main.py

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

# Set cache directory to avoid permission errors
os.environ["HF_HOME"] = "/tmp/hf-cache"

app = FastAPI()

# Load tokenizer and model once at startup
tokenizer = AutoTokenizer.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")
model = AutoModelForSequenceClassification.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")

class EmailInput(BaseModel):
    text: str

@app.post("/predict")
def predict(input: EmailInput):
    inputs = tokenizer(input.text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

    probs = predictions[0].tolist()
    labels = {
        "legitimate_email": probs[0],
        "phishing_email": probs[1],
        "legitimate_url": probs[2],
        "phishing_url": probs[3]
    }
    max_label = max(labels.items(), key=lambda x: x[1])

    return {
        "prediction": max_label[0],
        "confidence": round(max_label[1], 4),
        "all_probabilities": labels
    }