app.py

'''import os
import uuid
import time
from threading import Thread

import gradio as gr
import torch
from PIL import Image
from transformers import (
    Qwen2VLForConditionalGeneration,
    AutoProcessor,
    TextIteratorStreamer,
)

# Constants
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load olmOCR-7B-0225-preview
MODEL_ID = "allenai/olmOCR-7B-0225-preview"
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to(device).eval()

def generate_image(text: str, image: Image.Image,
                   max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
                   temperature: float = 0.6,
                   top_p: float = 0.9,
                   top_k: int = 50,
                   repetition_penalty: float = 1.2):
    """
    Generates responses using olmOCR-7B-0225-preview for image input.
    """
    if image is None:
        yield "Please upload an image.", "Please upload an image."
        return

    messages = [{
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": text},
        ]
    }]
    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=False,
        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()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        time.sleep(0.01)
        yield buffer, buffer

def save_to_md(output_text):
    file_path = f"result_{uuid.uuid4()}.md"
    with open(file_path, "w") as f:
        f.write(output_text)
    return file_path

# Gradio UI
image_examples = [
    ["Convert this page to doc [text] precisely.", "images/3.png"],
    ["Convert this page to doc [text] precisely.", "images/4.png"],
    ["Convert this page to doc [text] precisely.", "images/1.png"],
    ["Convert chart to OTSL.", "images/2.png"]
]

css = """
.submit-btn {
    background-color: #2980b9 !important;
    color: white !important;
}
.submit-btn:hover {
    background-color: #3498db !important;
}
.canvas-output {
    border: 2px solid #4682B4;
    border-radius: 10px;
    padding: 20px;
}
"""

with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
    gr.Markdown("# **Doc OCR - olmOCR-7B-0225-preview**")

    with gr.Row():
        with gr.Column():
            image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
            image_upload = gr.Image(type="pil", label="Upload Image")
            image_submit = gr.Button("Submit", elem_classes="submit-btn")
            gr.Examples(
                examples=image_examples,
                inputs=[image_query, image_upload]
            )

            with gr.Accordion("Advanced options", open=False):
                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
                top_p = gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
                top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)

        with gr.Column():
            with gr.Column(elem_classes="canvas-output"):
                gr.Markdown("## Output")
                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
                with gr.Accordion("Result.md", open=False):
                    markdown_output = gr.Markdown(label="(Result.md)")

            gr.Markdown("**Model: olmOCR-7B-0225-preview**")
            gr.Markdown("> [`olmOCR-7B`](https://huggingface.co/allenai/olmOCR-7B-0225-preview) is optimized for high-fidelity document OCR and LaTeX-aware image-to-text tasks.")

    image_submit.click(
        fn=generate_image,
        inputs=[image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
        outputs=[output, markdown_output]
    )

if __name__ == "__main__":
    demo.queue(max_size=30).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)'''