Delete app.py

app.py

@@ -1,456 +0,0 @@
-import spaces
-import json
-import math
-import os
-import traceback
-from io import BytesIO
-from typing import Any, Dict, List, Optional, Tuple
-import re
-
-import fitz  # PyMuPDF
-import gradio as gr
-import requests
-from PIL import Image, ImageDraw, ImageFont
-
-from model import load_model, inference_dots_ocr, inference_dolphin
-
-# Constants
-MIN_PIXELS = 3136
-MAX_PIXELS = 11289600
-IMAGE_FACTOR = 28
-
-# Prompts
-prompt = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
-
-1. Bbox format: [x1, y1, x2, y2]
-2. Layout Categories: ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title']
-3. Text Extraction & Formatting Rules:
-    - Picture: omit the text field
-    - Formula: format as LaTeX
-    - Table: format as HTML
-    - Others: format as Markdown
-4. Constraints:
-    - Use original text, no translation
-    - Sort elements by human reading order
-5. Final Output: Single JSON object
-"""
-
-# Load models at startup
-models = {
-    "dots.ocr": load_model("dots.ocr"),
-    "Dolphin": load_model("Dolphin")
-}
-
-# Global state for PDF handling
-pdf_cache = {
-    "images": [],
-    "current_page": 0,
-    "total_pages": 0,
-    "file_type": None,
-    "is_parsed": False,
-    "results": []
-}
-
-# Utility functions
-def round_by_factor(number: int, factor: int) -> int:
-    return round(number / factor) * factor
-
-def smart_resize(height: int, width: int, factor: int = 28, min_pixels: int = 3136, max_pixels: int = 11289600):
-    if max(height, width) / min(height, width) > 200:
-        raise ValueError(f"Aspect ratio must be < 200, got {max(height, width) / min(height, width)}")
-    h_bar = max(factor, round_by_factor(height, factor))
-    w_bar = max(factor, round_by_factor(width, factor))
-    if h_bar * w_bar > max_pixels:
-        beta = math.sqrt((height * width) / max_pixels)
-        h_bar = round_by_factor(height / beta, factor)
-        w_bar = round_by_factor(width / beta, factor)
-    elif h_bar * w_bar < min_pixels:
-        beta = math.sqrt(min_pixels / (height * width))
-        h_bar = round_by_factor(height * beta, factor)
-        w_bar = round_by_factor(width * beta, factor)
-    return h_bar, w_bar
-
-def fetch_image(image_input, min_pixels: int = None, max_pixels: int = None):
-    if isinstance(image_input, str):
-        if image_input.startswith(("http://", "https://")):
-            response = requests.get(image_input)
-            image = Image.open(BytesIO(response.content)).convert('RGB')
-        else:
-            image = Image.open(image_input).convert('RGB')
-    elif isinstance(image_input, Image.Image):
-        image = image_input.convert('RGB')
-    else:
-        raise ValueError(f"Invalid image input type: {type(image_input)}")
-    if min_pixels or max_pixels:
-        min_pixels = min_pixels or MIN_PIXELS
-        max_pixels = max_pixels or MAX_PIXELS
-        height, width = smart_resize(image.height, image.width, factor=IMAGE_FACTOR, min_pixels=min_pixels, max_pixels=max_pixels)
-        image = image.resize((width, height), Image.LANCZOS)
-    return image
-
-def load_images_from_pdf(pdf_path: str) -> List[Image.Image]:
-    images = []
-    try:
-        pdf_document = fitz.open(pdf_path)
-        for page_num in range(len(pdf_document)):
-            page = pdf_document.load_page(page_num)
-            mat = fitz.Matrix(2.0, 2.0)
-            pix = page.get_pixmap(matrix=mat)
-            img_data = pix.tobytes("ppm")
-            image = Image.open(BytesIO(img_data)).convert('RGB')
-            images.append(image)
-        pdf_document.close()
-    except Exception as e:
-        print(f"Error loading PDF: {e}")
-        return []
-    return images
-
-def draw_layout_on_image(image: Image.Image, layout_data: List[Dict]) -> Image.Image:
-    img_copy = image.copy()
-    draw = ImageDraw.Draw(img_copy)
-    colors = {
-        'Caption': '#FF6B6B', 'Footnote': '#4ECDC4', 'Formula': '#45B7D1', 'List-item': '#96CEB4',
-        'Page-footer': '#FFEAA7', 'Page-header': '#DDA0DD', 'Picture': '#FFD93D', 'Section-header': '#6C5CE7',
