upload app (#1)

- upload app (ed8d4c13f05563a1f453d8fa7446f2e8b884d5f4)

app.py

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+import os
+import hashlib
+import spaces
+import re
+import time
+import click
+import gradio as gr
+from io import BytesIO
+from PIL import Image
+from loguru import logger
+from pathlib import Path
+import torch
+from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+from transformers.image_utils import load_image
+import fitz
+import html2text
+import markdown
+import tempfile
+from typing import Optional, Tuple, Dict, Any, List
+
+pdf_suffixes = [".pdf"]
+image_suffixes = [".png", ".jpeg", ".jpg"]
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+logger.info(f"Using device: {device}")
+
+# Model 1: Logics-Parsing
+MODEL_ID_1 = "Logics-MLLM/Logics-Parsing"
+logger.info(f"Loading model 1: {MODEL_ID_1}")
+processor_1 = AutoProcessor.from_pretrained(MODEL_ID_1, trust_remote_code=True)
+model_1 = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_1,
+    trust_remote_code=True,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32
+).to(device).eval()
+logger.info(f"Model '{MODEL_ID_1}' loaded successfully.")
+
+# Model 2: Gliese-OCR-7B-Post1.0
+MODEL_ID_2 = "prithivMLmods/Gliese-OCR-7B-Post1.0"
+logger.info(f"Loading model 2: {MODEL_ID_2}")
+processor_2 = AutoProcessor.from_pretrained(MODEL_ID_2, trust_remote_code=True)
+model_2 = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_2,
+    trust_remote_code=True,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32
+).to(device).eval()
+logger.info(f"Model '{MODEL_ID_2}' loaded successfully.")
+
+@spaces.GPU
+def parse_page(image: Image.Image, model_name: str) -> str:
+    """
+    Parses a single document page image using the selected model.
+    """
+    if model_name == "Logics-Parsing":
+        current_processor, current_model = processor_1, model_1
+    elif model_name == "Gliese-OCR-7B-Post1.0":
+        current_processor, current_model = processor_2, model_2
+    else:
+        raise ValueError(f"Unknown model choice: {model_name}")
+
+    messages = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": "Parse this document page into a clean, structured HTML representation. Preserve the logical structure with appropriate tags for content blocks such as paragraphs (<p>), headings (<h1>-<h6>), tables (<table>), figures (<figure>), formulas (<formula>), and others. Include category tags, and filter out irrelevant elements like headers and footers."}]}]
+    prompt_full = current_processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = current_processor(text=[prompt_full], images=[image], return_tensors="pt", padding=True).to(device)
+
+    with torch.no_grad():
+        generated_ids = current_model.generate(**inputs, max_new_tokens=2048, temperature=0.1, top_p=0.9, do_sample=True, repetition_penalty=1.05)
+    
+    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+    output_text = current_processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    return output_text
+
+def convert_file_to_images(file_path: str, dpi: int = 200) -> List[Image.Image]:
+    """
+    Converts a PDF or image file into a list of PIL Images.
+    """
+    images = []
+    file_ext = Path(file_path).suffix.lower()
+    
+    if file_ext in image_suffixes:
+        images.append(Image.open(file_path).convert("RGB"))
+        return images
+        
+    if file_ext not in pdf_suffixes:
+        raise ValueError(f"Unsupported file type: {file_ext}")
+
+    try:
+        pdf_document = fitz.open(file_path)
+        zoom = dpi / 72.0
+        mat = fitz.Matrix(zoom, zoom)
+        for page_num in range(len(pdf_document)):
+            page = pdf_document.load_page(page_num)
+            pix = page.get_pixmap(matrix=mat)
+            img_data = pix.tobytes("png")
+            images.append(Image.open(BytesIO(img_data)))
+        pdf_document.close()
+    except Exception as e:
+        logger.error(f"Failed to convert PDF using PyMuPDF: {e}")
+        raise
+    return images
+
+def get_initial_state() -> Dict[str, Any]:
+    """Returns the default initial state for the application."""
+    return {"pages": [], "total_pages": 0, "current_page_index": 0, "page_results": []}
+
+def load_and_preview_file(file_path: Optional[str]) -> Tuple[Optional[Image.Image], str, Dict[str, Any]]:
+    """
+    Loads a file, converts all pages to images, and stores them in the state.
+    """
+    state = get_initial_state()
+    if not file_path:
+        return None, '<div class="page-info">No file loaded</div>', state
+
+    try:
+        pages = convert_file_to_images(file_path)
+        if not pages:
+            return None, '<div class="page-info">Could not load file</div>', state
+        
+        state["pages"] = pages
+        state["total_pages"] = len(pages)
+        page_info_html = f'<div class="page-info">Page 1 / {state["total_pages"]}</div>'
+        return pages[0], page_info_html, state
