Create app.py

app.py

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+import pandas as pd
+import pickle
+from sklearn.preprocessing import LabelEncoder
+import streamlit as st
+
+# Load the trained model from data.pkl
+def load_model():
+  with open('data.pkl', 'rb') as file:
+    model = pickle.load(file)
+  return model
+
+# Define the prediction function using the loaded model
+def predict_user_profile(inputs):
+  # Preprocess the input data
+  lang_encoder = LabelEncoder()
+  lang_code = lang_encoder.fit_transform([inputs['Language']])[0]
+
+  # Create a DataFrame from the user input dictionary
+  df = pd.DataFrame.from_dict([inputs])
+
+  # Select the relevant feature columns used during model training
+  feature_columns_to_use = ['statuses_count', 'followers_count', 'friends_count',
+                            'favourites_count', 'listed_count', 'lang_code']
+  df_features = df[feature_columns_to_use]
+
+  # Load the pre-trained model
+  model = load_model()
+
+  # Make predictions using the loaded model
+  prediction = model.predict(df_features)
+
+  # Return the predicted class label (0 for fake, 1 for genuine)
+  return "Genuine" if prediction[0] == 1 else "Fake"
+
+# Create the Streamlit app
+st.title('User Profile Classifier')
+st.write('Predict whether a user profile is genuine or fake.')
+
+# Create input fields for user data
+statuses_count = st.number_input("Statuses Count", min_value=0)
+followers_count = st.number_input("Followers Count", min_value=0)
+friends_count = st.number_input("Friends Count", min_value=0)
+favourites_count = st.number_input("Favourites Count", min_value=0)
+listed_count = st.number_input("Listed Count", min_value=0)
+name = st.text_input("Name")
+language = st.text_input("Language")
+
+# Create a dictionary to store user inputs
+user_input = {
+  "statuses_count": statuses_count,
+  "followers_count": followers_count,
+  "friends_count": friends_count,
+  "favourites_count": favourites_count,
+  "listed_count": listed_count,
+  "name": name,
+  "Language": language
+}
+
+# Predict if the user clicks the button
+if st.button("Predict"):
+  prediction = predict_user_profile(user_input)
+  st.write(f"Prediction: {prediction}")