app.py

# 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,
    )