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import pickle |
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import pandas as pd |
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import streamlit as st |
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model = pickle.load(open("data.pkl", "rb")) |
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def predict_user_data(user_data): |
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user_df = pd.DataFrame(user_data, index=[0]) |
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user_df = extract_features(user_df) |
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prediction = model.predict(user_df)[0] |
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return prediction |
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st.title("Fake or Genuine User Classifier") |
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user_statuses_count = st.number_input("Statuses Count", min_value=0) |
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user_followers_count = st.number_input("Followers Count", min_value=0) |
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user_friends_count = st.number_input("Friends Count", min_value=0) |
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user_favourites_count = st.number_input("Favourites Count", min_value=0) |
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user_listed_count = st.number_input("Listed Count", min_value=0) |
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user_name = st.text_input("Name") |
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user_data = { |
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"statuses_count": user_statuses_count, |
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"followers_count": user_followers_count, |
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"friends_count": user_friends_count, |
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"favourites_count": user_favourites_count, |
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"listed_count": user_listed_count, |
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"name": user_name, |
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} |
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if st.button("Classify User"): |
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prediction = predict_user_data(user_data) |
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if prediction == 1: |
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st.success("The user is likely Genuine.") |
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else: |
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st.warning("The user is likely Fake.") |
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