-        'Table': '#FD79A8', 'Text': '#74B9FF', 'Title': '#E17055'
-    }
-    try:
-        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 12)
-    except Exception:
-        font = ImageFont.load_default()
-    try:
-        for item in layout_data:
-            if 'bbox' in item and 'category' in item:
-                bbox = item['bbox']
-                category = item['category']
-                color = colors.get(category, '#000000')
-                draw.rectangle(bbox, outline=color, width=2)
-                label = category
-                label_bbox = draw.textbbox((0, 0), label, font=font)
-                label_width = label_bbox[2] - label_bbox[0]
-                label_height = label_bbox[3] - label_bbox[1]
-                label_x = bbox[0]
-                label_y = max(0, bbox[1] - label_height - 2)
-                draw.rectangle([label_x, label_y, label_x + label_width + 4, label_y + label_height + 2], fill=color)
-                draw.text((label_x + 2, label_y + 1), label, fill='white', font=font)
-    except Exception as e:
-        print(f"Error drawing layout: {e}")
-    return img_copy
-
-def is_arabic_text(text: str) -> bool:
-    if not text:
-        return False
-    header_pattern = r'^#{1,6}\s+(.+)$'
-    paragraph_pattern = r'^(?!#{1,6}\s|!\[|```|\||\s*[-*+]\s|\s*\d+\.\s)(.+)$'
-    content_text = []
-    for line in text.split('\n'):
-        line = line.strip()
-        if not line:
-            continue
-        header_match = re.match(header_pattern, line, re.MULTILINE)
-        if header_match:
-            content_text.append(header_match.group(1))
-            continue
-        if re.match(paragraph_pattern, line, re.MULTILINE):
-            content_text.append(line)
-    if not content_text:
-        return False
-    combined_text = ' '.join(content_text)
-    arabic_chars = 0
-    total_chars = 0
-    for char in combined_text:
-        if char.isalpha():
-            total_chars += 1
-            if ('\u0600' <= char <= '\u06FF') or ('\u0750' <= char <= '\u077F') or ('\u08A0' <= char <= '\u08FF'):
-                arabic_chars += 1
-    return total_chars > 0 and (arabic_chars / total_chars) > 0.5
-
-def layoutjson2md(image: Image.Image, layout_data: List[Dict], text_key: str = 'text') -> str:
-    import base64
-    markdown_lines = []
-    try:
-        sorted_items = sorted(layout_data, key=lambda x: (x.get('bbox', [0, 0, 0, 0])[1], x.get('bbox', [0, 0, 0, 0])[0]))
-        for item in sorted_items:
-            category = item.get('category', '')
-            text = item.get(text_key, '')
-            bbox = item.get('bbox', [])
-            if category == 'Picture':
-                if bbox and len(bbox) == 4:
-                    try:
-                        x1, y1, x2, y2 = [max(0, int(x)) if i < 2 else min(image.width if i % 2 == 0 else image.height, int(x)) for i, x in enumerate(bbox)]
-                        if x2 > x1 and y2 > y1:
-                            cropped_img = image.crop((x1, y1, x2, y2))
-                            buffer = BytesIO()
-                            cropped_img.save(buffer, format='PNG')
-                            img_data = base64.b64encode(buffer.getvalue()).decode()
-                            markdown_lines.append(f'<image-card alt="Image" src="data:image/png;base64,{img_data}" ></image-card>\n')
-                        else:
-                            markdown_lines.append('<image-card alt="Image" src="Image region detected" ></image-card>\n')
-                    except Exception as e:
-                        print(f"Error processing image region: {e}")
-                        markdown_lines.append('<image-card alt="Image" src="Image detected" ></image-card>\n')
-                else:
-                    markdown_lines.append('<image-card alt="Image" src="Image detected" ></image-card>\n')
-            elif not text:
-                continue
-            elif category == 'Title':
-                markdown_lines.append(f"# {text}\n")
-            elif category == 'Section-header':
-                markdown_lines.append(f"## {text}\n")
