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| # import gradio as gr | |
| # print("GRADIO VERSION:", gr.__version__) | |
| # import json | |
| # import os | |
| # import tempfile | |
| # from pathlib import Path | |
| # # NOTE: You must ensure that 'working_yolo_pipeline.py' exists | |
| # # and defines the following items correctly: | |
| # from working_yolo_pipeline import run_document_pipeline, DEFAULT_LAYOUTLMV3_MODEL_PATH, WEIGHTS_PATH | |
| # # Since I don't have this file, I am assuming the imports are correct. | |
| # # Define placeholders for assumed constants if the pipeline file isn't present | |
| # # You should replace these with your actual definitions if they are missing | |
| # try: | |
| # from working_yolo_pipeline import run_document_pipeline, DEFAULT_LAYOUTLMV3_MODEL_PATH, WEIGHTS_PATH | |
| # except ImportError: | |
| # print("Warning: 'working_yolo_pipeline.py' not found. Using dummy paths.") | |
| # def run_document_pipeline(*args): | |
| # return {"error": "Placeholder pipeline function called."} | |
| # DEFAULT_LAYOUTLMV3_MODEL_PATH = "./models/layoutlmv3_model" | |
| # WEIGHTS_PATH = "./weights/yolo_weights.pt" | |
| # def process_pdf(pdf_file, layoutlmv3_model_path=None): | |
| # """ | |
| # Wrapper function for Gradio interface. | |
| # Args: | |
| # pdf_file: Gradio UploadButton file object | |
| # layoutlmv3_model_path: Optional custom model path | |
| # Returns: | |
| # Tuple of (JSON string, download file path) | |
| # """ | |
| # if pdf_file is None: | |
| # return "❌ Error: No PDF file uploaded.", None | |
| # # Use default model path if not provided | |
| # if not layoutlmv3_model_path: | |
| # layoutlmv3_model_path = DEFAULT_LAYOUTLMV3_MODEL_PATH | |
| # # Verify model and weights exist | |
| # if not os.path.exists(layoutlmv3_model_path): | |
| # return f"❌ Error: LayoutLMv3 model not found at {layoutlmv3_model_path}", None | |
| # if not os.path.exists(WEIGHTS_PATH): | |
| # return f"❌ Error: YOLO weights not found at {WEIGHTS_PATH}", None | |
| # try: | |
| # # Get the uploaded PDF path | |
| # pdf_path = pdf_file.name | |
| # # Run the pipeline | |
| # result = run_document_pipeline(pdf_path, layoutlmv3_model_path, 'label_studio_import.json') | |
| # if result is None: | |
| # return "❌ Error: Pipeline failed to process the PDF. Check console for details.", None | |
| # # Create a temporary file for download | |
| # output_filename = f"{Path(pdf_path).stem}_analysis.json" | |
| # temp_output = tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json', prefix='analysis_') | |
| # # Dump results to the temporary file | |
| # with open(temp_output.name, 'w', encoding='utf-8') as f: | |
| # json.dump(result, f, indent=2, ensure_ascii=False) | |
| # # Format JSON for display | |
| # json_display = json.dumps(result, indent=2, ensure_ascii=False) | |
| # return json_display, temp_output.name | |
| # except Exception as e: | |
| # return f"❌ Error during processing: {str(e)}", None | |
| # # Create Gradio interface | |
| # # FIX APPLIED: Removed 'theme=gr.themes.Soft()' which caused the TypeError | |
| # with gr.Blocks(title="Document Analysis Pipeline") as demo: | |
| # gr.Markdown(""" | |
| # # 📄 Document Analysis Pipeline | |
| # Upload a PDF document to extract structured data including questions, options, answers, passages, and embedded images. | |
| # **Pipeline Steps:** | |
| # 1. 🔍 YOLO/OCR Preprocessing (word extraction + figure/equation detection) | |
| # 2. 🤖 LayoutLMv3 Inference (BIO tagging) | |
