Update app.py

app.py

@@ -1,9 +1,9 @@
+from flask import Flask, render_template, request, jsonify
 import cv2
 import numpy as np
-from flask import Flask, render_template, request, jsonify, send_file
-from PIL import Image
-import io
 import base64
+import io
+from PIL import Image
 
 app = Flask(__name__)
 
@@ -12,51 +12,55 @@ face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_fronta
 
 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"]
+    try:
+        # 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)
         
-        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)}
-        })
+        # 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):
+            # Very basic mock data - in production, use proper models
+            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
     
-    return f"data:image/jpeg;base64,{result_base64}", results
+    except Exception as e:
+        raise e
 
 @app.route('/')
 def index():
@@ -84,4 +88,4 @@ def detect():
         })
 
 if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=5000)
+    app.run(host='0.0.0.0', port=5000, debug=False)