app.py

import streamlit as st
from sentence_transformers import CrossEncoder

@st.cache_resource
def load_model(model_path) -> CrossEncoder:
    return CrossEncoder(model_path)


# Title and instructions
st.title("Typosquatting Detection using CrossEncoders")
st.markdown("Nowadays LLMs might feel like the reflexive first choice to solve tasks like typosquatting that require "
            "some reasoning capability to determine if one domain is spelled in such a way to look like another.  "
            "What we found was that we could fine tune an encoder-decoder model, but CrossEncoders performed equally as well "
            "with a smaller footprint in size and complexity.  CrossEncoders were orginally built to compare two sentences "
            "at the same time.  Here we use the same technique to compare two domains simultaneously.")
st.write("Enter two domains to check if one is a typosquatted variant of the other.")

model_choice="CE-typosquat-detect-Canine"
model_path = f"./{model_choice}"
model = load_model(model_path)
domain = st.text_input("Enter the legitimate domain name:", value="office365.com")
typosquat = st.text_input("Enter the potentially typosquatted domain name:", value="0ffice356.co")


# 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 > 0.5:
            st.success(f"The model predicts that '{typosquat}' is likely a typosquatted version of '{domain}' with a score of {prediction * 100:.2f} out of 100.")
        else:
            st.warning(f"The model predicts that '{typosquat}' is NOT likely a typosquatted version of '{domain}' with a score of {prediction * 100:.2f} out of 100.")
    else:
        st.error("Please enter both a legitimate domain and a potentially typosquatted domain.")