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from fastapi import FastAPI
from typing import List
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from IndicTransToolkit import IndicProcessor
from fastapi.middleware.cors import CORSMiddleware
import os
os.environ["HF_HOME"] = "/.cache"
# Initialize FastAPI
app = FastAPI()
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Initialize models and processors
model = AutoModelForSeq2SeqLM.from_pretrained(
"ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
"ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True
)
ip = IndicProcessor(inference=True)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(DEVICE)
def translate_text(sentences: List[str], target_lang: str):
try:
src_lang = "eng_Latn"
batch = ip.preprocess_batch(sentences, src_lang=src_lang, tgt_lang=target_lang)
inputs = tokenizer(
batch,
truncation=True,
padding="longest",
return_tensors="pt",
return_attention_mask=True,
).to(DEVICE)
with torch.no_grad():
generated_tokens = model.generate(
**inputs,
use_cache=True,
min_length=0,
max_length=256,
num_beams=5,
num_return_sequences=1,
)
with tokenizer.as_target_tokenizer():
generated_tokens = tokenizer.batch_decode(
generated_tokens.detach().cpu().tolist(),
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
translations = ip.postprocess_batch(generated_tokens, lang=target_lang)
return {
"translations": translations,
"source_language": src_lang,
"target_language": target_lang,
}
except Exception as e:
raise Exception(f"Translation failed: {str(e)}")
# FastAPI routes
@app.get("/health")
async def health_check():
return {"status": "healthy"}
@app.post("/translate")
async def translate_endpoint(sentences: List[str], target_lang: str):
try:
result = translate_text(sentences=sentences, target_lang=target_lang)
return result
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
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