"""
Neighborhood Insights and Crime Rate Analysis
Author: RSK World (https://rskworld.in)
Founded by: Molla Samser
Designer & Tester: Rima Khatun
Contact: info@rskworld.com, +91 93305 39277
Year: 2026
"""

import os
import requests
import json
from typing import Dict, Any, List
from datetime import datetime
import numpy as np

class NeighborhoodAnalyzer:
    def __init__(self):
        self.api_keys = {
            'google_places': os.getenv('GOOGLE_PLACES_API_KEY'),
            'crime_api': os.getenv('CRIME_API_KEY')
        }
    
    def analyze_neighborhood(self, location: str) -> Dict[str, Any]:
        """Comprehensive neighborhood analysis"""
        return {
            'safety_score': self._get_safety_score(location),
            'amenities': self._get_nearby_amenities(location),
            'schools': self._get_school_ratings(location),
            'transport': self._get_transport_info(location),
            'demographics': self._get_demographics(location)
        }
    
    def _get_safety_score(self, location: str) -> float:
        """Get safety score for location (0-10 scale)"""
        # This would typically use crime data APIs
        # For now, return a mock score based on location
        location_lower = location.lower()
        
        # Higher safety scores for major metropolitan areas
        if any(city in location_lower for city in ['mumbai', 'bangalore', 'pune', 'hyderabad']):
            return 7.5
        elif any(city in location_lower for city in ['delhi', 'chennai', 'kolkata']):
            return 7.0
        else:
            return 6.5  # Default score
    
    def _get_nearby_amenities(self, location: str) -> List[Dict[str, Any]]:
        """Get nearby amenities for location"""
        # This would typically use Google Places API
        # For now, return mock data
        amenities = [
            {'type': 'hospital', 'name': 'Nearby Hospital', 'distance': '2 km', 'rating': 4.5},
            {'type': 'school', 'name': 'Local School', 'distance': '1 km', 'rating': 4.2},
            {'type': 'shopping', 'name': 'Shopping Mall', 'distance': '3 km', 'rating': 4.3},
            {'type': 'restaurant', 'name': 'Restaurant Hub', 'distance': '1.5 km', 'rating': 4.4},
            {'type': 'park', 'name': 'Community Park', 'distance': '0.5 km', 'rating': 4.6}
        ]
        return amenities
    
    def _get_school_ratings(self, location: str) -> Dict[str, Any]:
        """Get school ratings for location"""
        # Mock data - would use education APIs in production
        return {
            'primary_schools': [
                {'name': 'Local Primary School', 'rating': 4.3, 'distance': '1 km'},
                {'name': 'Community Primary', 'rating': 4.1, 'distance': '1.5 km'}
            ],
            'secondary_schools': [
                {'name': 'Local Secondary School', 'rating': 4.5, 'distance': '2 km'},
                {'name': 'High School', 'rating': 4.4, 'distance': '2.5 km'}
            ],
            'colleges': [
                {'name': 'Nearby College', 'rating': 4.2, 'distance': '5 km'}
            ],
            'average_rating': 4.3
        }
    
    def _get_transport_info(self, location: str) -> Dict[str, Any]:
        """Get transportation information for location"""
        # Mock data - would use transit APIs in production
        location_lower = location.lower()
        
        has_metro = any(city in location_lower for city in ['delhi', 'bangalore', 'mumbai', 'chennai', 'kolkata', 'hyderabad'])
        
        return {
            'metro_available': has_metro,
            'metro_stations': [
                {'name': 'Nearest Metro Station', 'distance': '2 km', 'line': 'Blue Line'}
            ] if has_metro else [],
            'bus_stops': [
                {'name': 'Bus Stop 1', 'distance': '0.3 km'},
                {'name': 'Bus Stop 2', 'distance': '0.5 km'}
            ],
            'railway_station': {
                'name': 'Nearest Railway Station',
                'distance': '5 km',
                'available': True
            },
            'airport_distance': '25 km',
            'connectivity_score': 8.5 if has_metro else 7.0
        }
    
    def _get_demographics(self, location: str) -> Dict[str, Any]:
        """Get demographic information for location"""
        # Mock demographic data - would use census/statistics APIs in production
        return {
            'population_density': 'Medium',
            'average_age': 32,
            'family_composition': {
                'families_with_children': 65,
                'young_professionals': 25,
                'senior_citizens': 10
            },
            'income_level': 'Upper Middle Class',
            'languages_spoken': ['Hindi', 'English', 'Local Language'],
            'religious_diversity': 'High',
            'education_level': 'Above Average'
        }
    
    def get_location_rating(self, location: str) -> Dict[str, Any]:
        """Get overall location rating with factors"""
        analysis = self.analyze_neighborhood(location)
        
        # Calculate overall rating
        safety_score = analysis['safety_score']
        connectivity_score = analysis['transport'].get('connectivity_score', 7.0)
        amenities_count = len(analysis['amenities'])
        schools_rating = analysis['schools'].get('average_rating', 4.0)
        
        overall_rating = (
            (safety_score / 10) * 0.3 +
            (connectivity_score / 10) * 0.25 +
            (min(amenities_count, 10) / 10) * 0.25 +
            (schools_rating / 5) * 0.2
        ) * 10
        
        return {
            'location': location,
            'overall_rating': round(overall_rating, 2),
            'safety_rating': round(safety_score, 2),
            'connectivity_rating': round(connectivity_score, 1),
            'amenities_rating': round((amenities_count / 10) * 10, 1),
            'schools_rating': round((schools_rating / 5) * 10, 1),
            'factors': analysis,
            'recommendation': 'Excellent' if overall_rating >= 8 else 'Good' if overall_rating >= 7 else 'Average'
        }