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Dockerfile

@@ -1,21 +1,16 @@
+# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,58 @@
+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 = pd.DataFrame ([{
+    '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=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 - 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")
+

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2