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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)
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