+    except Exception as e:
+        logger.error(f"Failed to load and preview file: {e}")
+        return None, '<div class="page-info">Failed to load preview</div>', state
+
+async def process_all_pages(state: Dict[str, Any], model_choice: str):
+    """
+    Processes all pages stored in the state and updates the state with results.
+    """
+    if not state or not state["pages"]:
+        error_msg = "<h3>Please upload a file first.</h3>"
+        return error_msg, "", "", None, "Error: No file to process", state
+
+    logger.info(f'Processing {state["total_pages"]} pages with model: {model_choice}')
+    start_time = time.time()
+    
+    try:
+        page_results = []
+        for i, page_img in enumerate(state["pages"]):
+            logger.info(f"Parsing page {i + 1}/{state['total_pages']}")
+            html_result = parse_page(page_img, model_choice)
+            page_results.append({'raw_html': html_result})
+        
+        state["page_results"] = page_results
+        
+        # Create a single markdown file for download with all content
+        full_html_content = "\n\n".join([f'<!-- Page {i+1} -->\n{res["raw_html"]}' for i, res in enumerate(page_results)])
+        full_markdown = html2text.html2text(full_html_content)
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False, encoding='utf-8') as f:
+            f.write(full_markdown)
+            md_path = f.name
+            
+        parsing_time = time.time() - start_time
+        cost_time_str = f'Total processing time: {parsing_time:.2f}s'
+        
+        # Display the results for the current page
+        current_page_results = get_page_outputs(state)
+        
+        return *current_page_results, md_path, cost_time_str, state
+
+    except Exception as e:
+        logger.error(f"Parsing failed: {e}", exc_info=True)
+        error_html = f"<h3>An error occurred during processing:</h3><p>{str(e)}</p>"
+        return error_html, "", "", None, f"Error: {str(e)}", state
+
+def navigate_page(direction: str, state: Dict[str, Any]):
+    """
+    Navigates to the previous or next page and updates the UI accordingly.
+    """
+    if not state or not state["pages"]:
+        return None, '<div class="page-info">No file loaded</div>', *get_page_outputs(state), state
+
+    current_index = state["current_page_index"]
+    total_pages = state["total_pages"]
+    
+    if direction == "prev":
+        new_index = max(0, current_index - 1)
+    elif direction == "next":
+        new_index = min(total_pages - 1, current_index + 1)
+    else:
+        new_index = current_index
+        
+    state["current_page_index"] = new_index
+    
+    image_preview = state["pages"][new_index]
+    page_info_html = f'<div class="page-info">Page {new_index + 1} / {total_pages}</div>'
+    
+    page_outputs = get_page_outputs(state)
+    
+    return image_preview, page_info_html, *page_outputs, state
+
+def get_page_outputs(state: Dict[str, Any]) -> Tuple[str, str, str]:
+    """Helper to get displayable outputs for the current page."""
+    if not state or not state.get("page_results"):
+        return "<h3>Process the document to see results.</h3>", "", ""
+
+    index = state["current_page_index"]
+    result = state["page_results"][index]
+    raw_html = result['raw_html']
+    
+    mmd_source = html2text.html2text(raw_html)
+    mmd_render = markdown.markdown(mmd_source, extensions=['fenced_code', 'tables'])
+    
+    return mmd_render, mmd_source, raw_html
+
+def clear_all():
+    """Clears all UI components and resets the state."""
+    return (
+        None,
+        None,
+        "<h3>Results will be displayed here after processing.</h3>",
+        "",
+        "",
+        None,
+        "",
+        '<div class="page-info">No file loaded</div>',
+        get_initial_state()
+    )
+
+@click.command()
+def main():
+    """
+    Sets up and launches the Gradio user interface for the Logics-Parsing app.
+    """
+    css = """
+    .main-container { max-width: 1400px; margin: 0 auto; }
+    .header-text { text-align: center; color: #2c3e50; margin-bottom: 20px; }
+    .process-button { border: none !important; color: white !important; font-weight: bold !important; background-color: blue !important;}
+    .process-button:hover { background-color: darkblue !important; transform: translateY(-2px) !important; box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; }
+    .page-info { text-align: center; padding: 8px 16px; border-radius: 20px; font-weight: bold; margin: 10px 0; }
+    """
+    with gr.Blocks(theme="bethecloud/storj_theme", css=css, title="Logics-Parsing Demo") as demo:
+        app_state = gr.State(value=get_initial_state())
+
+        gr.HTML("""
+        <div class="header-text">
+            <h1>📄 Logics-Parsing: Document Parsing VLM</h1>
+            <p style="font-size: 1.1em; color: #6b7280;">An advanced Vision Language Model to parse documents and images into clean HTML and Markdown.</p>
