Update app.py

app.py

@@ -27,7 +27,6 @@ def detect_face_opencv(pil_image):
     (x, y, w, h) = max(faces, key=lambda b: b[2]*b[3])
     return pil_image.crop((x, y, x+w, y+h))
 
-# Grad-CAM visualization (Xception only)
 def grad_cam(model, img, size, preprocess_func):
     img_resized = img.resize(size)
     x = img_to_array(img_resized)
@@ -39,8 +38,9 @@ def grad_cam(model, img, size, preprocess_func):
     with tf.GradientTape() as tape:
         conv_outputs, predictions = grad_model(x_tensor)
         loss = predictions[:, 0]
-    grads = tape.gradient(loss, conv_outputs)[0]
-    cam = np.mean(grads, axis=-1)
+    grads = tape.gradient(loss, conv_outputs)
+    cam = tf.reduce_mean(grads, axis=-1).numpy()[0]
+
     cam = np.maximum(cam, 0)
     cam /= cam.max() if cam.max() != 0 else 1
     heatmap = cv2.resize(cam, size)
@@ -53,6 +53,7 @@ def grad_cam(model, img, size, preprocess_func):
     superimposed = cv2.addWeighted(img_np, 0.6, heatmap, 0.4, 0)
     return Image.fromarray(cv2.cvtColor(superimposed, cv2.COLOR_BGR2RGB))
 
+
 # Preprocessing helper
 def preprocess(img, size, func):
     img = img.resize(size)