| # 3. 📊 Structured JSON Decoding | |
| # 4. 🖼️ Base64 Image Embedding | |
| # """) | |
| # with gr.Row(): | |
| # with gr.Column(scale=1): | |
| # pdf_input = gr.File( | |
| # label="Upload PDF Document", | |
| # file_types=[".pdf"], | |
| # type="filepath" | |
| # ) | |
| # model_path_input = gr.Textbox( | |
| # label="LayoutLMv3 Model Path (optional)", | |
| # placeholder=DEFAULT_LAYOUTLMV3_MODEL_PATH, | |
| # value=DEFAULT_LAYOUTLMV3_MODEL_PATH, | |
| # interactive=True | |
| # ) | |
| # process_btn = gr.Button("🚀 Process Document", variant="primary", size="lg") | |
| # gr.Markdown(""" | |
| # ### ℹ️ Notes: | |
| # - Processing may take several minutes depending on PDF size | |
| # - Figures and equations will be extracted and embedded as Base64 | |
| # - The output JSON includes structured questions, options, and answers | |
| # """) | |
| # with gr.Column(scale=2): | |
| # json_output = gr.Code( | |
| # label="Structured JSON Output", | |
| # language="json", | |
| # lines=25 | |
| # ) | |
| # download_output = gr.File( | |
| # label="Download Full JSON", | |
| # interactive=False | |
| # ) | |
| # # Status/Examples section | |
| # with gr.Row(): | |
| # gr.Markdown(""" | |
| # ### 📋 Output Format | |
| # The pipeline generates JSON with the following structure: | |
| # - **Questions**: Extracted question text | |
| # - **Options**: Multiple choice options (A, B, C, D, etc.) | |
| # - **Answers**: Correct answer(s) | |
| # - **Passages**: Associated reading passages | |
| # - **Images**: Base64-encoded figures and equations (embedded with keys like `figure1`, `equation2`) | |
| # """) | |
| # # Connect the button to the processing function | |
| # process_btn.click( | |
| # fn=process_pdf, | |
| # inputs=[pdf_input, model_path_input], | |
| # outputs=[json_output, download_output], | |
| # api_name="process_document" | |
| # ) | |
| # # Example section (optional - add example PDFs if available) | |
| # # gr.Examples( | |
| # # examples=[ | |
| # # ["examples/sample1.pdf"], | |
| # # ["examples/sample2.pdf"], | |
| # # ], | |
| # # inputs=pdf_input, | |
| # # ) | |
| # # Launch the app | |
| # if __name__ == "__main__": | |
| # demo.launch( | |
| # server_name="0.0.0.0", | |
| # server_port=7860, | |
| # share=False, | |
| # show_error=True | |
| # ) | |
| import gradio as gr | |
| print("GRADIO VERSION:", gr.__version__) | |
| import json | |
| import os | |
| import tempfile | |
| from pathlib import Path | |
| # ============================== | |
| # WRITE CUSTOM CSS FOR FONTS | |
| # ============================== | |
| # CUSTOM_CSS = """ | |
| # @font-face { | |
| # font-family: 'NotoSansMath'; | |
| # src: url('./NotoSansMath-Regular.ttf') format('truetype'); | |
| # font-weight: normal; | |
| # font-style: normal; | |
| # } | |
| # html, body, * { | |
| # font-family: 'NotoSansMath', sans-serif !important; | |
| # } | |
| # """ | |
| # # Optionally write the CSS file if needed (not required for inline css) | |
| # if not os.path.exists("custom.css"): | |
| # with open("custom.css", "w") as f: | |
| # f.write(CUSTOM_CSS) | |
| # ============================== | |
| try: | |
| from working_yolo_pipeline import run_document_pipeline, DEFAULT_LAYOUTLMV3_MODEL_PATH, WEIGHTS_PATH | |
| except ImportError: | |
| print("Warning: 'working_yolo_pipeline.py' not found. Using dummy paths.") | |
| def run_document_pipeline(*args): | |
| return {"error": "Placeholder pipeline function called."} | |
| DEFAULT_LAYOUTLMV3_MODEL_PATH = "./models/layoutlmv3_model" | |
