import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
import folium
from streamlit_folium import st_folium
import requests
from geopy.distance import geodesic
import time

# Page configuration
st.set_page_config(
    page_title="AI City Companion",
    page_icon="🌍",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for modern styling
st.markdown("""
<style>
    .main-header {
        font-size: 3rem;
        font-weight: bold;
        text-align: center;
        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        margin-bottom: 2rem;
    }
    
    .feature-card {
        background: white;
        padding: 1.5rem;
        border-radius: 10px;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
        margin: 1rem 0;
        border-left: 4px solid #667eea;
    }
    
    .emergency-alert {
        background: #fee2e2;
        border: 1px solid #fecaca;
        border-radius: 8px;
        padding: 1rem;
        margin: 1rem 0;
        color: #991b1b;
    }
    
    .success-alert {
        background: #dcfce7;
        border: 1px solid #bbf7d0;
        border-radius: 8px;
        padding: 1rem;
        margin: 1rem 0;
        color: #166534;
    }
    
    .metric-card {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        padding: 1rem;
        border-radius: 10px;
        text-align: center;
        margin: 0.5rem;
    }
</style>
""", unsafe_allow_html=True)

# Initialize session state
if 'user_location' not in st.session_state:
    st.session_state.user_location = [24.8607, 67.0011]  # Karachi, Pakistan
if 'user_preferences' not in st.session_state:
    st.session_state.user_preferences = {}
if 'search_history' not in st.session_state:
    st.session_state.search_history = []
if 'current_itinerary' not in st.session_state:
    st.session_state.current_itinerary = []

# Mock data for demonstration
@st.cache_data
def load_mock_data():
    # Healthcare & Emergency
    healthcare_data = pd.DataFrame({
        'name': ['City Hospital', 'Emergency Clinic 24/7', 'Al-Shifa Medical Center', 'Quick Care Pharmacy', 'Blood Bank Center'],
        'category': ['Hospital', 'Clinic', 'Hospital', 'Pharmacy', 'Blood Bank'],
        'lat': [24.8615, 24.8590, 24.8625, 24.8580, 24.8635],
        'lon': [67.0020, 67.0000, 67.0040, 66.9990, 67.0050],
        'rating': [4.5, 4.2, 4.7, 4.0, 4.3],
        'distance': [0.5, 0.8, 0.3, 1.2, 0.7],
        'phone': ['+92-21-111-222', '+92-21-333-444', '+92-21-555-666', '+92-21-777-888', '+92-21-999-000'],
        'open_24h': [True, True, False, False, True]
    })
    
    # Food & Restaurants
    food_data = pd.DataFrame({
        'name': ['Halal Biryani House', 'Vegetarian Delight', 'Quick Bites Cafe', 'Traditional Karahi', 'Fresh Juice Corner'],
        'category': ['Pakistani', 'Vegetarian', 'Fast Food', 'Pakistani', 'Beverages'],
        'lat': [24.8600, 24.8620, 24.8585, 24.8640, 24.8575],
        'lon': [67.0015, 67.0035, 66.9995, 67.0055, 66.9985],
        'rating': [4.6, 4.3, 4.1, 4.8, 4.2],
        'price_range': ['$$', '$', '$', '$$$', '$'],
        'dietary': ['Halal', 'Vegetarian', 'Mixed', 'Halal', 'Vegan'],
        'crowd_level': ['Medium', 'Low', 'High', 'Medium', 'Low'],
        'noise_level': ['Medium', 'Low', 'High', 'Medium', 'Low']
    })
    
    # Electronics & Repairs
    electronics_data = pd.DataFrame({
        'name': ['TechMart Electronics', 'Mobile Repair Hub', 'Laptop Service Center', 'Gadget World', 'SIM Card Center'],
        'category': ['Electronics Store', 'Repair Shop', 'Repair Shop', 'Electronics Store', 'Telecom'],
        'lat': [24.8610, 24.8595, 24.8630, 24.8570, 24.8645],
        'lon': [67.0025, 67.0005, 67.0045, 66.9980, 67.0060],
        'rating': [4.4, 4.1, 4.5, 4.3, 4.0],
        'services': ['Phones, Laptops, Accessories', 'Phone Repair', 'Laptop Repair', 'All Electronics', 'SIM Cards, Top-up'],
        'price_fair': [True, True, False, True, True]
    })
    
