app.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import uvicorn

# Load model and tokenizer once at startup
MODEL_NAME = "16pramodh/t2s_model"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

# Create FastAPI app
app = FastAPI()

# Request body format
class QueryRequest(BaseModel):
    text: str

@app.post("/predict")
def predict(request: QueryRequest):
    try:
        inputs = tokenizer(request.text, return_tensors="pt")
        outputs = model.generate(**inputs, max_length=256)
        sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return {"sql": sql_query}
    except Exception as e:
        return {"error": str(e)}

# For local testing
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)