app.py

import json, base64
from io import BytesIO
from PIL import Image
import gradio as gr
from inference import OcrReorderPipeline
from transformers import (
    AutoProcessor,
    LayoutLMv3Model,
    T5ForConditionalGeneration,
    AutoTokenizer
)
import torch

# Load from your model repo
repo = "Uddipan107/ocr-layoutlmv3-base-t5-small"
model     = LayoutLMv3Model.from_pretrained(repo)
tokenizer = AutoTokenizer.from_pretrained(repo)
processor = AutoProcessor.from_pretrained(repo, apply_ocr=False)
pipe      = OcrReorderPipeline(model, tokenizer, processor, device=0)

def infer(image, words_json, boxes_json):
    words = json.loads(words_json)
    boxes = json.loads(boxes_json)
    buf = BytesIO(); image.save(buf, "PNG")
    b64 = base64.b64encode(buf.getvalue()).decode()
    # returns a list of strings; take first
    return pipe(b64, words, boxes)[0]

demo = gr.Interface(
    fn=infer,
    inputs=[
      gr.Image(type="pil", label="Image"),
      gr.Textbox(label="Words (JSON list)"),
      gr.Textbox(label="Boxes (JSON list)")
    ],
    outputs="text",
    title="OCR Reorder Pipeline"
)

if __name__ == "__main__":
    demo.launch()