    # Attractions & Places
    attractions_data = pd.DataFrame({
        'name': ['Clifton Beach', 'Quaid Mausoleum', 'Empress Market', 'Karachi Zoo', 'Port Grand'],
        'category': ['Beach', 'Monument', 'Market', 'Zoo', 'Entertainment'],
        'lat': [24.8138, 24.8738, 24.8615, 24.9056, 24.8406],
        'lon': [67.0299, 67.0362, 67.0099, 67.0516, 67.0219],
        'rating': [4.2, 4.7, 4.0, 3.8, 4.1],
        'best_time': ['Evening', 'Morning', 'Morning', 'Morning', 'Evening'],
        'crowd_level': ['High', 'Medium', 'High', 'Medium', 'Medium'],
        'entry_fee': [0, 0, 0, 50, 0]
    })
    
    return healthcare_data, food_data, electronics_data, attractions_data

# Load data
healthcare_df, food_df, electronics_df, attractions_df = load_mock_data()

# Helper functions
def calculate_distance(lat1, lon1, lat2, lon2):
    return geodesic((lat1, lon1), (lat2, lon2)).kilometers

def get_recommendations(category, user_prefs=None):
    if category == "healthcare":
        return healthcare_df.sort_values('rating', ascending=False)
    elif category == "food":
        df = food_df.copy()
        if user_prefs and 'dietary' in user_prefs:
            df = df[df['dietary'].str.contains(user_prefs['dietary'], case=False, na=False)]
        if user_prefs and 'crowd_preference' in user_prefs:
            if user_prefs['crowd_preference'] == 'Low':
                df = df[df['crowd_level'] == 'Low']
        return df.sort_values('rating', ascending=False)
    elif category == "electronics":
        return electronics_df.sort_values('rating', ascending=False)
    elif category == "attractions":
        return attractions_df.sort_values('rating', ascending=False)

def create_map(data_df, center_lat, center_lon):
    m = folium.Map(location=[center_lat, center_lon], zoom_start=13)
    
    # Add user location
    folium.Marker(
        [center_lat, center_lon],
        popup="Your Location",
        icon=folium.Icon(color='red', icon='user')
    ).add_to(m)
    
    # Add points of interest
    colors = {'Hospital': 'green', 'Clinic': 'blue', 'Pharmacy': 'orange', 
              'Pakistani': 'red', 'Vegetarian': 'green', 'Fast Food': 'orange',
              'Electronics Store': 'purple', 'Repair Shop': 'darkblue',
              'Beach': 'lightblue', 'Monument': 'gray', 'Market': 'orange'}
    
    for idx, row in data_df.iterrows():
        color = colors.get(row.get('category', 'Unknown'), 'gray')
        folium.Marker(
            [row['lat'], row['lon']],
            popup=f"<b>{row['name']}</b><br>Rating: {row.get('rating', 'N/A')}<br>Category: {row.get('category', 'N/A')}",
            icon=folium.Icon(color=color)
        ).add_to(m)
    
    return m

# Main App
def main():
    # Header
    st.markdown('<h1 class="main-header">🌍 AI City Companion</h1>', unsafe_allow_html=True)
    st.markdown('<p style="text-align: center; font-size: 1.2rem; color: #666;">Your Smart Travel Guide for Safe & Smart City Navigation</p>', unsafe_allow_html=True)
    
    # Sidebar for user preferences
    with st.sidebar:
        st.header("🎯 Your Preferences")
        
        # Location input
        st.subheader("📍 Current Location")
        col1, col2 = st.columns(2)
        with col1:
            user_lat = st.number_input("Latitude", value=24.8607, format="%.4f")
        with col2:
            user_lon = st.number_input("Longitude", value=67.0011, format="%.4f")
        
        st.session_state.user_location = [user_lat, user_lon]
        
        # Personal preferences
        st.subheader("👤 Personal Preferences")
        dietary_pref = st.selectbox("Dietary Preference", ["Any", "Halal", "Vegetarian", "Vegan"])
        crowd_pref = st.selectbox("Crowd Preference", ["Any", "Low", "Medium", "High"])
        budget_pref = st.selectbox("Budget Range", ["Any", "$", "$$", "$$$"])
        
        st.session_state.user_preferences = {
            'dietary': dietary_pref,
            'crowd_preference': crowd_pref,
            'budget': budget_pref
        }
        
        # Emergency contacts
        st.subheader("🚨 Quick Emergency")
        if st.button("🏥 Nearest Hospital", use_container_width=True):
            st.session_state.emergency_mode = True
        if st.button("🚓 Police Station", use_container_width=True):
            st.info("Emergency: 15 (Police)")
        if st.button("🚑 Ambulance", use_container_width=True):
            st.info("Emergency: 1122 (Rescue)")