-            elif category == 'Text':
-                markdown_lines.append(f"{text}\n")
-            elif category == 'List-item':
-                markdown_lines.append(f"- {text}\n")
-            elif category == 'Table':
-                if text.strip().startswith('<'):
-                    markdown_lines.append(f"{text}\n")
-                else:
-                    markdown_lines.append(f"**Table:** {text}\n")
-            elif category == 'Formula':
-                if text.strip().startswith('$') or '\\' in text:
-                    markdown_lines.append(f"$$  \n{text}\n  $$\n")
-                else:
-                    markdown_lines.append(f"**Formula:** {text}\n")
-            elif category == 'Caption':
-                markdown_lines.append(f"*{text}*\n")
-            elif category == 'Footnote':
-                markdown_lines.append(f"^{text}^\n")
-            elif category in ['Page-header', 'Page-footer']:
-                continue
-            else:
-                markdown_lines.append(f"{text}\n")
-            markdown_lines.append("")
-    except Exception as e:
-        print(f"Error converting to markdown: {e}")
-        return str(layout_data)
-    return "\n".join(markdown_lines)
-
-def load_file_for_preview(file_path: str) -> Tuple[Optional[Image.Image], str]:
-    global pdf_cache
-    if not file_path or not os.path.exists(file_path):
-        return None, "No file selected"
-    file_ext = os.path.splitext(file_path)[1].lower()
-    try:
-        if file_ext == '.pdf':
-            images = load_images_from_pdf(file_path)
-            if not images:
-                return None, "Failed to load PDF"
-            pdf_cache.update({
-                "images": images,
-                "current_page": 0,
-                "total_pages": len(images),
-                "file_type": "pdf",
-                "is_parsed": False,
-                "results": []
-            })
-            return images[0], f"Page 1 / {len(images)}"
-        elif file_ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
-            image = Image.open(file_path).convert('RGB')
-            pdf_cache.update({
-                "images": [image],
-                "current_page": 0,
-                "total_pages": 1,
-                "file_type": "image",
-                "is_parsed": False,
-                "results": []
-            })
-            return image, "Page 1 / 1"
-        else:
-            return None, f"Unsupported file format: {file_ext}"
-    except Exception as e:
-        print(f"Error loading file: {e}")
-        return None, f"Error loading file: {str(e)}"
-
-@spaces.GPU()
-def process_document(file_path, model_choice, max_tokens, min_pix, max_pix):
-    global pdf_cache
-    if not file_path:
-        return None, "Please upload a file first.", None
-    model, processor = models[model_choice]
-    image, page_info = load_file_for_preview(file_path)
-    if image is None:
-        return None, page_info, None
-    if pdf_cache["file_type"] == "pdf":
-        all_results = []
-        for i, img in enumerate(pdf_cache["images"]):
-            if model_choice == "dots.ocr":
-                raw_output = inference_dots_ocr(model, processor, img, prompt, max_tokens)
-                try:
-                    layout_data = json.loads(raw_output)
-                    processed_image = draw_layout_on_image(img, layout_data)
-                    markdown_content = layoutjson2md(img, layout_data)
-                    result = {
-                        'processed_image': processed_image,
-                        'markdown_content': markdown_content,
-                        'layout_result': layout_data
-                    }
-                except Exception:
-                    result = {
-                        'processed_image': img,
-                        'markdown_content': raw_output,
-                        'layout_result': None
-                    }
-            else:  # Dolphin
-                text = inference_dolphin(model, processor, img)
