_length, lst_time = [], []

            for a, b in zip(path_time[:-1], path_time[1:]):
                lst_start.append(a)
                lst_end.append(b)
                lst_length.append(round(store_locator.network_graph.edges[(a,b,0)]['length']))
                lst_time.append(round(store_locator.network_graph.edges[(a,b,0)]['travel_time']))
                start_x.append(store_locator.network_graph.nodes[a]['x'])
                start_y.append(store_locator.network_graph.nodes[a]['y'])
                end_x.append(store_locator.network_graph.nodes[b]['x'])
                end_y.append(store_locator.network_graph.nodes[b]['y'])

            df = pd.DataFrame(
                list(zip(lst_start, lst_end, start_x, start_y, end_x, end_y, 
                        lst_length, lst_time)),
                columns=["start", "end", "start_x", "start_y",
                        "end_x", "end_y", "length", "travel_time"]
            ).reset_index().rename(columns={"index": "id"})

            # Create animation using plotly
            df_start = df[df["start"] == start_node]
            df_end = df[df["end"] == end_node]

            fig = px.scatter_mapbox(
                data_frame=df, 
                lon="start_x", 
                lat="start_y",
                zoom=15, 
                width=1000, 
                height=800,
                animation_frame="id",
                mapbox_style="carto-positron"
            )

            # Add driver marker
            fig.data[0].marker = {"size": 12}

            # Add start point
            fig.add_trace(
                px.scatter_mapbox(
                    data_frame=df_start, 
                    lon="start_x", 
                    lat="start_y"
                ).data[0]
            )
            fig.data[1].marker = {"size": 15, "color": "red"}

            # Add end point
            fig.add_trace(
                px.scatter_mapbox(
                    data_frame=df_end, 
                    lon="start_x", 
                    lat="start_y"
                ).data[0]
            )
            fig.data[2].marker = {"size": 15, "color": "green"}

            # Add route
            fig.add_trace(
                px.line_mapbox(
                    data_frame=df, 
                    lon="start_x", 
                    lat="start_y"
                ).data[0]
            )

            # Update layout with slower animation settings
            fig.update_layout(
                showlegend=False,
                margin={"r":0,"t":0,"l":0,"b":0},
                autosize=True,
                height=None,
                updatemenus=[{
                    "type": "buttons",
                    "showactive": False,
                    "y": 0,
                    "x": 0,
                    "xanchor": "left",
                    "yanchor": "bottom",
                    "buttons": [
                        {
                            "label": "Play",
                            "method": "animate",
                            "args": [
                                None,
                                {
                                    "frame": {"duration": 1000, "redraw": True},
                                    "fromcurrent": True,
                                    "transition": {"duration": 800}
                                }
                            ]
                        },
                        {
                            "label": "Pause",
                            "method": "animate",
                            "args": [
                                [None],
                                {
                                    "frame": {"duration": 0, "redraw": False},
                                    "mode": "immediate",
                                    "transition": {"duration": 0}
                                }
                            ]
                        }
                    ]
                }],
                sliders=[{
                    "currentvalue": {"prefix": "Step: "},
                    "pad": {"t": 20},
                    "len": 0.9,
                    "x": 0.1,
                    "xanchor": "left",
                    "y": 0.02,
                    "yanchor": "bottom",
                    "steps": [
                        {
                            "args": [
                                [k],
                                {
                                    "frame": {"duration": 1000, "redraw": True},
                                    "transition": {"duration": 500},
                                    "mode": "immediate"
                                }
                            ],
                            "label": str(k),
                            "method": "animate"
                        } 
                        for k in range(len(df))
                    ]
                }]
            )

            # Create complete HTML content
            html_content = f"""
            <!DOCTYPE html>
            <html>
            <head>
                <meta charset="utf-8">
                <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
                <title>Route Map</title>
                <style>
                    body {{
                        margin: 0;
                        padding: 0;
                        width: 100vw;
                        height: 100vh;
                        overflow: hidden;
                    }}
                    #map-container {{
                        width: 100%;
                        height: 100%;
                    }}
                </style>
            </head>
            <body>
                <div id="map-container">
                    {fig.to_html(include_plotlyjs=True, full_html=False)}
                </div>
                <script>
                    window.onload = function() {{
                        setTimeout(function() {{
                            window.dispatchEvent(new Event('resize'));
                        }}, 1000);
                    }};
                </script>
            </body>
            </html>
            """
            
            # Save the HTML to a file
            file_path = 'temp/route_map.html'
            with open(file_path, 'w', encoding='utf-8') as f:
                f.write(html_content)
            
            # Return the file
            return FileResponse(file_path, media_type='text/html')

        except nx.NetworkXNoPath:
            raise HTTPException(status_code=404, detail="No route found")
            
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))
    