| WEIGHTS_PATH = "./weights/yolo_weights.pt" | |
| def process_pdf(pdf_file, layoutlmv3_model_path=None): | |
| if pdf_file is None: | |
| return "❌ Error: No PDF file uploaded.", None | |
| if not layoutlmv3_model_path: | |
| layoutlmv3_model_path = DEFAULT_LAYOUTLMV3_MODEL_PATH | |
| if not os.path.exists(layoutlmv3_model_path): | |
| return f"❌ Error: LayoutLMv3 model not found at {layoutlmv3_model_path}", None | |
| if not os.path.exists(WEIGHTS_PATH): | |
| return f"❌ Error: YOLO weights not found at {WEIGHTS_PATH}", None | |
| try: | |
| pdf_path = pdf_file.name | |
| result = run_document_pipeline(pdf_path, layoutlmv3_model_path, 'label_studio_import.json') | |
| if result is None: | |
| return "❌ Error: Pipeline failed to process the PDF. Check console for details.", None | |
| output_filename = f"{Path(pdf_path).stem}_analysis.json" | |
| temp_output = tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json', prefix='analysis_') | |
| with open(temp_output.name, 'w', encoding='utf-8') as f: | |
| json.dump(result, f, indent=2, ensure_ascii=False) | |
| json_display = json.dumps(result, indent=2, ensure_ascii=False) | |
| return json_display, temp_output.name | |
| except Exception as e: | |
| return f"❌ Error during processing: {str(e)}", None | |
| with gr.Blocks( | |
| title="Document Analysis Pipeline" | |
| ) as demo: | |
| gr.HTML() | |
| gr.Markdown(""" | |
| # 📄 Document Analysis Pipeline | |
| Upload a PDF document to extract structured data including questions, options, answers, passages, and embedded images. | |
| **Pipeline Steps:** | |
| 1. 🔍 YOLO/OCR Preprocessing (word extraction + figure/equation detection) | |
| 2. 🤖 LayoutLMv3 Inference (BIO tagging) | |
| 3. 📊 Structured JSON Decoding | |
| 4. 🖼️ Base64 Image Embedding | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| pdf_input = gr.File( | |
| label="Upload PDF Document", | |
| file_types=[".pdf"], | |
| type="filepath" | |
| ) | |
| model_path_input = gr.Textbox( | |
| label="LayoutLMv3 Model Path (optional)", | |
| placeholder=DEFAULT_LAYOUTLMV3_MODEL_PATH, | |
| value=DEFAULT_LAYOUTLMV3_MODEL_PATH, | |
| interactive=True | |
| ) | |
| process_btn = gr.Button("🚀 Process Document", variant="primary", size="lg") | |
| gr.Markdown(""" | |
| ### ℹ️ Notes: | |
| - Processing may take several minutes depending on PDF size | |
| - Figures and equations will be extracted and embedded as Base64 | |
| - The output JSON includes structured questions, options, and answers | |
| """) | |
| with gr.Column(scale=2): | |
| json_output = gr.Code( | |
| label="Structured JSON Output", | |
| language="json", | |
| lines=25 | |
| ) | |
| download_output = gr.File( | |
| label="Download Full JSON", | |
| interactive=False | |
| ) | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| ### 📋 Output Format | |
| The pipeline generates JSON with the following structure: | |
| - **Questions**: Extracted question text | |
| - **Options**: Multiple choice options | |
| - **Answers**: Correct answer(s) | |
| - **Passages**: Associated reading passages | |
| - **Images**: Base64-encoded figures and equations | |
| """) | |
| process_btn.click( | |
| fn=process_pdf, | |
| inputs=[pdf_input, model_path_input], | |
| outputs=[json_output, download_output], | |
| api_name="process_document" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| ssr_mode=False, # 🔥 FIXES YOUR ERROR | |
| show_error=True, | |
| # These two prevent asyncio cleanup issues | |
| enable_queue=True, | |
| max_threads=1, | |
| ) |