-                result = f"## Page {i+1}\n\n{text}" if text else "No text extracted"
-            all_results.append(result)
-        pdf_cache["results"] = all_results
-        pdf_cache["is_parsed"] = True
-        first_result = all_results[0]
-        if model_choice == "dots.ocr":
-            markdown_update = gr.update(value=first_result['markdown_content'], rtl=is_arabic_text(first_result['markdown_content']))
-            return first_result['processed_image'], markdown_update, first_result['layout_result']
-        else:
-            markdown_update = gr.update(value=first_result, rtl=is_arabic_text(first_result))
-            return None, markdown_update, None
-    else:
-        if model_choice == "dots.ocr":
-            raw_output = inference_dots_ocr(model, processor, image, prompt, max_tokens)
-            try:
-                layout_data = json.loads(raw_output)
-                processed_image = draw_layout_on_image(image, layout_data)
-                markdown_content = layoutjson2md(image, layout_data)
-                result = {
-                    'processed_image': processed_image,
-                    'markdown_content': markdown_content,
-                    'layout_result': layout_data
-                }
-            except Exception:
-                result = {
-                    'processed_image': image,
-                    'markdown_content': raw_output,
-                    'layout_result': None
-                }
-            pdf_cache["results"] = [result]
-        else:  # Dolphin
-            text = inference_dolphin(model, processor, image)
-            result = text if text else "No text extracted"
-            pdf_cache["results"] = [result]
-        pdf_cache["is_parsed"] = True
-        if model_choice == "dots.ocr":
-            markdown_update = gr.update(value=result['markdown_content'], rtl=is_arabic_text(result['markdown_content']))
-            return result['processed_image'], markdown_update, result['layout_result']
-        else:
-            markdown_update = gr.update(value=result, rtl=is_arabic_text(result))
-            return None, markdown_update, None
-
-def turn_page(direction: str) -> Tuple[Optional[Image.Image], str, Any, Optional[Image.Image], Optional[Dict]]:
-    global pdf_cache
-    if not pdf_cache["images"]:
-        return None, '<div class="page-info">No file loaded</div>', "No results yet", None, None
-    if direction == "prev":
-        pdf_cache["current_page"] = max(0, pdf_cache["current_page"] - 1)
-    elif direction == "next":
-        pdf_cache["current_page"] = min(pdf_cache["total_pages"] - 1, pdf_cache["current_page"] + 1)
-    index = pdf_cache["current_page"]
-    current_image_preview = pdf_cache["images"][index]
-    page_info_html = f'<div class="page-info">Page {index + 1} / {pdf_cache["total_pages"]}</div>'
-    if pdf_cache["is_parsed"] and index < len(pdf_cache["results"]):
-        result = pdf_cache["results"][index]
-        if isinstance(result, dict):  # dots.ocr
-            markdown_content = result.get('markdown_content', 'No content available')
-            processed_img = result.get('processed_image', None)
-            layout_json = result.get('layout_result', None)
-        else:  # Dolphin
-            markdown_content = result
-            processed_img = None
-            layout_json = None
-    else:
-        markdown_content = "Page not processed yet"
-        processed_img = None
-        layout_json = None
-    markdown_update = gr.update(value=markdown_content, rtl=is_arabic_text(markdown_content))
-    return current_image_preview, page_info_html, markdown_update, processed_img, layout_json
-
-def create_gradio_interface():
-    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; }
-    .info-box { border: 1px solid #dee2e6; border-radius: 8px; padding: 15px; margin: 10px 0; }
-    .page-info { text-align: center; padding: 8px 16px; border-radius: 20px; font-weight: bold; margin: 10px 0; }
-    .model-status { padding: 10px; border-radius: 8px; margin: 10px 0; text-align: center; font-weight: bold; }
-    .status-ready { background: #d1edff; color: #0c5460; border: 1px solid #b8daff; }
