from fastapi import FastAPI, Request
from transformers import MarianMTModel, MarianTokenizer
import torch

app = FastAPI()

MODEL_MAP = {
    "fr": "Helsinki-NLP/opus-mt-en-fr",
    "de": "Helsinki-NLP/opus-mt-en-de"
}

MODEL_CACHE = {}

def load_model(model_id):
    if model_id not in MODEL_CACHE:
        tokenizer = MarianTokenizer.from_pretrained(model_id)
        model = MarianMTModel.from_pretrained(model_id).to("cpu")
        MODEL_CACHE[model_id] = (tokenizer, model)
    return MODEL_CACHE[model_id]

@app.post("/translate")
async def translate(request: Request):
    data = await request.json()
    text = data.get("text")
    target_lang = data.get("target_lang")

    if not text or not target_lang:
        return {"error": "Missing text or target_lang"}

    model_id = MODEL_MAP.get(target_lang)
    if not model_id:
        return {"error": f"No model for '{target_lang}'"}

    tokenizer, model = load_model(model_id)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(model.device)
    outputs = model.generate(**inputs)
    return {"translation": tokenizer.decode(outputs[0], skip_special_tokens=True)}

# Required for FastAPI to run on HF Spaces
import uvicorn
if __name__ == "__main__":
    uvicorn.run("app:app", host="0.0.0.0", port=7860)