Update app.py

app.py

@@ -1,9 +1,7 @@
 import pandas as pd
 import pickle
-from sklearn.preprocessing import LabelEncoder
-import streamlit as st
 
-# Load the trained model from data.pkl
+# Load the trained model from data.pkl (assuming it's a custom model without sklearn)
 def load_model():
   with open('data.pkl', 'rb') as file:
     model = pickle.load(file)
@@ -11,26 +9,28 @@ def load_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]
+  # Preprocess the input data (assuming your model doesn't require LabelEncoder)
+  # ... (modify this section based on your specific model's preprocessing needs)
 
   # 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']
+                            'favourites_count', 'listed_count']  # Assuming language isn't used
   df_features = df[feature_columns_to_use]
 
   # Load the pre-trained model
   model = load_model()
 
-  # Make predictions using the loaded model
+  # Make predictions using the loaded model (assuming your model has a predict method)
   prediction = model.predict(df_features)
 
-  # Return the predicted class label (0 for fake, 1 for genuine)
-  return "Genuine" if prediction[0] == 1 else "Fake"
+  # Return the predicted class label (modify based on your model's output)
+  if prediction > 0.5:  # Assuming a threshold for genuine classification
+    return "Genuine"
+  else:
+    return "Fake"
 
 # Create the Streamlit app
 st.title('User Profile Classifier')
@@ -43,7 +43,7 @@ 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")
+# Remove language input since it's not used in this version
 
 # Create a dictionary to store user inputs
 user_input = {
@@ -53,7 +53,6 @@ user_input = {
   "favourites_count": favourites_count,
   "listed_count": listed_count,
   "name": name,
-  "Language": language
 }
 
 # Predict if the user clicks the button