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import streamlit as st
import pandas as pd
import requests
# Streamlit UI for Customer Churn Prediction
st.title("Telecom Customer Churn Prediction App")
st.write("This tool predicts customer churn risk based on their details. Enter the required information below.")
# Collect user input based on dataset columns
CustomerID = st.number_input("Customer ID", min_value=10000000, max_value=99999999)
SeniorCitizen = st.selectbox("Senior citizen", ["Yes", "No"])
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)
# Convert categorical inputs to match model training
customer_data = {
'SeniorCitizen': 1 if SeniorCitizen == "Yes" else 0,
'Partner': Partner,
'Dependents': Dependents,
'tenure': tenure,
'PhoneService': PhoneService,
'InternetService': InternetService,
'Contract': Contract,
'PaymentMethod': PaymentMethod,
'MonthlyCharges': MonthlyCharges,
'TotalCharges': TotalCharges
}
# Single Prediction
if st.button("Predict", type='primary'):
response = requests.post(
"https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customer",
json=customer_data
) # Send data to Flask API
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 {CustomerID} is likely to {churn_prediction}.")
else:
st.error("Error in API request")
# Batch Prediction Section
st.subheader("Batch Prediction")
file = st.file_uploader("Upload CSV file", type=["csv"])
if file is not None:
if st.button("Predict for Batch", type='primary'):
try:
file_data = {"file": ("batch.csv", file.getvalue(), "text/csv")
}
response = requests.post(
"https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customerbatch",
files=file_data
)
response.raise_for_status() # Raise error if request fails
result = response.json()
st.header("Batch Prediction Results")
st.write(result)
except requests.exceptions.RequestException as e:
st.error(f"Error in API request: {e}")
except Exception as e:
st.error(f"Unexpected error: {e}")
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