app.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import pipeline
from typing import List

app = FastAPI(
   title = "Hate Speech Detection API",
    description = "A simple API to classify text using the unitary/toxic-bert model.",
    version = "1.0.0"
)

classifier= pipeline("text-classification" , model="unitary/toxic-bert", tokenizer="unitary/toxic-bert", device=-1)

#writing pydantic models
class TextInput(BaseModel):
    text: str



@app.get("/")
def get_root():
    return {"message": "Welcome to the Hate Speech Detection API!"}

@app.post("/predict")
def predict_toxicity(input: TextInput):
    classifier_result = classifier(input.text)
    prediction=list(classifier_result)[0]
    final_prediction = {}
    if prediction['score']>0.5:
        final_prediction['label']='Toxic'
        final_prediction['non-toxic-score']=1-prediction['score']
        final_prediction['toxic-score']=prediction['score']
    else:
        final_prediction['label']='Non-Toxic'
        final_prediction['non-toxic-score']=1- prediction['score']
        final_prediction['toxic-score']=prediction['score']   
    
    return final_prediction