Create app.py

app.py

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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.")
+