app.py

import requests
import streamlit as st
import pandas as pd

st.title ("Customer Churn Prediction - Week1")

st.write ("This tool predicts customer churn risk based on their details. Enter the required information below.")

# Collect user input based on dataset columns
CustId          = st.text_input ("Customer ID", value="12345")
Age             = st.number_input ("Age", min_value=0, max_value=200, value=23)
Partner         = st.selectbox ("Does the customer have a partner?", ["Yes", "No"])
Dependents      = st.selectbox ("Does the customer have dependents?", ["Yes", "No"])
PhoneService    = st.selectbox ("Does the customer have phone service?", ["Yes", "No"])
InternetService = st.selectbox ("Type of Internet Service", ["DSL", "Fiber optic", "No"])
Contract        = st.selectbox ("Type of Contract", ["Month-to-month", "One year", "Two year"])
PaymentMethod   = st.selectbox ("Payment Method", ["Electronic check", "Mailed check", "Bank transfer", "Credit card"])
Tenure          = st.number_input ("Tenure (Months with the company)", min_value=0, value=12)
MonthlyCharges  = st.number_input ("Monthly Charges", min_value=0.0, value=50.0)
TotalCharges    = st.number_input ("Total Charges", min_value=0.0, value=600.0)

input_data = {
    'customerID': CustId,
    'SeniorCitizen': 1 if Age > 60 else 0,
    'tenure': Tenure,
    'MonthlyCharges': MonthlyCharges,
    'TotalCharges': TotalCharges,
    'Partner': Partner,
    'Dependents': "Yes",
    'PhoneService': PhoneService,
    'InternetService': InternetService,
    'Contract': Contract,
    'PaymentMethod': PaymentMethod
}

if st.button("Predict", type='primary'):
    response = requests.post ("https://harishsohani-CustChurnWeek1BackEnd.hf.space/v1/customer", json=input_data)    # enter user name and space name before running the cell
    if response.status_code == 200:
        result = response.json ()
        churn_prediction = result ["Prediction"]  # Extract only the value
        st.write (f"Based on the information provided, the customer with ID {CustId} is likely to {churn_prediction}.")
    else:
        st.error ("Error in API request -" + str(response.status_code))

# Batch Prediction
st.subheader ("Batch Prediction - Week1")

file = st.file_uploader ("Upload CSV file", type=["csv"])
if file is not None:
    if st.button("Predict for Batch", type='primary'):
        response = requests.post("https://harishsohani-CustChurnWeek1BackEnd.hf.space/v1/customerbatch", files={"file": file})    # enter user name and space name before running the cell
        if response.status_code == 200:
            result = response.json()
            st.header("Batch Prediction Results")
            st.write(result)
        else:
            st.error("Error in API request"+str(response.status_code))