import gradio as gr
from huggingface_hub import InferenceClient
from fastapi import FastAPI, Request
from gradio.routes import mount_gradio_app

client = InferenceClient("dlucidone/kumaoni-mbart-lora")

def translate(text: str):
    if not text.strip():
        return "Please enter text."
    try:
        response = client.post(json={"inputs": text})
        if isinstance(response, list) and len(response) and "generated_text" in response[0]:
            return response[0]["generated_text"]
        return str(response)
    except Exception as e:
        return f"Error: {e}"

# Create a FastAPI app
api = FastAPI()

# Add /predict endpoint
@api.post("/predict")
async def predict(request: Request):
    body = await request.json()
    text = body.get("text", "")
    return {"translation": translate(text)}

# Mount Gradio interface for human testing
gr_app = gr.Interface(fn=translate, inputs="text", outputs="text", title="Kumaoni Translator")
app = mount_gradio_app(api, gr_app, path="/")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)