app.py

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

import torch

app = FastAPI()

# Load model
model_name = "VietAI/vit5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Input format
class TextInput(BaseModel):
    text: str

@app.get("/")
def read_root():
    return {"message": "ViT5 summarization API is running!"}

@app.post("/summarize")
def summarize(input: TextInput):
    try:
        input_text = f"summarize: {input.text}"
        inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
        summary_ids = model.generate(
            inputs,
            max_length=128,
            min_length=20,
            num_beams=4,
            no_repeat_ngram_size=3,
            repetition_penalty=2.5,
            length_penalty=1.0,
            early_stopping=True
        )
        output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        return {"summary": output}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))