@app.get("/api/stores/locations", response_class=HTMLResponse, responses={400: {"model": ErrorResponse}})
async def get_all_store_locations(
    lat: float = Query(..., description="Latitude of center point"),
    lon: float = Query(..., description="Longitude of center point"),
    radius: float = Query(10, description="Search radius in kilometers")
):
    """
    Get a map showing all stores in the given radius with colors based on distance
    """
    try:
        # Get nearby stores
        nearby_stores = store_locator.find_nearby_stores(lat, lon, radius)
        
        # Create base map centered on user location
        m = folium.Map(
            location=[lat, lon],
            zoom_start=12,
            tiles="cartodbpositron"
        )
        
        # Add user location marker
        folium.Marker(
            [lat, lon],
            popup='Your Location',
            icon=folium.Icon(color='green', icon='home')
        ).add_to(m)
        
        # Add markers for each store with color coding based on distance
        for store in nearby_stores:
            # Color code based on distance
            if store['distance'] <= 2:
                color = 'red'  # Very close
            elif store['distance'] <= 5:
                color = 'orange'  # Moderate distance
            else:
                color = 'blue'  # Further away
                
            # Create detailed popup content with mobile-friendly styling
            popup_content = f"""
            <div style='width: 200px; font-size: 14px;'>
                <h4 style='color: {color}; margin: 0 0 8px 0;'>{store['store_name']}</h4>
                <b>Address:</b> {store['address']}<br>
                <b>Distance:</b> {store['distance']} km<br>
                <b>Est. Delivery:</b> {store['estimated_delivery_time']} mins<br>
                <b>Contact:</b> {store['contact']}<br>
                <b>Categories:</b> {store['product_categories']}<br>
                <button onclick="window.location.href='/api/stores/route?user_lat={lat}&user_lon={lon}&store_lat={store['location']['lat']}&store_lon={store['location']['lon']}'" 
                        style='margin-top: 8px; padding: 8px; width: 100%; background-color: #007bff; color: white; border: none; border-radius: 4px;'>
                    Get Route
                </button>
            </div>
            """
            
            # Add store marker
            folium.Marker(
                location=[store['location']['lat'], store['location']['lon']],
                popup=folium.Popup(popup_content, max_width=300),
                icon=folium.Icon(color=color, icon='info-sign'),
                tooltip=f"{store['store_name']} ({store['distance']} km)"
            ).add_to(m)
            
            # Add circle to show distance
            folium.Circle(
                location=[store['location']['lat'], store['location']['lon']],
                radius=store['distance'] * 100,
                color=color,
                fill=True,
                opacity=0.1
            ).add_to(m)
        
        # Add distance circles from user location
        for radius_circle, color in [(2000, 'red'), (5000, 'orange'), (radius * 1000, 'blue')]:
            folium.Circle(
                location=[lat, lon],
                radius=radius_circle,
                color=color,
                fill=False,
                weight=1,
                dash_array='5, 5'
            ).add_to(m)
        
        # Create mobile-friendly HTML content
        html_content = f"""
        <!DOCTYPE html>
        <html>
        <head>
            <meta charset="utf-8">
            <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <title>Nearby Stores</title>
            <style>
                body {{
                    margin: 0;
                    padding: 0;
                    width: 100vw;
                    height: 100vh;
                    overflow: hidden;
                }}
                #map {{
                    width: 100%;
                    height: 100%;
                }}
                .legend {{
                    position: fixed;
                    bottom: 20px;
                    right: 20px;
                    background: white;
                    padding: 10px;
                    border-radius: 5px;
                    box-shadow: 0 1px 5px rgba(0,0,0,0.2);
                    font-size: 12px;
                    z-index: 1000;
                }}
                .info-box {{
                    position: fixed;
                    top: 20px;
                    left: 20px;
                    background: white;
                    padding: 10px;
                    border-radius: 5px;
                    box-shadow: 0 1px 5px rgba(0,0,0,0.2);
                    font-size: 12px;
                    z-index: 1000;
                }}
            </style>
        </head>
        <body>
            {m.get_root().render()}
            <div class="legend">
                <b>Distance Zones</b><br>
                <span style="color: red;">●</span> &lt; 2 km<br>
                <span style="color: orange;">●</span> 2-5 km<br>
                <span style="color: blue;">●</span> &gt; 5 km
            </div>
            <div class="info-box">
                <b>Search Radius:</b> {radius} km<br>
                <b>Stores Found:</b> {len(nearby_stores)}
            </div>
            <script>
                window.onload = function() {{
                    setTimeout(function() {{
                        window.dispatchEvent(new Event('resize'));
                    }}, 1000);
                }};
            </script>
        </body>
        </html>
        """
        
        # Save and return the file
        file_path = 'temp/locations_map.html'
        with open(file_path, 'w', encoding='utf-8') as f:
            f.write(html_content)
        
        return FileResponse(file_path, media_type='text/html')
    
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

# Add swagger UI customization
@app.on_event("startup")
async def startup_event():
    app.title = "Store Locator API"
    app.description = "API for locating nearby stores and generating routes"
    app.version = "1.0.0"

# Entry point for running the application
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)