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app.py

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+import io
+import os
+import time
+import base64
+# import spaces
+import gradio as gr
+from pathlib import Path
+from transformers import AutoModel, AutoTokenizer
+
+
+# ........................................................................................................
+
+
+from utils.processimage import run_GOT
+
+
+UPLOAD_FOLDER = "./uploads"
+RESULTS_FOLDER = "./results"
+
+for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
+    if not os.path.exists(folder):
+        os.makedirs(folder)
+        
+def image_to_base64(image):
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode()
+
+# ........................................................................................................
+
+def task_update(task):
+    if "fine-grained" in task:
+        return [
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(visible=False),
+        ]
+    else:
+        return [
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+        ]
+
+def fine_grained_update(task):
+    if task == "box":
+        return [
+            gr.update(visible=False, value = ""),
+            gr.update(visible=True),
+        ]
+    elif task == 'color':
+        return [
+            gr.update(visible=True),
+            gr.update(visible=False, value = ""),
+        ]
+
+def cleanup_old_files():
+    current_time = time.time()
+    for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
+        for file_path in Path(folder).glob('*'):
+            if current_time - file_path.stat().st_mtime > 3600:  # 1 hour
+                file_path.unlink()
+                
+
+title_html = """
+<h2> <span class="gradient-text" id="text">General OCR Theory</span><span class="plain-text">Implementation for Demo purposes </span></h2> 
+"""
+
+with gr.Blocks() as demo:
+    gr.HTML(title_html)
+    gr.Markdown("""
+    "This is a demo using G.0.T for Optical Character Recognition "
+    
+    ### Demo Guidelines
+    You need to upload your image below and choose one mode of GOT, then click "Submit" to run GOT model. More characters will result in longer wait times.
+    - **plain texts OCR & format texts OCR**: The two modes are for the image-level OCR.
+    - **plain multi-crop OCR & format multi-crop OCR**: For images with more complex content, you can achieve higher-quality results with these modes.
+    - **plain fine-grained OCR & format fine-grained OCR**: In these modes, you can specify fine-grained regions on the input image for more flexible OCR. Fine-grained regions can be coordinates of the box, red color, blue color, or green color.
+    - **Warning: Please upload the file .jpeg, .jpg, .png format only. Other Format like PDF, .hvec etc do not work and will result in error.**
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="filepath", label="upload the image in .jpeg, .jpg, .png format only")
+            task_dropdown = gr.Dropdown(
+                choices=[
+                    "plain texts OCR",
+                    "format texts OCR",
+                    "plain multi-crop OCR",
+                    "format multi-crop OCR",
+                    "plain fine-grained OCR",
+                    "format fine-grained OCR",
+                ],
+                label="Choose one mode of GOT",
+                value="plain texts OCR"
+            )
+            fine_grained_dropdown = gr.Dropdown(
+                choices=["box", "color"],
+                label="fine-grained type",
+                visible=False
+            )
+            color_dropdown = gr.Dropdown(
+                choices=["red", "green", "blue"],
+                label="color list",
+                visible=False
+            )
+            box_input = gr.Textbox(
+                label="input box: [x1,y1,x2,y2]",
+                placeholder="e.g., [0,0,100,100]",
+                visible=False
+            )
+            submit_button = gr.Button("Submit-Image")
+        
+        with gr.Column():
+            ocr_result = gr.Textbox(label="GOT-OCR output")
+
+    with gr.Column():
+        gr.Markdown("**The mathpix result will be automatically rendered here:**")
+        html_result = gr.HTML(label="rendered html", show_label=True)
+
+
+    task_dropdown.change(
+        task_update,
+        inputs=[task_dropdown],
+        outputs=[fine_grained_dropdown, color_dropdown, box_input]
+    )
+    fine_grained_dropdown.change(
+        fine_grained_update,
+        inputs=[fine_grained_dropdown],
+        outputs=[color_dropdown, box_input]
+    )
+    
+    submit_button.click(
+        run_GOT,
+        inputs=[image_input, task_dropdown, fine_grained_dropdown, color_dropdown, box_input],
+        outputs=[ocr_result, html_result]
+    )
+    
+
+if __name__ == "__main__":
+    cleanup_old_files()
+    demo.launch()

utils/processimage.py

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+import os
+import uuid
+import base64
+import numpy as np
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+import shutil
+
+# ........................................................................................................
+
+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()
+
+UPLOAD_FOLDER = "./uploads"
+RESULTS_FOLDER = "./results"
+
+# for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
+#     if not os.path.exists(folder):
+#         os.makedirs(folder)
+
+# ........................................................................................................
+
+from dotenv import load_dotenv
+token = os.environ.get("HF_TOKEN1")
+
+# ........................................................................................................
+
+def run_GOT(image, got_mode, fine_grained_mode="", ocr_color="", ocr_box=""):
+    unique_id = str(uuid.uuid4())
+    image_path = os.path.join(UPLOAD_FOLDER, f"{unique_id}.png")
+    result_path = os.path.join(RESULTS_FOLDER, f"{unique_id}.html")
+    
+    shutil.copy(image, image_path)
+    
+    try:
+        if got_mode == "plain texts OCR":
+            res = model.chat(tokenizer, image_path, ocr_type='ocr')
+            return res, None
+        elif got_mode == "format texts OCR":
+            res = model.chat(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
+        elif got_mode == "plain multi-crop OCR":
+            res = model.chat_crop(tokenizer, image_path, ocr_type='ocr')
+            return res, None
+        elif got_mode == "format multi-crop OCR":
+            res = model.chat_crop(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
+        elif got_mode == "plain fine-grained OCR":
+            res = model.chat(tokenizer, image_path, ocr_type='ocr', ocr_box=ocr_box, ocr_color=ocr_color)
+            return res, None
+        elif got_mode == "format fine-grained OCR":
+            res = model.chat(tokenizer, image_path, ocr_type='format', ocr_box=ocr_box, ocr_color=ocr_color, render=True, save_render_file=result_path)
+
+        # res_markdown = f"$$ {res} $$"
+        res_markdown = res
+
+        if "format" in got_mode and os.path.exists(result_path):
+            with open(result_path, 'r') as f:
+                html_content = f.read()
+            encoded_html = base64.b64encode(html_content.encode('utf-8')).decode('utf-8')
+            iframe_src = f"data:text/html;base64,{encoded_html}"
+            iframe = f'<iframe src="{iframe_src}" width="100%" height="600px"></iframe>'
+            download_link = f'<a href="data:text/html;base64,{encoded_html}" download="result_{unique_id}.html">Download Full Result</a>'
+            return res_markdown, f"{download_link}<br>{iframe}"
+        else:
+            return res_markdown, None
+    except Exception as e:
+        return f"Error: {str(e)}", None
+    finally:
+        if os.path.exists(image_path):
+            os.remove(image_path)