app/main.py

import torch
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

MODEL_ID = "rudycaz/qwen3-4b-phishing-detection"

app = FastAPI(
    title="Phishing Email Detection API",
    description="Detects phishing emails using Qwen3-4B phishing detection model",
    version="1.0.0"
)

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(
    MODEL_ID,
    trust_remote_code=True
)

print("Loading model...")

# bnb_config = BitsAndBytesConfig(
#     load_in_8bit=True
# )

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    quantization_config=bnb_config,
    trust_remote_code=True
)

# model = AutoModelForCausalLM.from_pretrained(
#     MODEL_ID,
#     device_map="auto",
#     torch_dtype=torch.float16,
#     trust_remote_code=True,
#     ignore_mismatched_sizes=True
# )

print("Model loaded successfully!")


class EmailRequest(BaseModel):
    email: str


class EmailResponse(BaseModel):
    prediction: str
    model: str


def classify_email(email_text: str):

    prompt = (
        "You are a security assistant. Classify the following email as PHISHING or LEGIT.\n\n"
        f"EMAIL:\n{email_text}\n\n"
        "Answer with exactly one word: PHISHING or LEGIT."
    )

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    outputs = model.generate(
        **inputs,
        max_new_tokens=4,
        temperature=0
    )

    result = tokenizer.decode(outputs[0], skip_special_tokens=True)

    result_upper = result.upper()

    if "PHISHING" in result_upper:
        return "PHISHING"
    elif "LEGIT" in result_upper:
        return "LEGIT"
    else:
        return "UNKNOWN"


@app.get("/")
def root():
    return {"message": "Phishing Detection API is running"}


@app.post("/detect", response_model=EmailResponse)
def detect_email(data: EmailRequest):

    prediction = classify_email(data.email)

    return {
        "prediction": prediction,
        "model": MODEL_ID
    }