import gradio as gr
from transformers import pipeline
import transformers
from functools import lru_cache


@lru_cache(maxsize=4)
def get_pipeline(task: str, model_url: str):
    print(f"Loading pipeline: task={task}, model={model_url}")
    return pipeline(task, model=model_url)

def translate_text(task, modelUrl, chunk_to_translate):
    print(transformers.__version__)

    modelUrl = modelUrl.strip()
    task = task.strip()

    pipe = get_pipeline(task, modelUrl)
    result = pipe(chunk_to_translate)
    print(f"translated chunk is: {result}")

    return result[0]['translation_text']

demo = gr.Interface(
    fn=translate_text,
    inputs=["text", "text", "text"],
    outputs="text",
    title="academic translator",
    description="translates from academic german to English"
)

demo.launch(share=True, mcp_server=True)