app.py

import gdown
import torch
from fastapi import FastAPI
from transformers import AutoModel, BertTokenizerFast
from pydantic import BaseModel
from model import BERT_Arch
from preprocess_data import remove_html, remove_links
import gradio as gr

# Define input model structure
class TextRequest(BaseModel):
    text: str

# Download model from Google Drive
model_url = "https://drive.google.com/uc?id=16ZWVa0d2V0T3s11Oq86rLOTA6bOR0DnR"
model_path = "model.pth"
gdown.download(model_url, model_path, quiet=False)

# Load pre-trained BERT model
bert = AutoModel.from_pretrained("bert-base-uncased")
for param in bert.parameters():
    param.requires_grad = False

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load your custom BERT-based model
model = BERT_Arch(bert)
model.load_state_dict(torch.load(model_path, map_location=device))
model.to(device)
model.eval()

# Load tokenizer
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")

# Initialize FastAPI
app = FastAPI()

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

@app.post("/predict/")
def predict(request: TextRequest):
    return {"prediction": classify_text(request.text)}

# Function to classify text
def classify_text(text: str) -> str:
    try:
        text = text.strip()
        text = remove_html(text)
        text = remove_links(text)

        tokens = tokenizer(
            text, return_tensors="pt", truncation=True,
            padding="max_length", max_length=512
        )

        input_ids = tokens["input_ids"].to(device)
        attention_mask = tokens["attention_mask"].to(device)

        with torch.no_grad():
            output = model(input_ids, attention_mask)

        prediction = torch.argmax(output.cpu(), dim=1).item()
        return "Phishing" if prediction == 1 else "Not Phishing"

    except Exception as e:
        return f"Error: {str(e)}"

# Gradio UI
gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(label="Enter website content or email text"),
    outputs=gr.Label(label="Prediction"),
    title="Phishing Text Detector",
    description="Website text to check if it's phishing."
).launch()