Create main.py

main.py

@@ -0,0 +1,52 @@
+import requests
+import streamlit as st
+import pandas as pd
+
+st.title("Customer Churn Prediction")
+
+# Input fields for customer data
+CustomerID = st.number_input("Customer ID", min_value=10000000, max_value=99999999)
+CreditScore = st.number_input("Credit Score (customer's credit score)", min_value=300, max_value=900, value=650)
+Geography = st.selectbox("Geography (country where the customer resides)", ["France", "Germany", "Spain"])
+Age = st.number_input("Age (customer's age in years)", min_value=18, max_value=100, value=30)
+Tenure = st.number_input("Tenure (number of years the customer has been with the bank)", value=12)
+Balance = st.number_input("Account Balance (customer’s account balance)", min_value=0.0, value=10000.0)
+NumOfProducts = st.number_input("Number of Products (number of products the customer has with the bank)", min_value=1, value=1)
+HasCrCard = st.selectbox("Has Credit Card?", ["Yes", "No"])
+IsActiveMember = st.selectbox("Is Active Member?", ["Yes", "No"])
+EstimatedSalary = st.number_input("Estimated Salary (customer’s estimated salary)", min_value=0.0, value=50000.0)
+
+customer_data = {
+    'CreditScore': CreditScore,
+    'Geography': Geography,
+    'Age': Age,
+    'Tenure': Tenure,
+    'Balance': Balance,
+    'NumOfProducts': NumOfProducts,
+    'HasCrCard': 1 if HasCrCard == "Yes" else 0,
+    'IsActiveMember': 1 if IsActiveMember == "Yes" else 0,
+    'EstimatedSalary': EstimatedSalary
+}
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://praneeth232-demo-cont.hf.space/v1/customer", json=customer_data)
+    if response.status_code == 200:
+        result = response.json()
+        churn_prediction = result["Churn expected?"]  # 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
+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'):
+        response = requests.post("https://praneeth232-demo-cont.hf.space/v1/customerbatch", files={"file": file})
+        if response.status_code == 200:
+            result = response.json()
+            st.header("Batch Prediction Results")
+            # st.write(result["predictions"])
+            st.write(result)
+        else:
+            st.error("Error in API request")