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import pandas as pd
import pickle
import streamlit as st
# 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)
return model
# Define the prediction function using the loaded model
def predict_user_profile(inputs):
# 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'] # 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 (assuming your model has a predict method)
prediction = model.predict(df_features)
# 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')
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")
# Remove language input since it's not used in this version
# 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,
}
# Predict if the user clicks the button
if st.button("Predict"):
prediction = predict_user_profile(user_input)
st.write(f"Prediction: {prediction}")
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