app.py

# Install FlashAttention
import subprocess
subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)

import base64
from io import BytesIO
import re

from PIL import Image, ImageDraw
import gradio as gr
import spaces
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info


repo_id = "yuki-imajuku/Qwen2.5-VL-7B-Instruct-Manga109-FT-OCR-Page-VQA"
processor = AutoProcessor.from_pretrained(repo_id)


def pil2base64(image: Image.Image) -> str:
    buffered = BytesIO()
    image.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode()


@spaces.GPU
@torch.inference_mode()
def inference_fn(
    image: Image.Image | None,
    # progress=gr.Progress(track_tqdm=True),
) -> tuple[str, Image.Image | None]:
    if image is None:
        gr.Warning("Please upload an image!", duration=10)
        return "Please upload an image!", None

    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        repo_id,
        torch_dtype=torch.bfloat16,
        attn_implementation="flash_attention_2",
        device_map=device,
    )

    base64_image = pil2base64(image)
    messages = [
        {"role": "user", "content": [
            {"type": "image", "image": f"data:image;base64,{base64_image}"},
            {"type": "text", "text": "With this image, please output the result of OCR with grounding."}
        ]},
    ]
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to(model.device)

    generated_ids = model.generate(**inputs, max_new_tokens=1024)
    generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
    raw_output = processor.batch_decode(
        generated_ids_trimmed,
        skip_special_tokens=False,
        clean_up_tokenization_spaces=False,
    )[0]

    print(raw_output)

    result_image = image_inputs[0].copy()
    draw = ImageDraw.Draw(result_image)
    ocr_texts = []
    for ocr_text, ocr_quad in re.findall(r"<\|object_ref_start\|>(.+?)<\|object_ref_end\|><\|quad_start\|>([\d,]+)<\|quad_end\|>", raw_output):
        ocr_texts.append(f"{ocr_text} -> {ocr_quad}")
        quad = [int(x) for x in ocr_quad.split(",")]
        for i in range(4):
            start_point = quad[i*2:i*2+2]
            end_point = quad[i*2+2:i*2+4] if i < 3 else quad[:2]
            draw.line(start_point + end_point, fill="red", width=4)
    ocr_texts_str = "\n".join(ocr_texts)

    return ocr_texts_str, result_image


with gr.Blocks() as demo:
    gr.Markdown("# Manga Panel OCR")
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label="Input Image", image_mode="RGB", type="pil")
            input_button = gr.Button(value="Submit")
        with gr.Column():
            ocr_text = gr.Textbox(label="Result", lines=5)
            ocr_image = gr.Image(label="OCR Result", type="pil", show_label=False)

    input_button.click(
        fn=inference_fn,
        inputs=[input_image],
        outputs=[ocr_text, ocr_image],
    )
    ocr_examples = gr.Examples(
        examples=[],
        fn=inference_fn,
        inputs=[input_image],
        outputs=[ocr_text, ocr_image],
        cache_examples=False,
    )

demo.queue().launch()