app.py

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

app = FastAPI()

# Load model and tokenizer
model_name = "Bijoy09/your_mobilebert_model_repo"  # replace with your Hugging Face repo name
try:
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
except Exception as e:
    raise RuntimeError(f"Failed to load model or tokenizer: {e}")

class TextRequest(BaseModel):
    text: str

@app.post("/predict/")
async def predict(request: TextRequest):
    try:
        model.eval()
        inputs = tokenizer.encode_plus(
            request.text,
            add_special_tokens=True,
            max_length=64,
            truncation=True,
            padding='max_length',
            return_attention_mask=True,
            return_tensors='pt'
        )
        with torch.no_grad():
            logits = model(inputs['input_ids'], attention_mask=inputs['attention_mask']).logits
            prediction = torch.argmax(logits, dim=1).item()
        return {"prediction": "Spam" if prediction == 1 else "Ham"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Prediction failed: {e}")

@app.get("/")
async def root():
    return {"message": "Welcome to the MobileBERT API"}