Add application file

app.py

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+"""
+Crime Hotspot Prediction API Server
+Backend Flask application for serving crime predictions and hotspot data
+"""
+
+import os
+import sys
+from datetime import datetime, timedelta
+
+import numpy as np
+import pandas as pd
+from flask import Flask, jsonify, request
+from flask_cors import CORS
+
+# Add backend code directory to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'code'))
+
+app = Flask(__name__)
+CORS(app)
+
+# Configuration
+app.config['JSON_SORT_KEYS'] = False
+BACKEND_PORT = int(os.getenv('BACKEND_PORT', 5000))
+BACKEND_DEBUG = os.getenv('BACKEND_DEBUG', 'False') == 'True'
+
+# Mock data generators (replace with actual model inference later)
+def generate_mock_hotspots(city: str, threshold: float = 0.5):
+    """Generate mock hotspot data for demonstration"""
+    cities_data = {
+        'bangalore': {
+            'center': [12.9716, 77.5946],
+            'hotspots': [
+                {'lat': 12.9352, 'lon': 77.6245, 'risk': 0.85},
+                {'lat': 12.9716, 'lon': 77.5946, 'risk': 0.72},
+                {'lat': 12.935, 'lon': 77.62, 'risk': 0.68},
+                {'lat': 13.0027, 'lon': 77.5914, 'risk': 0.61},
+                {'lat': 12.9142, 'lon': 77.6391, 'risk': 0.55},
+            ]
+        },
+        'delhi': {
+            'center': [28.7041, 77.1025],
+            'hotspots': [
+                {'lat': 28.7041, 'lon': 77.1025, 'risk': 0.89},
+                {'lat': 28.6328, 'lon': 77.2197, 'risk': 0.76},
+            ]
+        },
+        'mumbai': {
+            'center': [19.0760, 72.8777],
+            'hotspots': [
+                {'lat': 19.0760, 'lon': 72.8777, 'risk': 0.82},
+                {'lat': 19.0176, 'lon': 72.8479, 'risk': 0.71},
+            ]
+        },
+        'newyork': {
+            'center': [40.7128, -74.0060],
+            'hotspots': [
+                {'lat': 40.7128, 'lon': -74.0060, 'risk': 0.88},
+                {'lat': 40.7580, 'lon': -73.9855, 'risk': 0.75},
+                {'lat': 40.6892, 'lon': -74.0445, 'risk': 0.63},
+            ]
+        }
+    }
+
+    city_data = cities_data.get(city.lower(), cities_data['bangalore'])
+    hotspots = []
+
+    for idx, hs in enumerate(city_data['hotspots']):
+        if hs['risk'] >= threshold:
+            risk_level = 'high' if hs['risk'] >= 0.75 else ('medium' if hs['risk'] >= 0.6 else 'low')
+            hotspots.append({
+                'id': f'hotspot-{city}-{idx}',
+                'latitude': hs['lat'],
+                'longitude': hs['lon'],
+                'riskLevel': risk_level,
+                'crimeCount': int(50 * hs['risk']) + 10,
+            })
+
+    return hotspots
+
+def generate_mock_statistics(city: str):
+    """Generate mock statistics for demonstration"""
+    hotspots = generate_mock_hotspots(city, threshold=0.0)
+    
+    total_crimes = sum(hs['crimeCount'] for hs in hotspots)
+    avg_risk = np.mean([0.85, 0.72, 0.68, 0.61, 0.55])
+    
+    # Generate mock time series data
+    time_series = []
+    base_date = datetime.now() - timedelta(days=30)
+    for i in range(30):
+        date = (base_date + timedelta(days=i)).strftime('%Y-%m-%d')
+        crimes = int(total_crimes / 30 + np.random.normal(0, 5))
+        predicted = int(total_crimes / 30 + np.random.normal(0, 3))
+        time_series.append({
+            'date': date,
+            'crimes': max(0, crimes),
+            'predicted': max(0, predicted)
+        })
+
+    return {
+        'hotspotsCount': len(hotspots),
+        'totalCrimes': total_crimes,
+        'averageRiskLevel': float(avg_risk),
+        'predictionAccuracy': 0.85,
+        'timeSeriesData': time_series
+    }
+
+# API Routes
+
+@app.route('/api/health', methods=['GET'])
+def health():
+    """Health check endpoint"""
+    return jsonify({
+        'status': 'healthy',
+        'timestamp': datetime.now().isoformat()
+    })
+
+@app.route('/api/hotspots', methods=['GET'])
+def get_hotspots():
+    """Get crime hotspots for a city and threshold"""
+    try:
+        city = request.args.get('city', 'bangalore')
+        threshold = float(request.args.get('threshold', 0.5))
+        date = request.args.get('date')
+
+        hotspots = generate_mock_hotspots(city, threshold)
+
+        return jsonify({
+            'city': city,
+            'threshold': threshold,
+            'date': date or datetime.now().strftime('%Y-%m-%d'),
+            'hotspots': hotspots,
+            'count': len(hotspots)
+        })
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/predictions', methods=['GET'])
+def get_predictions():
+    """Get model predictions for a city"""
+    try:
+        city = request.args.get('city', 'bangalore')
+        time_window = request.args.get('timeWindow', 'current')
+
+        # In production, this would call the actual ML model
+        predictions = {
+            'city': city,
+            'timeWindow': time_window,
+            'timestamp': datetime.now().isoformat(),
+            'data': generate_mock_hotspots(city, 0.0)
+        }
+
+        return jsonify(predictions)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/statistics', methods=['GET'])
+def get_statistics():
+    """Get statistics and insights for a city"""
+    try:
+        city = request.args.get('city', 'bangalore')
+
+        stats = generate_mock_statistics(city)
+
+        return jsonify(stats)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/train', methods=['POST'])
+def train_model():
+    """Trigger model retraining (placeholder)"""
+    try:
+        data = request.get_json()
+        city = data.get('city', 'bangalore')
+
+        return jsonify({
+            'status': 'training_started',
+            'city': city,
+            'message': 'Model training initiated. Check back later for results.',
+            'timestamp': datetime.now().isoformat()
+        })
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.errorhandler(404)
+def not_found(error):
+    """Handle 404 errors"""
+    return jsonify({'error': 'Endpoint not found'}), 404
+
+@app.errorhandler(500)
+def server_error(error):
+    """Handle 500 errors"""
+    return jsonify({'error': 'Internal server error'}), 500
+
+if __name__ == '__main__':
+    print("Starting Crime Hotspot Prediction API Server...")
+    print(f"Running on http://localhost:{BACKEND_PORT}")
+    app.run(host='0.0.0.0', port=BACKEND_PORT, debug=BACKEND_DEBUG)