    # Main content tabs
    tab1, tab2, tab3, tab4, tab5 = st.tabs(["🗺️ Smart Map", "🔍 AI Search", "📋 Itinerary Builder", "📊 City Insights", "⚙️ Settings"])
    
    with tab1:
        st.header("🗺️ Smart Contextual City Map")
        
        # Map controls
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            map_category = st.selectbox("Show Category", ["All", "Healthcare", "Food", "Electronics", "Attractions"])
        with col2:
            time_filter = st.selectbox("Time Filter", ["Current", "Morning", "Afternoon", "Evening", "Night"])
        with col3:
            radius_km = st.slider("Search Radius (km)", 0.5, 10.0, 2.0)
        with col4:
            show_traffic = st.checkbox("Show Traffic Info")
        
        # Create and display map
        if map_category == "All":
            all_data = pd.concat([healthcare_df, food_df, electronics_df, attractions_df], ignore_index=True)
        elif map_category == "Healthcare":
            all_data = healthcare_df
        elif map_category == "Food":
            all_data = get_recommendations("food", st.session_state.user_preferences)
        elif map_category == "Electronics":
            all_data = electronics_df
        elif map_category == "Attractions":
            all_data = attractions_df
        
        # Filter by radius
        all_data['distance_calc'] = all_data.apply(
            lambda row: calculate_distance(user_lat, user_lon, row['lat'], row['lon']), axis=1
        )
        filtered_data = all_data[all_data['distance_calc'] <= radius_km]
        
        if not filtered_data.empty:
            map_obj = create_map(filtered_data, user_lat, user_lon)
            st_folium(map_obj, width=700, height=500)
            
            # Show nearby places
            st.subheader(f"📍 Nearby Places ({len(filtered_data)} found)")
            for idx, row in filtered_data.head(5).iterrows():
                with st.expander(f"{row['name']} - {row.get('category', 'Unknown')} ({row['distance_calc']:.1f}km)"):
                    col1, col2 = st.columns(2)
                    with col1:
                        st.write(f"⭐ Rating: {row.get('rating', 'N/A')}")
                        st.write(f"📞 Phone: {row.get('phone', 'N/A')}")
                    with col2:
                        if 'services' in row:
                            st.write(f"🔧 Services: {row['services']}")
                        if 'dietary' in row:
                            st.write(f"🍽️ Dietary: {row['dietary']}")
        else:
            st.warning("No places found in the selected radius. Try increasing the search area.")

    with tab2:
        st.header("🔍 AI-Powered Smart Search")
        
        # Multi-modal search
        search_type = st.radio("Search Type", ["Text Query", "Voice Command (Simulated)", "Image Upload (Simulated)"])
        
        if search_type == "Text Query":
            query = st.text_input("Ask me anything about the city:", 
                                placeholder="e.g., 'Find halal biryani that's not crowded' or 'Where can I fix my laptop?'")
            
            if query:
                st.session_state.search_history.append({"query": query, "timestamp": datetime.now()})
                
                # Simple AI simulation
                results = []
                query_lower = query.lower()
                
                if any(word in query_lower for word in ['hospital', 'doctor', 'medical', 'emergency']):
                    results = healthcare_df.head(3).to_dict('records')
                    st.success("🏥 Found healthcare facilities for you!")
                elif any(word in query_lower for word in ['food', 'eat', 'restaurant', 'biryani', 'halal']):
                    results = get_recommendations("food", st.session_state.user_preferences).head(3).to_dict('records')
                    st.success("🍽️ Found great food options!")
                elif any(word in query_lower for word in ['laptop', 'phone', 'repair', 'electronics', 'charger']):
                    results = electronics_df.head(3).to_dict('records')
                    st.success("🔧 Found electronics and repair services!")
                elif any(word in query_lower for word in ['visit', 'see', 'attraction', 'tourist']):
                    results = attractions_df.head(3).to_dict('records')
                    st.success("🎯 Found amazing places to visit!")
                else:
                    st.info("🤔 I'm learning! Try asking about healthcare, food, electronics, or attractions.")
                
