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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 joblib
+import numpy as np
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    return joblib.load("deployment_files/super_kart_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("Super Kart Forecasting App")
+st.write("This tool predicts the Sales Strategies")
+
+st.subheader("Enter the listing details:")
+
+# Collect user input
+product_weight = st.number_input("Weight", min_value=1, step=1, value=2)
+Product_Sugar_Content = st.selectbox("Sugar", ["Low", "Regular", "No"])
+Product_Allocated_Area = st.number_input("Area", min_value=1, step=1, value=2)
+Product_Type = st.selectbox("Product type", ["meat", "snack foods", "hard drinks", "dairy", "canned", "soft drinks", "health","hygiene", "baking goods", "bread", "breakfast", "frozen foods", "fruits","vegetables", "household", "seafood", "starchy foods", "others"])
+Product_MRP = st.number_input("MRP", min_value=1, step=1, value=2)
+Store_Establishment_Year = st.number_input("year", min_value=1950, step=1, value=2)
+Store_Size = st.selectbox("Store Size", ["High", "Medium", "Low"])
+Store_Location_City_Type =  st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type =  st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2","Food Mart"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    '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_Establishment_Year': Store_Establishment_Year,
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type
+}])
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    st.write(f"The predicted value ${np.exp(prediction)[0]:.2f}.")

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2

super_kart_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e8bb5aa05c3370560197c9b842bd5a3d938f3abc0f5f5b122a18ec355d555fee
+size 207896