Update app.py

app.py

@@ -1,22 +1,87 @@
-import numpy as np
 import cv2
-import gradio as gr
+import numpy as np
+from flask import Flask, render_template, request, jsonify, send_file
 from PIL import Image
+import io
+import base64
+
+app = Flask(__name__)
+
+# Initialize face detector
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+def detect_faces(image_data, scale_factor=1.1):
+    """Detect faces in image and return results"""
+    # 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
+    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):
+        # Very basic mock data
+        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
+
+@app.route('/')
+def index():
+    return render_template('index.html')
 
-def detect_faces(image):
-     image_np = np.array(image)
-     gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
-     face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +  "haarcascade_frontalface_default.xml")
-     faces = face_cascade.detectMultiScale(gray_image,  scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-     for (x, y, w, h) in faces:
-         cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2)
-     return image_np
+@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:
+        return jsonify({
+            'success': False,
+            'error': str(e)
+        })
 
-iface= gr.Interface(
-     fn  = detect_faces,
-     inputs   ="image",
-     outputs  ="image",
-     title    ="Face Detection",
-     description ="Upload an image, and the model will detect faces and draw bounding boxes around  them.",
- )
-iface.launch()
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=5000)