Upload app.py

app.py

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+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 time
+from threading import Thread
+
+import gradio as gr
+import requests
+import torch
+from PIL import Image
+from transformers import (
+    Qwen2_5_VLForConditionalGeneration,
+    AutoProcessor,
+    TextIteratorStreamer,
+)
+
+js_func = """
+function refresh() {
+    const url = new URL(window.location);
+    if (url.searchParams.get('__theme') !== 'dark') {
+        url.searchParams.set('__theme', 'dark');
+        window.location.href = url.href;
+    }
+}
+"""
+
+# --- Constants and Model Setup ---
+MAX_INPUT_TOKEN_LENGTH = 4096
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# --- Prompts for Different Tasks ---
+layout_prompt = """Please output the layout information from the 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: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
+3. Text Extraction & Formatting Rules:
+    - For tables, provide the content in a structured JSON format.
+    - For all other elements, provide the plain text.
+4. Constraints:
+    - The output must be the original text from the image.
+    - All layout elements must be sorted according to human reading order.
+5. Final Output: The entire output must be a single JSON object wrapped in ```json ... ```.
+"""
+
+ocr_prompt = "Perform precise OCR on the image. Extract all text content, maintaining the original structure, paragraphs, and tables as formatted markdown."
+
+# --- Model Loading ---
+MODEL_ID_M = "prithivMLmods/Camel-Doc-OCR-062825"
+processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
+model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_M, trust_remote_code=True, torch_dtype=torch.float16
+).to(device).eval()
+
+MODEL_ID_T = "prithivMLmods/Megalodon-OCR-Sync-0713"
+processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
+model_t = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_T, trust_remote_code=True, torch_dtype=torch.float16
+).to(device).eval()
+
+MODEL_ID_C = "nanonets/Nanonets-OCR-s"
+processor_c = AutoProcessor.from_pretrained(MODEL_ID_C, trust_remote_code=True)
+model_c = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_C, trust_remote_code=True, torch_dtype=torch.float16
+).to(device).eval()
+
+MODEL_ID_G = "echo840/MonkeyOCR"
+SUBFOLDER = "Recognition"
+processor_g = AutoProcessor.from_pretrained(
+    MODEL_ID_G, trust_remote_code=True, subfolder=SUBFOLDER
+)
+model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_G, trust_remote_code=True, subfolder=SUBFOLDER, torch_dtype=torch.float16
+).to(device).eval()
+
+# --- Utility Functions ---
+def layoutjson2md(layout_data: List[Dict]) -> str:
+    """Converts the structured JSON from Layout Analysis into formatted Markdown."""
+    markdown_lines = []
+    try:
+        # Sort items by reading order (top-to-bottom, left-to-right)
+        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', '')
+            if not text: continue
+
+            if category == 'Title': markdown_lines.append(f"# {text}\n")
+            elif category == 'Section-header': markdown_lines.append(f"## {text}\n")
+            elif category == 'Table':
+                 # Handle structured table JSON
+                if isinstance(text, dict) and 'header' in text and 'rows' in text:
+                    header = '| ' + ' | '.join(map(str, text['header'])) + ' |'
+                    separator = '| ' + ' | '.join(['---'] * len(text['header'])) + ' |'
+                    rows = ['| ' + ' | '.join(map(str, row)) + ' |' for row in text['rows']]
+                    markdown_lines.extend([header, separator] + rows)
+                    markdown_lines.append("\n")
+                else: # Fallback for simple text
+                    markdown_lines.append(f"{text}\n")
+            else:
+                 markdown_lines.append(f"{text}\n")
+    except Exception as e:
+        print(f"Error converting to markdown: {e}")
+        return "### Error converting JSON to Markdown."
+    return "\n".join(markdown_lines)
+
+# --- Core Application Logic ---
+@spaces.GPU
+def process_document_stream(model_name: str, task_choice: str, image: Image.Image, max_new_tokens: int):
+    """
+    Main generator function that handles both OCR and Layout Analysis tasks.
+    """
+    if image is None:
+        yield "Please upload an image.", "Please upload an image.", None
+        return
+
+    # 1. Select prompt based on user's task choice
+    text_prompt = ocr_prompt if task_choice == "Content Extraction" else layout_prompt
+
+    # 2. Select model and processor
+    if model_name == "Camel-Doc-OCR-062825": processor, model = processor_m, model_m
+    elif model_name == "Megalodon-OCR-Sync-0713": processor, model = processor_t, model_t
+    elif model_name == "Nanonets-OCR-s": processor, model = processor_c, model_c
+    elif model_name == "MonkeyOCR-Recognition": processor, model = processor_g, model_g
+    else:
+        yield "Invalid model selected.", "Invalid model selected.", None
+        return
+
+    # 3. Prepare model inputs and streamer
+    messages = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": text_prompt}]}]
+    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(text=[prompt_full], images=[image], return_tensors="pt", padding=True, truncation=True, max_length=MAX_INPUT_TOKEN_LENGTH).to(device)
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
+    
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    
+    # 4. Stream raw output to the UI in real-time
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        buffer = buffer.replace("<|im_end|>", "")
+        time.sleep(0.01)
+        yield buffer, "⏳ Processing...", {"status": "streaming"}
+
+    # 5. Post-process the final buffer based on the selected task
+    if task_choice == "Content Extraction":
+        # For OCR, the buffer is the final result.
