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}")