app.py

import streamlit as st
from sentence_transformers import CrossEncoder

# Model selection
st.title("Typosquatting Detection App")
st.write("Enter two domains to check if one is a typosquatted variant of the other.")

model_choice = st.selectbox("Choose a model for detection:", ["CE-typosquat-detect-Canine", "CE-typosquat-detect"])
model_path = f"./{model_choice}"
model = CrossEncoder(model_path)

# User inputs
domain = st.text_input("Enter the legitimate domain name:")
typosquat = st.text_input("Enter the potentially typosquatted domain name:")
st.write("Recommended threshold for detection is 0.3.")
threshold = st.slider("Set detection threshold", 0.0, 1.0, 0.3)

# Typosquatting detection
if st.button("Check Typosquatting"):
    inputs = [(typosquat, domain)]
    prediction = model.predict(inputs)[0]
    
    # Display results
    if prediction > threshold:
        st.success(f"The model predicts that '{typosquat}' is likely a typosquatted version of '{domain}' with a score of {prediction:.4f}.")
    else:
        st.warning(f"The model predicts that '{typosquat}' is NOT likely a typosquatted version of '{domain}' with a score of {prediction:.4f}.")