prediction.py

import streamlit as st
import numpy as np
import pickle

# Load the trained Random Forest classifier
with open('model.pkl', 'rb') as file:
    model = pickle.load(file)

def run_prediction_app():
    st.subheader('Predict Revenue Generation')
    
    # Taking input from the user
    Administrative = st.number_input('Administrative', value=0)
    Administrative_Duration = st.number_input('Administrative Duration', value=0.0)
    Informational = st.number_input('Informational', value=0)
    Informational_Duration = st.number_input('Informational Duration', value=0.0)
    ProductRelated = st.number_input('ProductRelated', value=0)
    ProductRelated_Duration = st.number_input('ProductRelated Duration', value=0.0)
    BounceRates = st.number_input('BounceRates', value=0.0)
    ExitRates = st.number_input('ExitRates', value=0.0)
    PageValues = st.number_input('PageValues', value=0.0)
    SpecialDay = st.number_input('SpecialDay', value=0.0)
    Month = st.selectbox('Month', ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
    OperatingSystems = st.number_input('Operating Systems', value=1)
    Browser = st.number_input('Browser', value=1)
    Region = st.number_input('Region', value=1)
    TrafficType = st.number_input('Traffic Type', value=1)
    VisitorType = st.selectbox('Visitor Type', ['Returning_Visitor', 'New_Visitor', 'Other'])
    Weekend = st.checkbox('Weekend?')
    
    # When 'Predict' is clicked, make the prediction and store it 
    if st.button('Predict'):
        input_data = {
            'Administrative': Administrative,
            'Administrative_Duration': Administrative_Duration,
            'Informational': Informational,
            'Informational_Duration': Informational_Duration,
            'ProductRelated': ProductRelated,
            'ProductRelated_Duration': ProductRelated_Duration,
            'BounceRates': BounceRates,
            'ExitRates': ExitRates,
            'PageValues': PageValues,
            'SpecialDay': SpecialDay,
            'Month': Month,
            'OperatingSystems': OperatingSystems,
            'Browser': Browser,
            'Region': Region,
            'TrafficType': TrafficType,
            'VisitorType': VisitorType,
            'Weekend': Weekend
        }
        
        # Make prediction
        prediction = model.predict([list(input_data.values())])[0]
        st.write(f"Prediction: {'Revenue' if prediction else 'No Revenue'}")