+            <div style="display: flex; justify-content: center; gap: 20px; margin: 15px 0;">
+                <a href="https://huggingface.co/Logics-MLLM/Logics-Parsing" target="_blank" style="text-decoration: none; color: #2563eb; font-weight: 500;">🤗 Model Page</a>
+                <a href="https://github.com/alibaba/Logics-Parsing" target="_blank" style="text-decoration: none; color: #2563eb; font-weight: 500;">💻 GitHub</a>
+                <a href="https://arxiv.org/abs/2509.19760" target="_blank" style="text-decoration: none; color: #2563eb; font-weight: 500;">📝 Arxiv Paper</a>
+            </div>
+        </div>
+        """)
+
+        with gr.Row(elem_classes=["main-container"]):
+            with gr.Column(scale=1):
+                model_choice = gr.Dropdown(choices=["Logics-Parsing", "Gliese-OCR-7B-Post1.0"], label="Select Model⚡️", value="Logics-Parsing")
+                file_input = gr.File(label="Upload PDF or Image", file_types=[".pdf", ".jpg", ".jpeg", ".png"], type="filepath")
+                image_preview = gr.Image(label="Preview", type="pil", interactive=False, height=280)
+                
+                with gr.Row():
+                    prev_page_btn = gr.Button("◀ Previous", size="md")
+                    page_info = gr.HTML('<div class="page-info">No file loaded</div>')
+                    next_page_btn = gr.Button("Next ▶", size="md")
+
+                example_root = "examples"
+                if os.path.exists(example_root) and os.path.isdir(example_root):
+                    example_files = [os.path.join(example_root, f) for f in os.listdir(example_root) if f.endswith(tuple(pdf_suffixes + image_suffixes))]
+                    if example_files:
+                        with gr.Accordion("Open Examples⚙️", open=False):
+                            gr.Examples(examples=example_files, inputs=file_input, examples_per_page=10)
+
+                with gr.Accordion("Download Details🕧", open=False):
+                    output_file = gr.File(label='Download Markdown Result', interactive=False)
+                    cost_time = gr.Text(label='Time Cost', interactive=False)
+
+                process_btn = gr.Button("🚀 Process Document", variant="primary", elem_classes=["process-button"], size="lg")
+                clear_btn = gr.Button("🗑️ Clear All", variant="secondary")
+
+            with gr.Column(scale=2):
+                with gr.Tabs():
+                    with gr.Tab("Markdown Rendering"):
+                        mmd_html = gr.TextArea(lines=27, label='Markdown Rendering', show_copy_button=True, interactive=True)
+                    with gr.Tab("Markdown Source"):
+                        mmd = gr.TextArea(lines=27, show_copy_button=True, label="Markdown Source", interactive=True)
+                    with gr.Tab("Generated HTML"):
+                        raw_html = gr.TextArea(lines=27, show_copy_button=True, label="Generated HTML")
+
+        # --- Event Handlers ---
+        file_input.change(
+            fn=load_and_preview_file, 
+            inputs=file_input, 
+            outputs=[image_preview, page_info, app_state],
+            show_progress="full")
+        
+        process_btn.click(
+            fn=process_all_pages, 
+            inputs=[app_state, model_choice], 
+            outputs=[mmd_html, mmd, raw_html, 
+            output_file, cost_time, app_state], 
+            concurrency_limit=15, 
+            show_progress="full")
+
+        prev_page_btn.click(
+            fn=lambda s: navigate_page("prev", s), 
+            inputs=app_state, outputs=[image_preview, 
+            page_info, mmd_html, mmd, raw_html, app_state])
+        
+        next_page_btn.click(
+            fn=lambda s: navigate_page("next", s), 
+            inputs=app_state, outputs=[image_preview, 
+            page_info, mmd_html, mmd, raw_html, app_state])
+
+        clear_btn.click(
+            fn=clear_all, 
+            outputs=[file_input, image_preview, mmd_html, mmd, raw_html, 
+                     output_file, cost_time, page_info, app_state])
+        
+    demo.queue().launch(debug=True, show_error=True)
+
+if __name__ == '__main__':
+    if not os.path.exists("examples"):
+        os.makedirs("examples")
+        logger.info("Created 'examples' directory. Please add some sample PDF/image files there.")
+    main()

pre-requirements.txt

@@ -0,0 +1 @@
+pip>=23.0.0

requirements.txt

@@ -0,0 +1,38 @@
+git+https://github.com/huggingface/accelerate.git
+git+https://github.com/huggingface/peft.git
+transformers-stream-generator
+gradio_pdf==0.0.22
+huggingface_hub
+albumentations
+beautifulsoup4
+qwen-vl-utils
+pyvips-binary
+sentencepiece
+opencv-python
+transformers
+docling-core
+python-docx
+torchvision
+matplotlib
+pdf2image
+num2words
+reportlab
+html2text
+xformers
+markdown
+requests
+pymupdf
+loguru
+hf_xet
+spaces
+pyvips
+pillow
+gradio
+einops
+httpx
+click
+torch
+oss2
+fpdf
+timm
+av