Delete app.py

app.py

@@ -1,313 +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 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,
-)
-
-from transformers.image_utils import load_image
-
-# --- Constants and Model Setup ---
-MAX_INPUT_TOKEN_LENGTH = 4096
-# Note: The following line correctly falls back to CPU if CUDA is not available.
-# Let the environment (e.g., Hugging Face Spaces) determine the device.
-# This avoids conflicts with the CUDA environment setup by the platform.
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
-print("torch.__version__ =", torch.__version__)
-print("torch.version.cuda =", torch.version.cuda)
-print("cuda available:", torch.cuda.is_available())
-print("cuda device count:", torch.cuda.device_count())
-if torch.cuda.is_available():
-    print("current device:", torch.cuda.current_device())
-    print("device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
-
-print("Using device:", device)
-
-# --- Model Loading ---
-
-# --- 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: Any) -> str:
-    """
-    FIXED: Converts the structured JSON from Layout Analysis into formatted Markdown.
-    This version is robust against malformed JSON from the model.
-    """
-    markdown_lines = []
-
-    # If the model wraps the list in a dictionary, find and extract the list.
-    if isinstance(layout_data, dict):
-        found_list = None
-        for value in layout_data.values():
-            if isinstance(value, list):
-                found_list = value
-                break
-        if found_list is not None:
-            layout_data = found_list
-        else:
-            return "### Error: Could not find a list of layout items in the JSON object."
-
-    if not isinstance(layout_data, list):
-        return f"### Error: Expected a list of layout items, but received type {type(layout_data).__name__}."
-
-    try:
-        # Filter out any non-dictionary items and sort by reading order.
-        valid_items = [item for item in layout_data if isinstance(item, dict)]
-        sorted_items = sorted(valid_items, 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') # Default to 'Text' if no 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':
-                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 or malformed tables
-                    markdown_lines.append(f"{text}\n")
-            else:
-                markdown_lines.append(f"{text}\n")
-
-    except Exception as e:
-        print(f"Error converting to markdown: {e}")
-        traceback.print_exc()
-        return "### Error: An unexpected error occurred while 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:
-                # If no JSON block is found, try to parse the whole buffer as a fallback.
-                try:
-                    layout_data = json.loads(buffer)
-                    markdown_content = layoutjson2md(layout_data)
-                    yield buffer, markdown_content, layout_data
-                    return
-                except json.JSONDecodeError:
-                    raise ValueError("JSON object not found in the model's output.")
-
-            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)}`\n\n**Raw Output:**\n```\n{buffer}\n```"
-            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>Tiny VLMs Lab🧪</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"
-                        )
-                        gr.Markdown("[Report-Bug💻](https://huggingface.co/spaces/prithivMLmods/OCR-Comparator/discussions)")
-                    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=50).launch(share=True, ssr_mode=False, show_error=True)