app.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

app = FastAPI()

MODEL_NAME = "shobika04/harassment-nlp-model"

print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
model.eval()
print("Model loaded successfully!")

class TextRequest(BaseModel):
    text: str

@app.get("/")
def home():
    return {"status": "Harassment Detection API is running"}

@app.post("/predict")
def predict(request: TextRequest):
    inputs = tokenizer(
        request.text,
        return_tensors="pt",
        truncation=True,
        padding=True
    )

    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)

    predicted_class = torch.argmax(probs, dim=1).item()
    confidence = torch.max(probs).item()

    return {
        "prediction": int(predicted_class),
        "confidence": float(confidence)
    }