app.py

from fastapi import FastAPI
from pydantic import BaseModel
import os
import torch
from transformers import BartTokenizer, BartForSequenceClassification, pipeline

app = FastAPI()

tokens = os.getenv("HF_TOKEN")
model_name = "iconcube/BART-large_classifier"
classifier_tokenizer = BartTokenizer.from_pretrained(model_name)
classifier_model = BartForSequenceClassification.from_pretrained(model_name)

classifier = pipeline(
    "text-classification",
    model=classifier_model,
    tokenizer=classifier_tokenizer,
    token=tokens
)

class RequestText(BaseModel):
    text: str

class ResponseLabel(BaseModel):
    label: str

@app.post("/predict", response_model=ResponseLabel)
async def predict(request: RequestText):
    result = classifier(request.text)[0]
    label = result["label"]
    if label == "LABEL_0":
        message = "safe_response"
    elif label == "LABEL_1":
        message = "unsafe_response"
    else:
        message = "error"
    return ResponseLabel(label=message)