new app.py

app.py

@@ -28,21 +28,20 @@ if not logger.handlers:
     handler = logging.StreamHandler()
     handler.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s] %(name)s: %(message)s"))
     logger.addHandler(handler)
-logger.warning("here")
 
 # Define repository and local directory
 repo_id = "microsoft/OmniParser-v2.0"  # HF repo
 local_dir = "weights"  # Target local directory
+dtype = torch.bfloat16
+DEVICE = torch.device('cuda')  
 
 som_generator = MarkHelper()
 magma_som_prompt = "<image>\nIn this view I need to click a button to \"{}\"? Provide the coordinates and the mark index of the containing bounding box if applicable."
 magma_qa_prompt = "<image>\n{} Answer the question briefly."
 magma_model_id = "microsoft/Magma-8B"
-magam_model = AutoModelForCausalLM.from_pretrained(magma_model_id, trust_remote_code=True, torch_dtype=torch.bfloat16)
-magma_processor = AutoProcessor.from_pretrained(magma_model_id, trust_remote_code=True, torch_dtype=torch.bfloat16)
-magam_model.to("cuda")
-
-logger.warning(f"The repository is downloading to: {local_dir}")
+magam_model = AutoModelForCausalLM.from_pretrained(magma_model_id, trust_remote_code=True, torch_dtype=dtype)
+magma_processor = AutoProcessor.from_pretrained(magma_model_id, trust_remote_code=True)
+magam_model.to(DEVICE)
 
 # Download the entire repository
 snapshot_download(repo_id=repo_id, local_dir=local_dir)
@@ -58,27 +57,14 @@ MARKDOWN = """
 <div align="center">
 <h2>Magma: A Foundation Model for Multimodal AI Agents</h2>
 
-[Jianwei Yang](https://jwyang.github.io/)<sup>*</sup><sup>1</sup><sup>†</sup>&nbsp;
-[Reuben Tan](https://cs-people.bu.edu/rxtan/)<sup>1</sup><sup>†</sup>&nbsp;
-[Qianhui Wu](https://qianhuiwu.github.io/)<sup>1</sup><sup>†</sup>&nbsp;
-[Ruijie Zheng](https://ruijiezheng.com/)<sup>2</sup><sup>‡</sup>&nbsp;
-[Baolin Peng](https://scholar.google.com/citations?user=u1CNjgwAAAAJ&hl=en&oi=ao)<sup>1</sup><sup>‡</sup>&nbsp;
-[Yongyuan Liang](https://cheryyunl.github.io)<sup>2</sup><sup>‡</sup>
-[Yu Gu](https://users.umiacs.umd.edu/~hal/)<sup>1</sup>&nbsp;
-[Mu Cai](https://pages.cs.wisc.edu/~mucai/)<sup>3</sup>&nbsp;
-[Seonghyeon Ye](https://seonghyeonye.github.io/)<sup>4</sup>&nbsp;
-[Joel Jang](https://joeljang.github.io/)<sup>5</sup>&nbsp;
-[Yuquan Deng](https://scholar.google.com/citations?user=LTC0Q6YAAAAJ&hl=en)<sup>5</sup>&nbsp;
-[Lars Liden](https://sites.google.com/site/larsliden)<sup>1</sup>&nbsp;
-[Jianfeng Gao](https://www.microsoft.com/en-us/research/people/jfgao/)<sup>1</sup><sup>▽</sup>
-
-<sup>1</sup> Microsoft Research; <sup>2</sup> University of Maryland; <sup>3</sup> University of Wisconsin-Madison; <sup>4</sup> KAIST; <sup>5</sup> University of Washington
-
-<sup>*</sup> Project lead  <sup>†</sup> First authors  <sup>‡</sup> Second authors  <sup>▽</sup> Leadership  
-
 \[[arXiv Paper](https://www.arxiv.org/pdf/2502.13130)\] &nbsp; \[[Project Page](https://microsoft.github.io/Magma/)\] &nbsp; \[[Github Repo](https://github.com/microsoft/Magma)\] &nbsp; \[[Hugging Face Model](https://huggingface.co/microsoft/Magma-8B)\] &nbsp; 
 
