from fastapi import FastAPI, Request
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

app = FastAPI()

# Load model + tokenizer
model_name = "grammarly/coedit-xl"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

class InputText(BaseModel):
    text: str

@app.post("/correct")
async def correct_text(data: InputText):
    input_text = data.text
    inputs = tokenizer(input_text, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=256)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"corrected": result}