                # Display results
                if results:
                    for result in results:
                        with st.container():
                            st.markdown(f"""
                            <div class="feature-card">
                                <h4>{result['name']}</h4>
                                <p><strong>Category:</strong> {result.get('category', 'N/A')}</p>
                                <p><strong>Rating:</strong> ⭐ {result.get('rating', 'N/A')}</p>
                                <p><strong>Distance:</strong> {result.get('distance', calculate_distance(user_lat, user_lon, result['lat'], result['lon'])):.1f} km</p>
                            </div>
                            """, unsafe_allow_html=True)
        
        elif search_type == "Voice Command (Simulated)":
            st.info("🎤 Voice search simulation - Click to 'speak'")
            if st.button("🎙️ Start Voice Search"):
                with st.spinner("Listening..."):
                    time.sleep(2)
                st.success("Voice recognized: 'Find nearest pharmacy'")
                results = healthcare_df[healthcare_df['category'] == 'Pharmacy']
                for idx, row in results.iterrows():
                    st.write(f"📍 {row['name']} - {row['distance']}km away")
        
        else:  # Image Upload
            st.info("📸 Image search simulation")
            uploaded_file = st.file_uploader("Upload an image of what you're looking for", type=['jpg', 'jpeg', 'png'])
            if uploaded_file:
                st.image(uploaded_file, caption="Analyzing image...", width=300)
                with st.spinner("AI analyzing image..."):
                    time.sleep(2)
                st.success("🔍 Detected: Broken phone charger")
                st.write("Found electronics repair shops nearby:")
                repair_shops = electronics_df[electronics_df['category'] == 'Repair Shop']
                for idx, row in repair_shops.iterrows():
                    st.write(f"🔧 {row['name']} - {row['services']}")

    with tab3:
        st.header("📋 Smart Itinerary Builder")
        
        # Itinerary preferences
        st.subheader("🎯 Tell me about your day")
        col1, col2 = st.columns(2)
        
        with col1:
            duration = st.selectbox("Trip Duration", ["Half Day (4 hours)", "Full Day (8 hours)", "Weekend (2 days)"])
            walking_pref = st.selectbox("Walking Preference", ["Minimal walking", "Moderate walking", "Lots of walking"])
            interests = st.multiselect("Interests", ["Food", "Shopping", "Culture", "Nature", "Technology", "Healthcare"])
        
        with col2:
            budget = st.selectbox("Budget Range", ["Budget ($)", "Mid-range ($$)", "Premium ($$$)"])
            group_size = st.number_input("Group Size", min_value=1, max_value=10, value=1)
            special_needs = st.multiselect("Special Requirements", ["Wheelchair accessible", "Halal food only", "Quiet places", "Female-friendly"])
        
        if st.button("🚀 Generate Smart Itinerary", use_container_width=True):
            with st.spinner("AI is crafting your perfect day..."):
                time.sleep(3)
            
            # Generate sample itinerary
            itinerary = [
                {"time": "09:00 AM", "activity": "Breakfast at Halal Biryani House", "duration": "1 hour", "type": "food"},
                {"time": "10:30 AM", "activity": "Visit Quaid Mausoleum", "duration": "1.5 hours", "type": "culture"},
                {"time": "12:30 PM", "activity": "Electronics shopping at TechMart", "duration": "1 hour", "type": "shopping"},
                {"time": "02:00 PM", "activity": "Lunch at Traditional Karahi", "duration": "1 hour", "type": "food"},
                {"time": "04:00 PM", "activity": "Relax at Clifton Beach", "duration": "2 hours", "type": "nature"},
                {"time": "06:30 PM", "activity": "Dinner at Port Grand", "duration": "1.5 hours", "type": "food"}
            ]
            
            st.session_state.current_itinerary = itinerary
            
            st.success("✅ Your personalized itinerary is ready!")
            
            # Display itinerary
            for i, item in enumerate(itinerary):
                with st.expander(f"{item['time']} - {item['activity']} ({item['duration']})"):
                    col1, col2, col3 = st.columns(3)
                    with col1:
                        st.write(f"⏰ Duration: {item['duration']}")
                    with col2:
                        st.write(f"🏷️ Type: {item['type'].title()}")
                    with col3:
                        if st.button(f"Get Directions", key=f"dir_{i}"):
                            st.info("🗺️ Opening navigation...")
            
