Edit app.py

app.py

@@ -35,9 +35,15 @@ def make_gradcam_heatmap(img_array, model, last_conv_layer_name="conv4", pred_in
     )
     with tf.GradientTape() as tape:
         conv_outputs, predictions = grad_model(img_array)
+
+        # 👇 Aseguramos que predictions sea tensor
+        if isinstance(predictions, list):
+            predictions = predictions[0]
+
         if pred_index is None:
             pred_index = tf.argmax(predictions[0])
         class_channel = predictions[:, pred_index]
+
     grads = tape.gradient(class_channel, conv_outputs)
     pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
     conv_outputs = conv_outputs[0]
@@ -87,7 +93,7 @@ def preprocess_and_predict(img_input):
         f"Simetría Horizontal: {sim_h}"
     )
 
-    # 7. Grad-CAM con ZoomNet (usando la imagen en bruto)
+    # 7. Grad-CAM con ZoomNet (imagen en bruto)
     raw_resized = cv2.resize(np.array(img_input), (ROWS, COLS))
     raw_array = image.img_to_array(raw_resized) / 255.0
     raw_array = np.expand_dims(raw_array, axis=0)