-This demo is powered by [Gradio](https://gradio.app/) and uses OmniParserv2 to generate Set-of-Mark prompts.
+This demo is powered by [Gradio](https://gradio.app/) and uses [OmniParserv2](https://github.com/microsoft/OmniParser) to generate [Set-of-Mark prompts](https://github.com/microsoft/SoM).
+
+The demo supports three modes:
+1. Empty text inut: it downgrades to an OmniParser demo.
+2. Text input starting with "Q:": it leads to a visual question answering demo.
+3. Text input for UI navigation: it leads to a UI navigation demo.
 </div>
 """
 
@@ -86,7 +72,6 @@ DEVICE = torch.device('cuda')
 
 @spaces.GPU
 @torch.inference_mode()
-@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
 def get_som_response(instruction, image_som):
     prompt = magma_som_prompt.format(instruction)
     if magam_model.config.mm_use_image_start_end:
@@ -101,23 +86,10 @@ def get_som_response(instruction, image_som):
         add_generation_prompt=True
     )
 
-    # inputs = magma_processor(images=[image_som], texts=prompt, return_tensors="pt")
-    # # with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
-    # #     inputs['pixel_values'] = inputs['pixel_values'].unsqueeze(0).to(torch.bfloat16) # Add .to(torch.bfloat16) here for explicit casting
-    # #     inputs['image_sizes'] = inputs['image_sizes'].unsqueeze(0)
-    # #     logger.warning(inputs['pixel_values'].dtype)
-    # #     # inputs = inputs.to("cuda")
-    # inputs = inputs.to("cuda", dtype=torch.bfloat16)
-
     inputs = magma_processor(images=[image_som], texts=prompt, return_tensors="pt")
-    inputs['pixel_values'] = inputs['pixel_values'].to("cuda", dtype=torch.bfloat16)
+    inputs['pixel_values'] = inputs['pixel_values'].unsqueeze(0)
     inputs['image_sizes'] = inputs['image_sizes'].unsqueeze(0)
-    inputs['image_sizes'] = inputs['image_sizes'].to("cuda")
-
-    # 处理其他可能的输入
-    for key in inputs:
-        if key not in ['pixel_values', 'image_sizes'] and torch.is_tensor(inputs[key]):
-            inputs[key] = inputs[key].to("cuda")
+    inputs = inputs.to(dtype).to(DEVICE)
 
     magam_model.generation_config.pad_token_id = magma_processor.tokenizer.pad_token_id
     with torch.inference_mode():
@@ -137,7 +109,6 @@ def get_som_response(instruction, image_som):
 
 @spaces.GPU
 @torch.inference_mode()
-@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
 def get_qa_response(instruction, image):
     prompt = magma_qa_prompt.format(instruction)
     if magam_model.config.mm_use_image_start_end:
@@ -152,12 +123,10 @@ def get_qa_response(instruction, image):
         add_generation_prompt=True
     )
 
-    with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
-        inputs = magma_processor(images=[image], texts=prompt, return_tensors="pt")
-        inputs['pixel_values'] = inputs['pixel_values'].unsqueeze(0).to(torch.bfloat16) # Add .to(torch.bfloat16) here for explicit casting
-        inputs['image_sizes'] = inputs['image_sizes'].unsqueeze(0)
-        # inputs = inputs.to("cuda")
-        inputs = inputs.to("cuda", dtype=torch.bfloat16)
+    inputs = magma_processor(images=[image], texts=prompt, return_tensors="pt")
+    inputs['pixel_values'] = inputs['pixel_values'].unsqueeze(0)
+    inputs['image_sizes'] = inputs['image_sizes'].unsqueeze(0)
+    inputs = inputs.to(dtype).to(DEVICE)
 
     magam_model.generation_config.pad_token_id = magma_processor.tokenizer.pad_token_id
     with torch.inference_mode():
@@ -177,7 +146,7 @@ def get_qa_response(instruction, image):
 
 @spaces.GPU
 @torch.inference_mode()
-@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
+# @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
 def process(
     image_input,
     box_threshold,
@@ -187,107 +156,99 @@ def process(
     instruction,
 ) -> Optional[Image.Image]:
 
