import os import sys from typing import Iterable, Optional, Tuple, Dict, Any, List 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 gradio.themes import Soft from gradio.themes.utils import colors, fonts, sizes # --- Theme and CSS Definition --- colors.steel_blue = colors.Color( name="steel_blue", c50="#EBF3F8", c100="#D3E5F0", c200="#A8CCE1", c300="#7DB3D2", c400="#529AC3", c500="#4682B4", c600="#3E72A0", c700="#36638C", c800="#2E5378", c900="#264364", c950="#1E3450", ) class SteelBlueTheme(Soft): def __init__( self, *, primary_hue: colors.Color | str = colors.gray, secondary_hue: colors.Color | str = colors.steel_blue, neutral_hue: colors.Color | str = colors.slate, text_size: sizes.Size | str = sizes.text_lg, font: fonts.Font | str | Iterable[fonts.Font | str] = ( fonts.GoogleFont("Outfit"), "Arial", "sans-serif", ), font_mono: fonts.Font | str | Iterable[fonts.Font | str] = ( fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace", ), ): super().__init__( primary_hue=primary_hue, secondary_hue=secondary_hue, neutral_hue=neutral_hue, text_size=text_size, font=font, font_mono=font_mono, ) super().set( background_fill_primary="*primary_50", background_fill_primary_dark="*primary_900", body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)", body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)", button_primary_text_color="white", button_primary_text_color_hover="white", button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)", button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)", button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)", button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)", slider_color="*secondary_500", slider_color_dark="*secondary_600", block_title_text_weight="600", block_border_width="3px", block_shadow="*shadow_drop_lg", button_primary_shadow="*shadow_drop_lg", button_large_padding="11px", color_accent_soft="*primary_100", block_label_background_fill="*primary_200", ) steel_blue_theme = SteelBlueTheme() # --- Model and App Logic --- 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.") # Model 3: olmOCR-7B-0825 MODEL_ID_3 = "allenai/olmOCR-7B-0825" logger.info(f"Loading model 3: {MODEL_ID_3}") processor_3 = AutoProcessor.from_pretrained(MODEL_ID_3, trust_remote_code=True) model_3 = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_3, trust_remote_code=True, torch_dtype=torch.float16 if device == "cuda" else torch.float32 ).to(device).eval() logger.info(f"Model '{MODEL_ID_3}' loaded successfully.") @spaces.GPU def parse_page(image: Image.Image, model_name: str) -> str: 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 elif model_name == "olmOCR-7B-0825": current_processor, current_model = processor_3, model_3 else: raise ValueError(f"Unknown model choice: {model_name}") messages = [{"role": "user", "content": [{"type": "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 (

), headings (

-

), tables (), figures (
), formulas (), 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.convert("RGB")], return_tensors="pt").to(device) with torch.no_grad(): generated_ids = current_model.generate(**inputs, max_new_tokens=2048, do_sample=False) generated_ids = generated_ids[:, inputs['input_ids'].shape[1]:] output_text = current_processor.batch_decode(generated_ids, skip_special_tokens=True)[0] return output_text def convert_file_to_images(file_path: str, dpi: int = 200) -> List[Image.Image]: 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)).convert("RGB")) 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]: 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]]: state = get_initial_state() if not file_path: return None, '
No file loaded
', state try: pages = convert_file_to_images(file_path) if not pages: return None, '
Could not load file
', state state["pages"] = pages state["total_pages"] = len(pages) page_info_html = f'
Page 1 / {state["total_pages"]}
' return pages[0], page_info_html, state except Exception as e: logger.error(f"Failed to load and preview file: {e}") return None, '
Failed to load preview
', state async def process_all_pages(state: Dict[str, Any], model_choice: str, progress=gr.Progress(track_tqdm=True)): if not state or not state["pages"]: error_msg = "

Please upload a file first.

" 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 progress.tqdm(enumerate(state["pages"]), desc="Processing Pages"): html_result = parse_page(page_img, model_choice) page_results.append({'raw_html': html_result}) state["page_results"] = page_results full_html_content = "\n\n".join([f'\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' 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"

An error occurred during processing:

{str(e)}

" return error_html, "", "", None, f"Error: {str(e)}", state def navigate_page(direction: str, state: Dict[str, Any]): if not state or not state["pages"]: return None, '
No file loaded
', *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'
Page {new_index + 1} / {total_pages}
' 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]: if not state or not state.get("page_results"): return "

Process the document to see results.

", "", "" index = state["current_page_index"] if index >= len(state["page_results"]): return "

Result not available for this page.

", "", "" result = state["page_results"][index] raw_html = result['raw_html'] md_source = html2text.html2text(raw_html) md_render = markdown.markdown(md_source, extensions=['fenced_code', 'tables']) return md_render, md_source, raw_html def clear_all(): return None, None, "

Results will be displayed here after processing.

", "", "", None, "", '
No file loaded
', get_initial_state() @click.command() def main(): css = """ .main-container { max-width: 1400px; margin: 0 auto; } .header-text { text-align: center; margin-bottom: 20px; } .page-info { text-align: center; padding: 8px 16px; font-weight: bold; margin: 10px 0; } """ with gr.Blocks(theme=steel_blue_theme, css=css, title="Logics-Parsing Demo") as demo: app_state = gr.State(value=get_initial_state()) gr.HTML("""

📄 Multimodal: VLM Parsing

An advanced Vision Language Model to parse documents and images into clean Markdown (html)

🤗 Model Info 💻 GitHub 📝 Multimodal VLMs
""") with gr.Row(elem_classes=["main-container"]): with gr.Column(scale=1): model_choice = gr.Dropdown(choices=["Logics-Parsing", "Gliese-OCR-7B-Post1.0", "olmOCR-7B-0825"], 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=320) with gr.Row(): prev_page_btn = gr.Button("◀ Previous") page_info = gr.HTML('
No file loaded
') next_page_btn = gr.Button("Next ▶") with gr.Accordion("Download & Details", open=False): output_file = gr.File(label='Download Markdown Result', interactive=False) cost_time = gr.Textbox(label='Time Cost', interactive=False) 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: gr.Examples(examples=example_files, inputs=file_input, label="Examples") process_btn = gr.Button("🚀 Process Document", variant="primary", size="lg") clear_btn = gr.Button("🗑️ Clear All", variant="secondary") with gr.Column(scale=2): with gr.Tabs(): with gr.Tab("Markdown Source"): md_source_output = gr.Code(language="markdown", label="Markdown Source") with gr.Tab("Rendered Markdown"): md_render_output = gr.Markdown(label='Markdown Rendering') with gr.Tab("Generated HTML"): raw_html_output = gr.Code(language="html", label="Generated HTML") 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=[md_render_output, md_source_output, raw_html_output, output_file, cost_time, app_state], show_progress="full") prev_page_btn.click(fn=lambda s: navigate_page("prev", s), inputs=app_state, outputs=[image_preview, page_info, md_render_output, md_source_output, raw_html_output, app_state]) next_page_btn.click(fn=lambda s: navigate_page("next", s), inputs=app_state, outputs=[image_preview, page_info, md_render_output, md_source_output, raw_html_output, app_state]) clear_btn.click(fn=clear_all, outputs=[file_input, image_preview, md_render_output, md_source_output, raw_html_output, 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()