app.py

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 (
    Qwen2VLForConditionalGeneration,
    Qwen2_5_VLForConditionalGeneration,
    AutoModelForImageTextToText,
    AutoProcessor,
    TextIteratorStreamer,
    AutoModel,
    AutoTokenizer,
)

# --- 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-080125"
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()

MODEL_ID_I = "allenai/olmOCR-7B-0725"
processor_i = AutoProcessor.from_pretrained(MODEL_ID_I, trust_remote_code=True)
model_i = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_I, trust_remote_code=True, 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-080125": 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
    elif model_name == "olmOCR-7B-0725": processor, model = processor_i, model_i
    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) 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-080125", 
                             "MonkeyOCR-Recognition", 
                             "olmOCR-7B-0725",
                             "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/1.png", "examples/2.png", "examples/3.png", "examples/4.png", "examples/5.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(max_size=40).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)