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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 joblib
+import pandas as pd
+import numpy as np
+
+# Load the trained Random Forest pipeline model
+try:
+    model = joblib.load('random_forest_pipeline.pkl')
+except FileNotFoundError:
+    st.error("Model file 'random_forest_pipeline.pkl' not found. Please ensure the model is trained and saved.")
+    model = None
+
+st.title('SuperKart Sales Prediction App')
+
+if model:
+    st.sidebar.header('Input Features')
+
+    # Define input fields for each feature based on your dataset columns
+    # Product_Weight
+    product_weight = st.sidebar.number_input('Product Weight(kg)', min_value=0.1, max_value=30.0, value=10.0)
+
+    # Product_Sugar_Content
+    sugar_content_options = ['Low Sugar', 'Regular', 'No Sugar', 'reg'] # Based on EDA
+    product_sugar_content = st.sidebar.selectbox('Product Sugar Content', sugar_content_options)
+
+    # Product_Allocated_Area
+    product_allocated_area = st.sidebar.number_input('Product Allocated Area (sq. m)', min_value=0.0, max_value=1.0, value=0.05)
+
+    # Product_Type
+    product_type_options = ['Breads', 'Snack Foods', 'Frozen Foods', 'Dairy', 'Seafood', 'Starchy Foods', 'Soft Drinks', 'Meat', 'Hard Drinks', 'Health and Hygiene', 'Baking Goods', 'Breakfast', 'Canned', 'Fruits and Vegetables', 'Household', 'Others'] # Based on EDA
+    product_type = st.sidebar.selectbox('Product Type', product_type_options)
+
+    # Product_MRP
+    product_mrp = st.sidebar.number_input('Product MRP ($)', min_value=10.0, max_value=300.0, value=150.0)
+
+    # Store_Id - Using an example list, replace with actual Store IDs from your data if possible
+    store_id_options = ['OUT027', 'OUT013', 'OUT011', 'OUT010', 'OUT004', 'OUT001', 'OUT002', 'OUT003']
+    store_id = st.sidebar.selectbox('Store ID', store_id_options)
+
+    # Store_Establishment_Year
+    store_establishment_year = st.sidebar.number_input('Store Establishment Year', min_value=1900, max_value=2024, value=2000)
+
+    # Store_Size
+    store_size_options = ['Medium', 'High', 'Low']
+    store_size = st.sidebar.selectbox('Store Size', store_size_options)
+
+    # Store_Location_City_Type
+    city_type_options = ['Tier 1', 'Tier 2', 'Tier 3']
+    store_location_city_type = st.sidebar.selectbox('Store Location City Type', city_type_options)
+
+    # Store_Type
+    store_type_options = ['Departmental Store', 'Supermarket Type 1', 'Supermarket Type 2', 'Food Mart']
+    store_type = st.sidebar.selectbox('Store Type', store_type_options)
+
+    # Product_Id - Although not used in the model, it's in the original data structure
+    # For prediction, we can use a placeholder or a dummy value if not strictly needed by the preprocessor
+    product_id = 'dummy_product_id' # Placeholder
+
+    # Create a DataFrame with the input features
+    input_data = pd.DataFrame({
+        'Product_Id': [product_id],
+        'Product_Weight': [product_weight],
+        'Product_Sugar_Content': [product_sugar_content],
+        'Product_Allocated_Area': [product_allocated_area],
+        'Product_Type': [product_type],
+        'Product_MRP': [product_mrp],
+        'Store_Id': [store_id],
+        'Store_Establishment_Year': [store_establishment_year],
+        'Store_Size': [store_size],
+        'Store_Location_City_Type': [store_location_city_type],
+        'Store_Type': [store_type]
+    })
+
+
+    st.subheader('Input Data')
+    st.write(input_data)
+
+    # Make prediction
+    if st.sidebar.button('Predict Sales'):
+        try:
+            prediction = model.predict(input_data)
+            st.subheader('Predicted Product Store Sales Total')
+            st.success(f'Predicted Sales: ${prediction[0]:,.2f}')
+        except Exception as e:
+            st.error(f"An error occurred during prediction: {e}")
+
+else:
+    st.warning("Model not loaded. Please ensure the model file exists and the pipeline is correctly defined.")

requirements.txt

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+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+matplotlib==3.10.0
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+requests==2.32.3
+huggingface_hub==0.30.1
+streamlit==1.37.0