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import pickle
import pandas as pd
import streamlit as st
# Load the trained model
model = pickle.load(open("data.pkl", "rb"))
# Define a function to predict user data
def predict_user_data(user_data):
user_df = pd.DataFrame(user_data, index=[0])
user_df = extract_features(user_df) # Assuming the extract_features function is defined elsewhere in your code
prediction = model.predict(user_df)[0]
return prediction
# Streamlit app layout
st.title("Fake or Genuine User Classifier")
# Get user input
user_statuses_count = st.number_input("Statuses Count", min_value=0)
user_followers_count = st.number_input("Followers Count", min_value=0)
user_friends_count = st.number_input("Friends Count", min_value=0)
user_favourites_count = st.number_input("Favourites Count", min_value=0)
user_listed_count = st.number_input("Listed Count", min_value=0)
user_name = st.text_input("Name")
# Get user input as a dictionary
user_data = {
"statuses_count": user_statuses_count,
"followers_count": user_followers_count,
"friends_count": user_friends_count,
"favourites_count": user_favourites_count,
"listed_count": user_listed_count,
"name": user_name,
}
# Predict if the user clicks the button
if st.button("Classify User"):
prediction = predict_user_data(user_data)
if prediction == 1:
st.success("The user is likely Genuine.")
else:
st.warning("The user is likely Fake.")
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