Delete app.py

app.py

@@ -1,339 +0,0 @@
-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, AutoTokenizer, AutoModel
-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: DeepSeek-OCR
-logger.info("Loading model and tokenizer for DeepSeek-OCR...")
-model_name_2 = "deepseek-ai/DeepSeek-OCR"
-tokenizer_2 = AutoTokenizer.from_pretrained(model_name_2, trust_remote_code=True)
-model_2 = AutoModel.from_pretrained(
-    model_name_2,
-    _attn_implementation="flash_attention_2",
-    trust_remote_code=True
-).eval()
-logger.info("✅ DeepSeek-OCR model 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
-        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 (<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.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
-        
-    elif model_name == "DeepSeek-OCR":
-        # Move model to the correct device for inference
-        model_2.to(device)
-        
-        conversation = [
-            {"role": "user", "content": ["", image]},
-        ]
-        
-        input_tensor = tokenizer_2.apply_chat_template(conversation, return_tensors="pt")
-        
-        with torch.no_grad():
-            output_tensor = model_2.run(input_tensor.to(device))
-        
-        # This model returns plain text, so we wrap it in basic HTML for consistency
-        ocr_text = output_tensor[0]
-        html_output = "".join(f"<p>{line}</p>" for line in ocr_text.split('\n'))
-        return html_output
-        
-    else:
-        raise ValueError(f"Unknown model choice: {model_name}")
-
-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, '<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, progress=gr.Progress(track_tqdm=True)):
-    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 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'<!-- 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'
-        
-        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]):
-    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]:
-    if not state or not state.get("page_results"):
-        return "<h3>Process the document to see results.</h3>", "", ""
-
-    index = state["current_page_index"]
-    if index >= len(state["page_results"]):
-        return "<h3>Result not available for this page.</h3>", "", ""
-        
-    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, "<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():
-    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("""
-        <div class="header-text">
-            <h1>📄 Multimodal: VLM Parsing & OCR</h1>
-            <p style="font-size: 1.1em;">An advanced Vision Language Model to parse documents and images into clean Markdown (html)</p>
-            <div style="display: flex; justify-content: center; gap: 20px; margin: 15px 0;">
-                <a href="https://huggingface.co/collections/prithivMLmods/mm-vlm-parsing-68e33e52bfb9ae60b50602dc" target="_blank" style="text-decoration: none; font-weight: 500;">🤗 Model Info</a>
-                <a href="https://github.com/PRITHIVSAKTHIUR/VLM-Parsing" target="_blank" style="text-decoration: none; font-weight: 500;">💻 GitHub</a>
-                <a href="https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending" target="_blank" style="text-decoration: none; font-weight: 500;">📝 Multimodal VLMs</a>
-            </div>
-        </div>
-        """)
-
-        with gr.Row(elem_classes=["main-container"]):
-            with gr.Column(scale=1):
-                model_choice = gr.Dropdown(choices=["Logics-Parsing", "DeepSeek-OCR"], label="Select Model", value="Logics-Parsing")
-                file_input = gr.File(label="Upload PDF or Image", file_types=[".pdf", ".jpg", ".jpeg", ".png"], type="filepath")
-                
-                process_btn = gr.Button("🚀Process Document", variant="primary", size="lg")
-                clear_btn = gr.Button("🗑️ Clear All", variant="secondary")
-
-                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('<div class="page-info">No file loaded</div>')
-                    next_page_btn = gr.Button("Next ▶")
-                
-                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")
-
-                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)
-
-            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()