app.py

import gradio as gr
from transformers import AutoProcessor, AutoModelForImageTextToText
import torch
import os

device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")

os.environ["PYTORCH_SDP_ATTENTION"] = "0"  # Disable SDPA

def extract_text_from_image(image):
    inputs = processor(image, return_tensors="pt").to(device)
    generate_ids = model.generate(
        **inputs,
        do_sample=False,
        tokenizer=processor.tokenizer,
        stop_strings="<|im_end|>",
        max_new_tokens=4096,
    )
    return processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)

interface = gr.Interface(
    fn=extract_text_from_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Textbox(),
    title="OCR on Receipts",
    description="Upload an image to extract text using the GOT-OCR 2.0 model.",
    examples = [
        ["images/250406_01.jpg"],
        ["images/250409_01.jpg"]
    ]
)

interface.launch(share=False)