-    logger.warning("Starting processing.")
-    try:
-        # image_save_path = 'imgs/saved_image_demo.png'
-        # image_input.save(image_save_path)
-        # image = Image.open(image_save_path)
-        box_overlay_ratio = image_input.size[0] / 3200
-        draw_bbox_config = {
-            'text_scale': 0.8 * box_overlay_ratio,
-            'text_thickness': max(int(2 * box_overlay_ratio), 1),
-            'text_padding': max(int(3 * box_overlay_ratio), 1),
-            'thickness': max(int(3 * box_overlay_ratio), 1),
-        }
-
-        ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_input, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}, use_paddleocr=use_paddleocr)
-        text, ocr_bbox = ocr_bbox_rslt
-        dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_input, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold, imgsz=imgsz,)  
-        parsed_content_list = '\n'.join([f'icon {i}: ' + str(v) for i,v in enumerate(parsed_content_list)])
-        
-        if len(instruction) == 0:
-            logger.warning('finish processing')
-            image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
-            return image, str(parsed_content_list)
-
-        elif instruction.startswith('Q:'):
-            response = get_qa_response(instruction, image_input)
-            return image_input, response
-
-        # parsed_content_list = str(parsed_content_list)
-        # convert xywh to yxhw
-        label_coordinates_yxhw = {}
-        for key, val in label_coordinates.items():
-            if val[2] < 0 or val[3] < 0:
-                continue
-            label_coordinates_yxhw[key] = [val[1], val[0], val[3], val[2]]
-        image_som = plot_boxes_with_marks(image_input.copy(), [val for key, val in label_coordinates_yxhw.items()], som_generator, edgecolor=(255,0,0), fn_save=None, normalized_to_pixel=False)
-
-        # convert xywh to xyxy
-        for key, val in label_coordinates.items():
-            label_coordinates[key] = [val[0], val[1], val[0] + val[2], val[1] + val[3]]
-
-        # normalize label_coordinates
-        for key, val in label_coordinates.items():
-            label_coordinates[key] = [val[0] / image_input.size[0], val[1] / image_input.size[1], val[2] / image_input.size[0], val[3] / image_input.size[1]]
-        
-        magma_response = get_som_response(instruction, image_som)
-        logger.warning("magma repsonse: ", magma_response)
-
-        # map magma_response into the mark id
-        mark_id = extract_mark_id(magma_response)
-        if mark_id is not None:
-            if str(mark_id) in label_coordinates:
-                bbox_for_mark = label_coordinates[str(mark_id)]
-            else:
-                bbox_for_mark = None
+    # image_save_path = 'imgs/saved_image_demo.png'
+    # image_input.save(image_save_path)
+    # image = Image.open(image_save_path)
+    box_overlay_ratio = image_input.size[0] / 3200
+    draw_bbox_config = {
+        'text_scale': 0.8 * box_overlay_ratio,
+        'text_thickness': max(int(2 * box_overlay_ratio), 1),
+        'text_padding': max(int(3 * box_overlay_ratio), 1),
+        'thickness': max(int(3 * box_overlay_ratio), 1),
+    }
+
+    ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_input, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}, use_paddleocr=use_paddleocr)
+    text, ocr_bbox = ocr_bbox_rslt
+    dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_input, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold, imgsz=imgsz,)  
+    parsed_content_list = '\n'.join([f'icon {i}: ' + str(v) for i,v in enumerate(parsed_content_list)])
+    
+    if len(instruction) == 0:
+        logger.warning('finish processing')
+        image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
+        return image, str(parsed_content_list)
+
+    elif instruction.startswith('Q:'):
+        response = get_qa_response(instruction, image_input)
+        return image_input, response
+
+    # parsed_content_list = str(parsed_content_list)
+    # convert xywh to yxhw
+    label_coordinates_yxhw = {}
+    for key, val in label_coordinates.items():
+        if val[2] < 0 or val[3] < 0:
+            continue
+        label_coordinates_yxhw[key] = [val[1], val[0], val[3], val[2]]
+    image_som = plot_boxes_with_marks(image_input.copy(), [val for key, val in label_coordinates_yxhw.items()], som_generator, edgecolor=(255,0,0), fn_save=None, normalized_to_pixel=False)