            # Itinerary summary
            st.subheader("📊 Itinerary Summary")
            col1, col2, col3, col4 = st.columns(4)
            with col1:
                st.markdown('<div class="metric-card"><h3>6</h3><p>Total Stops</p></div>', unsafe_allow_html=True)
            with col2:
                st.markdown('<div class="metric-card"><h3>8.5h</h3><p>Total Duration</p></div>', unsafe_allow_html=True)
            with col3:
                st.markdown('<div class="metric-card"><h3>5.2km</h3><p>Total Distance</p></div>', unsafe_allow_html=True)
            with col4:
                st.markdown('<div class="metric-card"><h3>$$</h3><p>Est. Budget</p></div>', unsafe_allow_html=True)

    with tab4:
        st.header("📊 City Insights & Analytics")
        
        # Real-time city stats
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            st.metric("🌡️ Temperature", "28°C", "2°C")
        with col2:
            st.metric("🚦 Traffic Level", "Medium", "↑ 15%")
        with col3:
            st.metric("👥 Crowd Density", "Low", "↓ 5%")
        with col4:
            st.metric("💰 Price Index", "Moderate", "↑ 2%")
        
        # Charts and analytics
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("📈 Popular Categories")
            category_data = pd.DataFrame({
                'Category': ['Food', 'Healthcare', 'Electronics', 'Attractions', 'Shopping'],
                'Searches': [45, 23, 18, 32, 28]
            })
            fig = px.bar(category_data, x='Category', y='Searches', 
                        title="Most Searched Categories Today")
            st.plotly_chart(fig, use_container_width=True)
        
        with col2:
            st.subheader("⏰ Best Times to Visit")
            time_data = pd.DataFrame({
                'Hour': list(range(6, 23)),
                'Crowd_Level': [20, 30, 45, 60, 70, 85, 90, 95, 80, 70, 60, 50, 45, 55, 70, 85, 90]
            })
            fig = px.line(time_data, x='Hour', y='Crowd_Level', 
                         title="Crowd Levels Throughout the Day")
            st.plotly_chart(fig, use_container_width=True)
        
        # Safety alerts
        st.subheader("🛡️ Safety & Alerts")
        alerts = [
            {"type": "warning", "message": "Heavy traffic on Shahrah-e-Faisal (avoid 5-7 PM)"},
            {"type": "info", "message": "New electronics market opened in Saddar"},
            {"type": "success", "message": "All hospitals report normal capacity"},
        ]
        
        for alert in alerts:
            if alert["type"] == "warning":
                st.warning(f"⚠️ {alert['message']}")
            elif alert["type"] == "info":
                st.info(f"ℹ️ {alert['message']}")
            else:
                st.success(f"✅ {alert['message']}")

    with tab5:
        st.header("⚙️ Settings & Preferences")
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("🔔 Notifications")
            st.checkbox("Emergency alerts", value=True)
            st.checkbox("Traffic updates", value=True)
            st.checkbox("Price alerts", value=False)
            st.checkbox("New place recommendations", value=True)
            
            st.subheader("🌐 Language & Region")
            language = st.selectbox("Language", ["English", "Urdu", "Arabic"])
            currency = st.selectbox("Currency", ["PKR", "USD", "EUR"])
            
        with col2:
            st.subheader("🔒 Privacy & Safety")
            st.checkbox("Share location for better recommendations", value=True)
            st.checkbox("Save search history", value=True)
            st.checkbox("Anonymous usage analytics", value=False)
            
            st.subheader("📱 App Preferences")
            theme = st.selectbox("Theme", ["Auto", "Light", "Dark"])
            map_style = st.selectbox("Map Style", ["Standard", "Satellite", "Terrain"])
        
        # Export data
        st.subheader("📤 Export Your Data")
        if st.button("Download Search History"):
            if st.session_state.search_history:
                df = pd.DataFrame(st.session_state.search_history)
                st.download_button(
                    label="📥 Download CSV",
                    data=df.to_csv(index=False),
                    file_name="search_history.csv",
                    mime="text/csv"
                )
            else:
                st.info("No search history to export yet!")
        
        if st.button("Download Current Itinerary"):
            if st.session_state.current_itinerary:
                df = pd.DataFrame(st.session_state.current_itinerary)
                st.download_button(
                    label="📥 Download Itinerary",
                    data=df.to_csv(index=False),
                    file_name="my_itinerary.csv",
                    mime="text/csv"
                )
            else:
                st.info("No itinerary created yet!")

    # Footer
    st.markdown("---")
    st.markdown("""
    <div style="text-align: center; color: #666; padding: 2rem;">
        <p>🌍 <strong>AI City Companion</strong> - Your Smart Travel Guide</p>
        <p>Built with ❤️ for safe and smart city navigation | Hackathon MVP 2024</p>
        <p>🚨 Emergency: Police (15) | Rescue (1122) | Fire (16)</p>
    </div>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()