+        yield buffer, buffer, None
+    else: # Layout Analysis
+        try:
+            json_match = re.search(r'```json\s*([\s\S]+?)\s*```', buffer)
+            if not json_match:
+                raise json.JSONDecodeError("JSON object not found in output.", buffer, 0)
+            
+            json_str = json_match.group(1)
+            layout_data = json.loads(json_str)
+            markdown_content = layoutjson2md(layout_data)
+            
+            yield buffer, markdown_content, layout_data
+        except Exception as e:
+            error_md = f"❌ **Error:** Failed to parse Layout JSON.\n\n**Details:**\n`{str(e)}`"
+            error_json = {"error": "ProcessingError", "details": str(e), "raw_output": buffer}
+            yield buffer, error_md, error_json
+
+# --- Gradio UI Definition ---
+def create_gradio_interface():
+    """Builds and returns the Gradio web interface."""
+    css = """
+    .main-container { max-width: 1400px; margin: 0 auto; }
+    .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; }
+    """
+    with gr.Blocks(theme="bethecloud/storj_theme", css=css, js=js_func) as demo:
+        gr.HTML("""
+        <div class="title" style="text-align: center">
+            <h1>OCR Comparator👨‍🏫</h1>
+            <p style="font-size: 1.1em; color: #6b7280; margin-bottom: 0.6em;">
+                Advanced Vision-Language Model for Image Content and Layout Extraction
+            </p>
+        </div>
+        """)
+        
+        with gr.Row():
+            # Left Column (Inputs)
+            with gr.Column(scale=1):
+                model_choice = gr.Dropdown(
+                    choices=["Camel-Doc-OCR-062825", 
+                             "MonkeyOCR-Recognition", 
+                             "Nanonets-OCR-s", 
+                             "Megalodon-OCR-Sync-0713"],
+                    label="Select Model", value="Nanonets-OCR-s"
+                )
+                task_choice = gr.Dropdown(
+                    choices=["Content Extraction", "Layout Analysis(.json)"],
+                    label="Select Task", value="Content Extraction"
+                )
+                image_input = gr.Image(label="Upload Image", type="pil", sources=['upload'])
+                with gr.Accordion("Advanced Settings", open=False):
+                    max_new_tokens = gr.Slider(minimum=512, maximum=8192, value=4096, step=256, label="Max New Tokens")
+                
+                process_btn = gr.Button("🚀 Process Document", variant="primary", elem_classes=["process-button"], size="lg")
+                clear_btn = gr.Button("🗑️ Clear All", variant="secondary")
+
+            # Right Column (Outputs)
+            with gr.Column(scale=2):
+                with gr.Tabs() as tabs:
+                    with gr.Tab("📝 Extracted Content"):
+                        raw_output_stream = gr.Textbox(label="Raw Model Output Stream", interactive=False, lines=13, show_copy_button=True)
+                        with gr.Row():
+                            examples = gr.Examples(
+                                examples=["examples/example_img2.png", "examples/example_img1.png"],
+                                inputs=image_input,
+                                label="Examples"
+                    )                    
+                    with gr.Tab("📰 README.md"):  
+                        with gr.Accordion("(Formatted Result)", open=True):
+                            markdown_output = gr.Markdown(label="Formatted Markdown")
+                    
+                    with gr.Tab("📋 Layout Analysis Results"):
+                        json_output = gr.JSON(label="Structured Layout Data (JSON)")
+        
+        # Event Handlers
+        def clear_all_outputs():
+            return None, "Raw output will appear here.", "Formatted results will appear here.", None
+
+        process_btn.click(
+            fn=process_document_stream,
+            inputs=[model_choice, 
+                    task_choice, 
+                    image_input, 
+                    max_new_tokens],
+            outputs=[raw_output_stream, 
+                     markdown_output, 
+                     json_output]
+        )
+        clear_btn.click(
+            clear_all_outputs,
+            outputs=[image_input, 
+                     raw_output_stream, 
+                     markdown_output, 
+                     json_output]
+        )
+    return demo
+
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    demo.queue().launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)