app.py

import os
import importlib.util
from huggingface_hub import hf_hub_download
import gradio as gr

infinity_repo_2026 = "SoyVitou/infinity-khmer-ocr-large"
infinity_model = "model.py"

EXAMPLES = [
    ["./image.png"],
    ["./image1.png"],
    ["./image2.png"],
    ["./image3.png"],
    ["./image4.png"],
]


def import_private_module(repo_id: str, filename: str, token: str):
    path = hf_hub_download(repo_id=repo_id, filename=filename, token=token)
    spec = importlib.util.spec_from_file_location("private_model", path)
    mod = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(mod)
    return mod


HF_TOKEN = os.environ.get("HF_TOKEN", "").strip()
if not HF_TOKEN:
    raise RuntimeError("HF_TOKEN not found.")

pm = import_private_module(infinity_repo_2026, infinity_model, HF_TOKEN)
MODEL, TOK, CFG_OBJ, DEVICE = pm.load_from_private_repo(hf_hub_download, HF_TOKEN)


def predict(img):
    return pm.predict_one(MODEL, TOK, CFG_OBJ, DEVICE, img)


demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload image"),
    outputs=gr.Textbox(label="OCR result", lines=6),
    title="Infinity Large Khmer OCR",
    examples=EXAMPLES,
    cache_examples=False,
)

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