app.py

import streamlit as st
from sentence_transformers import CrossEncoder

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

# Model selection
# model_choice = st.selectbox("Choose a model for detection:", ["CE-typosquat-detect-Canine", "CE-typosquat-detect"])

# # Load model after selection
# if model_choice:
#     model_path = f"./{model_choice}"
#     model = CrossEncoder(model_path)
model_choice="CE-typosquat-detect-Canine"
# User inputs for domains and threshold
domain = st.text_input("Enter the legitimate domain name:")
typosquat = st.text_input("Enter the potentially typosquatted domain name:")
threshold = st.slider("Set detection threshold", 0.0, 1.0, 0.5)

# Typosquatting detection on button click
if st.button("Check Typosquatting"):
    if domain and typosquat:
        inputs = [(typosquat, domain)]
        prediction = model.predict(inputs)[0]
        
        # Display result
        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}.")
    else:
        st.error("Please enter both a legitimate domain and a potentially typosquatted domain.")