add app.py

app.py

@@ -1,7 +1,108 @@
+from typing import Optional
+
 import gradio as gr
+import numpy as np
+import torch
+from PIL import Image
+import io
+
+
+import base64, os
+from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img
+import torch
+from PIL import Image
+
+yolo_model = get_yolo_model(model_path='weights/icon_detect/best.pt')
+caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence")
+platform = 'pc'
+if platform == 'pc':
+    draw_bbox_config = {
+        'text_scale': 0.8,
+        'text_thickness': 2,
+        'text_padding': 2,
+        'thickness': 2,
+    }
+elif platform == 'web':
+    draw_bbox_config = {
+        'text_scale': 0.8,
+        'text_thickness': 2,
+        'text_padding': 3,
+        'thickness': 3,
+    }
+elif platform == 'mobile':
+    draw_bbox_config = {
+        'text_scale': 0.8,
+        'text_thickness': 2,
+        'text_padding': 3,
+        'thickness': 3,
+    }
+
+
+
+MARKDOWN = """
+# OmniParser for Pure Vision Based General GUI Agent 🔥
+<div>
+    <a href="https://arxiv.org/pdf/2408.00203">
+        <img src="https://img.shields.io/badge/arXiv-2408.00203-b31b1b.svg" alt="Arxiv" style="display:inline-block;">
+    </a>
+</div>
+
+OmniParser is a screen parsing tool to convert general GUI screen to structured elements. 
+"""
+
+DEVICE = torch.device('cuda')
+
+# @spaces.GPU
+# @torch.inference_mode()
+# @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
+def process(
+    image_input,
+    box_threshold,
+    iou_threshold
+) -> Optional[Image.Image]:
+
+    image_save_path = 'imgs/saved_image_demo.png'
+    image_input.save(image_save_path)
+    # import pdb; pdb.set_trace()
+
+    ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_save_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9})
+    text, ocr_bbox = ocr_bbox_rslt
+    # print('prompt:', prompt)
+    dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold)
+    image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
+    print('finish processing')
+    parsed_content_list = '\n'.join(parsed_content_list)
+    return image, str(parsed_content_list)
+
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(MARKDOWN)
+    with gr.Row():
+        with gr.Column():
+            image_input_component = gr.Image(
+                type='pil', label='Upload image')
+            # set the threshold for removing the bounding boxes with low confidence, default is 0.05
+            box_threshold_component = gr.Slider(
+                label='Box Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.05)
+            # set the threshold for removing the bounding boxes with large overlap, default is 0.1
+            iou_threshold_component = gr.Slider(
+                label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1)
+            submit_button_component = gr.Button(
+                value='Submit', variant='primary')
+        with gr.Column():
+            image_output_component = gr.Image(type='pil', label='Image Output')
+            text_output_component = gr.Textbox(label='Parsed screen elements', placeholder='Text Output')
 
-def greet(name):
-    return "Hello " + name + "!!"
+    submit_button_component.click(
+        fn=process,
+        inputs=[
+            image_input_component,
+            box_threshold_component,
+            iou_threshold_component
+        ],
+        outputs=[image_output_component, text_output_component]
+    )
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# demo.launch(debug=False, show_error=True, share=True)
+demo.launch(share=True, server_port=7861, server_name='0.0.0.0')