app.py

from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

app = FastAPI()

# Allow calls from MERN stack
origins = ["http://localhost:3000", "http://localhost:5000"]
app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_methods=["*"],
    allow_headers=["*"]
)

model_dir = "./spam_detector_model"

model = AutoModelForSequenceClassification.from_pretrained(model_dir)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
labels = list(model.config.id2label.values())

@app.post("/predict")
async def predict(req: Request):
    data = await req.json()
    inputs = tokenizer(data["text"], return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        pred = torch.argmax(probs).item()
        confidence = probs[0][pred].item()
    return {
        "label": labels[pred],
        "confidence": round(confidence, 3)
    }