prediction.py

# import library
import streamlit as st
import pandas as pd
import numpy as np
import pickle

# Load Model
with open('model.pkl', 'rb') as file:
    model = pickle.load(file)

# Function to run streamlit model predictor
def run():

   # Set Title
    st.title("Customer Churn Prediction")
    st.markdown('---')

    # Create a Form for Data Inference
    st.markdown('## Input Data')
    with st.form('my_form'):
        RowNumber = st.number_input('Row Number', min_value=10000, max_value=200000)
        CustomerId = st.number_input('Customer ID', min_value=100000, max_value=20000000)
        Surname = st.text_input('Surname or Last Name', '')
        CreditScore = st.number_input('Credit Score', min_value=350, max_value=850)
        Geography = st.selectbox('Select Geography', ['Spain', 'Germany', 'France'])
        Gender = st.selectbox('Select gender', ['Male', 'Female'])
        Age = st.number_input('Age', min_value=18, max_value=95)
        Tenure  = st.number_input('Tenure', min_value=0, max_value=11)
        Balance = st.number_input('Balance', min_value=0, max_value=300000)
        NumOfProducts = st.selectbox('Number of Products', (1,2,3,4))
        HasCrCard = st.selectbox('Has Credit Card or not? 0 = No, Yes = 1', (0,1))
        IsActiveMember = st.selectbox('Is Active Member or not? 0 = No, Yes = 1', (0,1))
        EstimatedSalary = st.number_input('Estimated Salary', min_value=12, max_value=300000)

        # Create a button to make predictions
        submitted = st.form_submit_button("Predict")

    # Dataframe
    data = {'RowNumber': RowNumber,
            'CustomerId': CustomerId,
            'Surname': Surname,
            'CreditScore': CreditScore,
            'Geography': Geography,
            'Gender': Gender, 
            'Age': Age,
            'Tenure': Tenure, 
            'Balance': Balance,
            'NumOfProducts': NumOfProducts,
            'HasCrCard': HasCrCard,
            'IsActiveMember': IsActiveMember,
            'EstimatedSalary': EstimatedSalary
            }

    df = pd.DataFrame([data])
    st.dataframe(df)

    if submitted:        
        y_pred_inf = model.predict(df)
        if y_pred_inf[0] == 0:
            st.write('~ This Customer is Predicted Not to Churn ~')
        else:
            st.write('~ This Customer is Predicted to Churn ~')

if __name__== '__main__':
    run()