app.py

from flask import Flask, render_template, request, jsonify
import cv2
import numpy as np
import base64
import io
from PIL import Image
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)

# Initialize face detector
try:
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
    logger.info("Face cascade classifier loaded successfully")
except Exception as e:
    logger.error(f"Error loading face cascade: {e}")
    face_cascade = None

def detect_faces(image_data, scale_factor=1.1):
    """Detect faces in image and return results"""
    try:
        if face_cascade is None:
            raise Exception("Face detector not initialized")
            
        # Convert base64 image to numpy array
        image_data = image_data.split(',')[1]  # Remove data:image/jpeg;base64,
        image_bytes = base64.b64decode(image_data)
        image = Image.open(io.BytesIO(image_bytes))
        image_np = np.array(image)
        
        # Convert to grayscale for face detection
        gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
        
        # Detect faces
        faces = face_cascade.detectMultiScale(
            gray_image, 
            scaleFactor=scale_factor, 
            minNeighbors=5, 
            minSize=(30, 30)
        )
        
        # Draw bounding boxes and labels
        for (x, y, w, h) in faces:
            cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2)
            cv2.putText(image_np, f"Face", (x, y-10), 
                       cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
        
        # Convert back to base64
        result_image = Image.fromarray(image_np)
        buffered = io.BytesIO()
        result_image.save(buffered, format="JPEG")
        result_base64 = base64.b64encode(buffered.getvalue()).decode()
        
        # Simple age/gender estimation (placeholder)
        results = []
        for i, (x, y, w, h) in enumerate(faces):
            import random
            ages = ["20-25", "26-32", "33-40", "41-50", "51-60"]
            genders = ["Male", "Female"]
            
            results.append({
                'id': i + 1,
                'age': random.choice(ages),
                'gender': random.choice(genders),
                'position': {'x': int(x), 'y': int(y), 'width': int(w), 'height': int(h)}
            })
        
        return f"data:image/jpeg;base64,{result_base64}", results
    
    except Exception as e:
        logger.error(f"Error in detect_faces: {e}")
        raise e

@app.route('/')
def index():
    logger.info("Index page accessed")
    return render_template('index.html')

@app.route('/detect', methods=['POST'])
def detect():
    try:
        data = request.json
        image_data = data['image']
        scale_factor = float(data.get('scale', 1.1))
        
        result_image, face_data = detect_faces(image_data, scale_factor)
        
        return jsonify({
            'success': True,
            'result_image': result_image,
            'faces_detected': len(face_data),
            'face_data': face_data
        })
    except Exception as e:
        logger.error(f"Error in detect endpoint: {e}")
        return jsonify({
            'success': False,
            'error': str(e)
        })

@app.route('/health')
def health():
    return jsonify({'status': 'healthy', 'face_detector_loaded': face_cascade is not None})

if __name__ == '__main__':
    logger.info("Starting Flask application...")
    app.run(host='0.0.0.0', port=5000, debug=False)