-    """
-    with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo:
-        gr.HTML("""
-        <div class="title" style="text-align: center">
-            <h1>Dot<span style="color: red;">●</span><strong></strong>OCR vs Dolphin🐬</h1>
-            <p style="font-size: 1.1em; color: #6b7280; margin-bottom: 0.6em;">
-                Advanced vision-language model for image/PDF to markdown document processing
-            </p>
-        </div>
-        """)
-        with gr.Row():
-            with gr.Column(scale=1):
-                file_input = gr.File(
-                    label="Upload Image or PDF",
-                    file_types=[".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".pdf"],
-                    type="filepath"
-                )
-                image_preview = gr.Image(label="Preview", type="pil", interactive=False, height=300)
-                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")
-                model_choice = gr.Radio(
-                    choices=["dots.ocr", "Dolphin"],
-                    label="Select Model",
-                    value="dots.ocr"
-                )
-                with gr.Accordion("Advanced Settings", open=False):
-                    max_new_tokens = gr.Slider(minimum=1000, maximum=32000, value=24000, step=1000, label="Max New Tokens")
-                    min_pixels = gr.Number(value=MIN_PIXELS, label="Min Pixels")
-                    max_pixels = gr.Number(value=MAX_PIXELS, label="Max Pixels")
-                process_btn = gr.Button("🔥 Process Document", variant="primary", elem_classes=["process-button"], size="lg")
-                clear_btn = gr.Button("Clear Document", variant="secondary")
-
-            with gr.Column(scale=2):
-                with gr.Tabs():
-                    with gr.Tab("✦︎ Processed Image"):
-                        processed_image = gr.Image(label="Image with Layout Detection", type="pil", interactive=False, height=500)
-                    with gr.Tab("🀥 Extracted Content"):
-                        markdown_output = gr.Markdown(value="Click 'Process Document' to see extracted content...", height=500)
-                    with gr.Tab("⏲ Layout JSON"):
-                        json_output = gr.JSON(label="Layout Analysis Results", value=None)
-
-                with gr.Row():
-                    examples = gr.Examples(
-                        examples=["examples/sample_image1.png", "examples/sample_image2.png", "examples/sample_pdf.pdf"],
-                        inputs=file_input,
-                        label="Example Documents"
-                )
-                        
-        def handle_file_upload(file_path):
-            image, page_info = load_file_for_preview(file_path)
-            return image, page_info
-        
-        def clear_all():
-            global pdf_cache
-            pdf_cache = {"images": [], "current_page": 0, "total_pages": 0, "file_type": None, "is_parsed": False, "results": []}
-            return None, None, '<div class="page-info">No file loaded</div>', None, "Click 'Process Document' to see extracted content...", None
-        
-        file_input.change(handle_file_upload, inputs=[file_input], outputs=[image_preview, page_info])
-        prev_page_btn.click(lambda: turn_page("prev"), outputs=[image_preview, page_info, markdown_output, processed_image, json_output])
-        next_page_btn.click(lambda: turn_page("next"), outputs=[image_preview, page_info, markdown_output, processed_image, json_output])
-        process_btn.click(
-            process_document,
-            inputs=[file_input, model_choice, max_new_tokens, min_pixels, max_pixels],
-            outputs=[processed_image, markdown_output, json_output]
-        )
-        clear_btn.click(
-            clear_all,
-            outputs=[file_input, image_preview, page_info, processed_image, markdown_output, json_output]
-        )
-    return demo
-
-if __name__ == "__main__":
-    demo = create_gradio_interface()
-    demo.queue(max_size=30).launch(share=False, debug=True, show_error=True)