Update app.py

app.py

@@ -222,7 +222,7 @@ def make_gradcam(image_pil, model, last_conv_layer_name, class_index, progress=N
         img_resized = cv2.resize(img_np, input_size)
         img_array_preprocessed = preprocessor(np.expand_dims(img_resized, axis=0))
 
-        _update_progress(progress, 25, desc="Calcul des gradients...")
+        _update_progress(progress, 25, desc="Calcul des gradients...", sleep=0.005)
 
         try:
             conv_layer = model.get_layer(last_conv_layer_name)
@@ -246,7 +246,7 @@ def make_gradcam(image_pil, model, last_conv_layer_name, class_index, progress=N
         if grads is None:
             return img_resized
 
-        _update_progress(progress, 50, desc="Génération de la heatmap...")
+        _update_progress(progress, 50, desc="Génération de la heatmap...", sleep=0.005)
 
         pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
         last_conv_layer_output = last_conv_layer_output[0]
@@ -263,7 +263,7 @@ def make_gradcam(image_pil, model, last_conv_layer_name, class_index, progress=N
         
         heatmap_np = heatmap.numpy()
 
-        _update_progress(progress, 75, desc="Application de la superposition...", sleep=0.05)
+        _update_progress(progress, 75, desc="Application de la superposition...", sleep=0.005)
 
         heatmap_np = np.clip(heatmap_np.astype(np.float32), 0, 1)
         heatmap_resized = cv2.resize(heatmap_np, (img_resized.shape[1], img_resized.shape[0]))