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Dockerfile

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+# 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
+
+# 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
+
+# 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"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Product Sales Prediction App")
+st.write("This tool predicts production sales prediction. Enter the required information below.")
+
+# Collect user input based on dataset columns
+weight = st.number_input("Product Weight", min_value=1, max_value=99999999)
+sugarcontent = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular Sugar", "reg"])
+area = st.number_input("Product allocated area", min_value=1, max_value=9999999)
+producttype = st.selectbox("Product type", ["Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "Snack Foods", "Meat", "Household", "Hard Drinks", "Fruits and Vegetables",
+"Breads", "Others", "Starchy Foods", Seafood"])
+productmrp = st.number_input("Product MRP", min_val=1, max_value=9999999)
+year = st.number_input("Store establishment year", min_value=1985, max_val=2024)
+storesize = st.selectbox("store size", ["Small", "Medium", "High"])
+citytype = st.number_input("City type", ["Tier1", "Tier2", "Tier3"])
+storetype = st.selectbox("store type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])
+
+# Convert categorical inputs to match model training
+customer_data = {
+    'Product Weight': weight
+    'Product Sugar Content':sugarcontent,
+    'Product allocated area': area,
+    'Product Type': producttype,
+    'Product MRP': productmrp,
+    'Store establishment year': year,
+    'store size': storesize,
+    'City type': citytype,
+    'store type': storetype,
+}
+
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://sp1505-frontend.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://sp1505-frontend.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