+
+    # convert xywh to xyxy
+    for key, val in label_coordinates.items():
+        label_coordinates[key] = [val[0], val[1], val[0] + val[2], val[1] + val[3]]
+
+    # normalize label_coordinates
+    for key, val in label_coordinates.items():
+        label_coordinates[key] = [val[0] / image_input.size[0], val[1] / image_input.size[1], val[2] / image_input.size[0], val[3] / image_input.size[1]]
+    
+    magma_response = get_som_response(instruction, image_som)
+    logger.warning("magma repsonse: ", magma_response)
+
+    # map magma_response into the mark id
+    mark_id = extract_mark_id(magma_response)
+    if mark_id is not None:
+        if str(mark_id) in label_coordinates:
+            bbox_for_mark = label_coordinates[str(mark_id)]
         else:
             bbox_for_mark = None
-        
-        if bbox_for_mark:
-            # draw bbox_for_mark on the image
-            image_som = plot_boxes_with_marks(
+    else:
+        bbox_for_mark = None
+    
+    if bbox_for_mark:
+        # draw bbox_for_mark on the image
+        image_som = plot_boxes_with_marks(
+            image_input, 
+            [label_coordinates_yxhw[str(mark_id)]], 
+            som_generator, 
+            edgecolor=(255,127,111), 
+            alpha=30, 
+            fn_save=None, 
+            normalized_to_pixel=False,
+            add_mark=False
+        )
+    else:
+        try:
+            if 'box' in magma_response:
+                pred_bbox = extract_bbox(magma_response)
+                click_point = [(pred_bbox[0][0] + pred_bbox[1][0]) / 2, (pred_bbox[0][1] + pred_bbox[1][1]) / 2]
+                click_point = [item / 1000 for item in click_point]
+            else:
+                click_point = pred_2_point(magma_response)
+            # de-normalize click_point (width, height)
+            click_point = [click_point[0] * image_input.size[0], click_point[1] * image_input.size[1]]
+
+            image_som = plot_circles_with_marks(
                 image_input, 
-                [label_coordinates_yxhw[str(mark_id)]], 
-                som_generator, 
+                [click_point],
+                som_generator,
                 edgecolor=(255,127,111), 
-                alpha=30, 
-                fn_save=None, 
+                linewidth=3,
+                fn_save=None,
                 normalized_to_pixel=False,
                 add_mark=False
             )
-        else:
-            try:
-                if 'box' in magma_response:
-                    pred_bbox = extract_bbox(magma_response)
-                    click_point = [(pred_bbox[0][0] + pred_bbox[1][0]) / 2, (pred_bbox[0][1] + pred_bbox[1][1]) / 2]
-                    click_point = [item / 1000 for item in click_point]
-                else:
-                    click_point = pred_2_point(magma_response)
-                # de-normalize click_point (width, height)
-                click_point = [click_point[0] * image_input.size[0], click_point[1] * image_input.size[1]]
-
-                image_som = plot_circles_with_marks(
-                    image_input, 
-                    [click_point],
-                    som_generator,
-                    edgecolor=(255,127,111), 
-                    linewidth=3,
-                    fn_save=None,
-                    normalized_to_pixel=False,
-                    add_mark=False
-                )
-            except:
-                image_som = image_input
-
-        logger.warning("finish processing")
-        return image_som, str(parsed_content_list)
-    except Exception as e:
-        error_message = traceback.format_exc()
-        logger.warning(error_message)
-        return image_input, error_message
-
-logger.warning("Starting App.")
+        except:
+            image_som = image_input
+    
+    return image_som, str(parsed_content_list)
+
 with gr.Blocks() as demo:
     gr.Markdown(MARKDOWN)
     with gr.Row():
@@ -326,6 +287,6 @@ with gr.Blocks() as demo:
         outputs=[image_output_component, text_output_component]
     )
 
-# demo.launch(debug=True, show_error=True, share=True)
+# demo.launch(debug=False, show_error=True, share=True)
 # demo.launch(share=True, server_port=7861, server_name='0.0.0.0')
-demo.queue().launch(share=False)
+demo.queue().launch(share=False)