update app.py

app.py

@@ -12,7 +12,7 @@ else:
     # run_gradio = True
 
 if run_gradio:
-    os.system("pip install gradio==3.40.1")
+    os.system("pip install gradio==3.50.2")
 
 import gradio as gr
 
@@ -346,21 +346,29 @@ def load_lama_cleaner_model(device):
 
 def lama_cleaner_process(image, mask, cleaner_size_limit=1080):
     try:
+        logger.info(f'_______lama_cleaner_process_______1____') 
         ori_image = image
         if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]:
             # rotate image
+            logger.info(f'_______lama_cleaner_process_______2____')
             ori_image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...]
+            logger.info(f'_______lama_cleaner_process_______3____')
             image = ori_image
         
+        logger.info(f'_______lama_cleaner_process_______4____')
         original_shape = ori_image.shape
+        logger.info(f'_______lama_cleaner_process_______5____')
         interpolation = cv2.INTER_CUBIC
         
         size_limit = cleaner_size_limit
         if size_limit == -1:
+            logger.info(f'_______lama_cleaner_process_______6____')
             size_limit = max(image.shape)
         else:
+            logger.info(f'_______lama_cleaner_process_______7____')
             size_limit = int(size_limit)
 
+        logger.info(f'_______lama_cleaner_process_______8____')
         config = lama_Config(
             ldm_steps=25,
             ldm_sampler='plms',
@@ -385,22 +393,30 @@ def lama_cleaner_process(image, mask, cleaner_size_limit=1080):
             cv2_radius=5,
         )
         
+        logger.info(f'_______lama_cleaner_process_______9____')
         if config.sd_seed == -1:
             config.sd_seed = random.randint(1, 999999999)
 
         # logger.info(f"Origin image shape_0_: {original_shape} / {size_limit}")
+        logger.info(f'_______lama_cleaner_process_______10____')
         image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
         # logger.info(f"Resized image shape_1_: {image.shape}")
         
         # logger.info(f"mask image shape_0_: {mask.shape} / {type(mask)}")
+        logger.info(f'_______lama_cleaner_process_______11____')
         mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
         # logger.info(f"mask image shape_1_: {mask.shape} / {type(mask)}")
 
+        logger.info(f'_______lama_cleaner_process_______12____')
         res_np_img = lama_cleaner_model(image, mask, config)
+        logger.info(f'_______lama_cleaner_process_______13____')
         torch.cuda.empty_cache()
     
+        logger.info(f'_______lama_cleaner_process_______14____')
         image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, 'png')))
+        logger.info(f'_______lama_cleaner_process_______15____')
     except Exception as e:
+        logger.info(f'lama_cleaner_process[Error]:' + str(e))
         image = None        
     return  image
 
@@ -863,7 +879,8 @@ def main_gradio(args):
                 if kosmos_enable:
                     task_types.append("Kosmos-2")           
          
-                input_image = gr.Image(source='upload', elem_id="image_upload", tool='sketch', type='pil', label="Upload")    
+                input_image = gr.Image(source='upload', elem_id="image_upload", tool='sketch', type='pil', label="Upload",
+                                    height=512, brush_color='#00FFFF', mask_opacity=0.6)    
                 task_type = gr.Radio(task_types,  value="detection", 
                                                 label='Task type', visible=True) 
                 mask_source_radio = gr.Radio([mask_source_draw, mask_source_segment], 
@@ -894,7 +911,7 @@ def main_gradio(args):
                             remove_mask_extend = gr.Textbox(label="remove_mask_extend", value='10')
 
             with gr.Column():
-                image_gallery = gr.Gallery(label="result images", show_label=True, elem_id="gallery", visible=True
+                image_gallery = gr.Gallery(label="result images", show_label=True, elem_id="gallery", height=512, visible=True
                     ).style(preview=True, columns=[5], object_fit="scale-down", height="auto")   
                 time_cost = gr.Textbox(label="Time cost by step (ms):", visible=False, interactive=False)
 
@@ -1011,8 +1028,10 @@ class API_Starter:
     def handle_data(self, data):
         im_b64 = data['img']
         img = base64_to_PILImage(im_b64)
+        remove_texts = data['remove_texts']
+        remove_mask_extend = data['mask_extend']
         results = run_anything_task(input_image = img, 
-                            text_prompt = data['remove_texts'],  
+                            text_prompt = f"{remove_texts}",  
                             task_type = 'remove', 
                             inpaint_prompt = '', 
                             box_threshold = 0.3, 
@@ -1021,7 +1040,7 @@ class API_Starter:
                             inpaint_mode = "merge", 
                             mask_source_radio = "type what to detect below", 
                             remove_mode = "rectangle",   # ["segment", "rectangle"]
-                            remove_mask_extend = f"{data['mask_extend']}", 
+                            remove_mask_extend = f"{remove_mask_extend}", 
                             num_relation = 5,
                             kosmos_input = None,
                             cleaner_size_limit = -1,
@@ -1107,20 +1126,12 @@ if __name__ == "__main__":
     if os.environ.get('IS_MY_DEBUG') is None:
         os.system("pip list")
 
-    # print(f'groundingdino_model__{get_model_device(groundingdino_model)}')
-    # print(f'sam_model__{get_model_device(sam_model)}')
-    # print(f'sd_model__{get_model_device(sd_model)}')
-    # print(f'lama_cleaner_model__{get_model_device(lama_cleaner_model)}')
-    # print(f'ram_model__{get_model_device(ram_model)}')
-    # print(f'kosmos_model__{get_model_device(kosmos_model)}')    
-
     if run_gradio:
         # Provide gradio services
         main_gradio(args)
     else:        
         # Provide API services
         main_api(args)
- 
-       
+