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from fastapi import FastAPI, Request
from transformers import MarianMTModel, MarianTokenizer
import torch
app = FastAPI()
# Map target languages to Hugging Face model IDs
MODEL_MAP = {
"bg": "Helsinki-NLP/opus-mt-tc-big-en-bg",
"cs": "Helsinki-NLP/opus-mt-en-cs",
"da": "Helsinki-NLP/opus-mt-en-da",
"de": "Helsinki-NLP/opus-mt-en-de",
"el": "Helsinki-NLP/opus-mt-tc-big-en-el",
"es": "facebook/nllb-200-distilled-600M",
"et": "Helsinki-NLP/opus-mt-tc-big-en-et",
"fi": "Helsinki-NLP/opus-mt-tc-big-en-fi",
"fr": "Helsinki-NLP/opus-mt-en-fr",
"hr": "facebook/mbart-large-50-many-to-many-mmt",
"hu": "Helsinki-NLP/opus-mt-tc-big-en-hu",
"is": "facebook/nllb-200-distilled-600M",
"it": "facebook/nllb-200-distilled-600M",
"lt": "Helsinki-NLP/opus-mt-tc-big-en-lt",
"lv": "facebook/mbart-large-50-many-to-many-mmt",
"mk": "facebook/nllb-200-distilled-600M",
"nb": "facebook/mbart-large-50-many-to-many-mmt", #place holder!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"nl": "facebook/mbart-large-50-many-to-many-mmt",
"no": "facebook/mbart-large-50-many-to-many-mmt", #place holder!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"pl": "facebook/nllb-200-distilled-600M",
"pt": "facebook/mbart-large-50-many-to-many-mmt",
"ro": "facebook/mbart-large-50-many-to-many-mmt",
"sk": "Helsinki-NLP/opus-mt-en-sk",
"sl": "alirezamsh/small100",
"sq": "alirezamsh/small100",
"sv": "Helsinki-NLP/opus-mt-en-sv",
"tr": "facebook/nllb-200-distilled-600M"
}
MODEL_CACHE = {}
# ✅ Load Hugging Face model (Helsinki or Small100)
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]
# ✅ POST /translate
@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 found for target language '{target_lang}'"}
if model_id.startswith("facebook/"):
return {"translation": f"[{target_lang}] uses model '{model_id}', which is not supported in this Space yet."}
try:
tokenizer, model = load_model(model_id)
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(model.device)
outputs = model.generate(**inputs, num_beams=5, length_penalty=1.2, early_stopping=True)
return {"translation": tokenizer.decode(outputs[0], skip_special_tokens=True)}
except Exception as e:
return {"error": f"Translation failed: {str(e)}"}
# ✅ GET /languages
@app.get("/languages")
def list_languages():
return {"supported_languages": list(MODEL_MAP.keys())}
# ✅ GET /health
@app.get("/health")
def health():
return {"status": "ok"}
# ✅ Uvicorn startup (required by Hugging Face)
import uvicorn
if __name__ == "__main__":
uvicorn.run("app:app", host="0.0.0.0", port=7860) |