Update app.py

app.py

@@ -1,63 +1,122 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import spaces
+from transformers import AutoModel, AutoTokenizer
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
+model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True)
+model = model.eval().cuda()
 
+@spaces.GPU
+def process_image(image, task, ocr_type=None, ocr_box=None, ocr_color=None, render=True):
+    if task == "Plain Text OCR":
+        res = model.chat(tokenizer, image, ocr_type='ocr')
+    elif task == "Format Text OCR":
+        res = model.chat(tokenizer, image, ocr_type='format')
+    elif task == "Fine-grained OCR (Box)":
+        res = model.chat(tokenizer, image, ocr_type=ocr_type, ocr_box=ocr_box)
+    elif task == "Fine-grained OCR (Color)":
+        res = model.chat(tokenizer, image, ocr_type=ocr_type, ocr_color=ocr_color)
+    elif task == "Multi-crop OCR":
+        res = model.chat_crop(tokenizer, image_file=image)
+    elif task == "Render Formatted OCR":
+        res = model.chat(tokenizer, image, ocr_type='format', render=True, save_render_file='./demo.html')
+        with open('./demo.html', 'r') as f:
+            html_content = f.read()
+        return res, html_content
+    
+    return res, None
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def update_inputs(task):
+    if task == "Plain Text OCR" or task == "Format Text OCR" or task == "Multi-crop OCR":
+        return [gr.update(visible=False)] * 4
+    elif task == "Fine-grained OCR (Box)":
+        return [
+            gr.update(visible=True, choices=["ocr", "format"]),
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(visible=False)
+        ]
+    elif task == "Fine-grained OCR (Color)":
+        return [
+            gr.update(visible=True, choices=["ocr", "format"]),
+            gr.update(visible=False),
+            gr.update(visible=True, choices=["red", "green", "blue"]),
+            gr.update(visible=False)
+        ]
+    elif task == "Render Formatted OCR":
+        return [gr.update(visible=False)] * 3 + [gr.update(visible=True)]
+    
+def ocr_demo(image, task, ocr_type, ocr_box, ocr_color, render):
+    res, html_content = process_image(image, task, ocr_type, ocr_box, ocr_color, render)
+    if html_content:
+        return res, html_content
+    return res, None
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+with gr.Blocks() as demo:
+    gr.Markdown()
+    gr.Markdown()
+    gr.Markdown("""
+    # "General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model"
+    
+    "🔥🔥🔥This is the official online demo of GOT-OCR-2.0 model!!!"
+    
+    ### Repo
+    - **Hugging Face**: [ucaslcl/GOT-OCR2_0](https://huggingface.co/ucaslcl/GOT-OCR2_0)
+    - **GitHub**: [ucaslcl/GOT-OCR2_0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/)
+    - **Paper**: [AriXiv](https://arxiv.org/abs/2409.01704)
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="filepath", label="upload your image")
+            task_dropdown = gr.Dropdown(
+                choices=[
+                    "plain text OCR",
+                    "format text OCR",
+                    "fine-grained OCR (Box)",
+                    "fine-grained OCR (Color)",
+                    "multi-crop OCR",
+                    "render Formatted OCR"
+                ],
+                label="Select Task",
+                value="Plain Text OCR"
+            )
+            ocr_type_dropdown = gr.Dropdown(
+                choices=["ocr", "format"],
+                label="OCR Type",
+                visible=False
+            )
+            ocr_box_input = gr.Textbox(
+                label="OCR Box (x1,y1,x2,y2)",
+                placeholder="e.g., 100,100,200,200",
+                visible=False
+            )
+            ocr_color_dropdown = gr.Dropdown(
+                choices=["red", "green", "blue"],
+                label="OCR Color",
+                visible=False
+            )
+            render_checkbox = gr.Checkbox(
+                label="Render Result",
+                visible=False
+            )
+            submit_button = gr.Button("Process")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="OCR Result")
+            output_html = gr.HTML(label="Rendered HTML Output")
+    
 
-    messages.append({"role": "user", "content": message})
+    task_dropdown.change(
+        update_inputs,
+        inputs=[task_dropdown],
+        outputs=[ocr_type_dropdown, ocr_box_input, ocr_color_dropdown, render_checkbox]
+    )
+    
+    submit_button.click(
+        ocr_demo,
+        inputs=[image_input, task_dropdown, ocr_type_dropdown, ocr_box_input, ocr_color_dropdown],
+        outputs=[output_text, output_html]
+    )
 
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()