from fastapi import FastAPI
from pydantic import BaseModel
import joblib

app = FastAPI()

class PredictRequest(BaseModel):
    text: str

# Load model and vectorizer
model = joblib.load("english_model.pkl")
vectorizer = joblib.load("english_tfid.pkl")
label_encoder = joblib.load("english_label_encoder.pkl")

@app.get("/")
def root():
    return {"message": "Welcome to the English model API endpoint"}

@app.post("/predict")
def predict(request: PredictRequest):
    vect = vectorizer.transform([request.text])
    pred = model.predict(vect)                 # returns encoded int
    label = label_encoder.inverse_transform(pred)  # convert back to string

    # Map label strings to friendly messages
    mapping = {
        "age": "Hate regarding age",
        "ethnicity": "Hate regarding ethnicity",
        "gender": "Hate regarding gender",
        "not_cyberbullying": "Not Hate",
        "other_cyberbullying": "Other hate",
        "religion": "Hate regarding religion"
    }

    return {"prediction": mapping[label[0]]}