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+# FROM python:3.9-slim
+# WORKDIR /app
+# COPY . .
+# COPY requirements.txt .
+# COPY app.py .   
+# RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# CMD ["streamlit", "run", "app.py", "server.address=0.0.0.0", "server.port=8501"]
+FROM python:3.9-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY app.py .
+CMD ["streamlit", "run", "app.py", "--server.port=7861", "--server.address=0.0.0.0"]

app.py

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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
+}
+
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://MainiSandeep1987-FlaskAPITelecomChurnPrediction.hf.space/v1/customer", json=customer_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 {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://MainiSandeep1987-FlaskAPITelecomChurnPrediction.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")

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2