Upload frontend Streamlit files

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

requirements.txt

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+streamlit
+pandas
+numpy
+xgboost
+scikit-learn

src/streamlit_app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+
+# load assets
+with open("model/xgb_superkart_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+X_test = pd.read_csv("data/X_test.csv")
+
+# predict
+preds = model.predict(X_test)
+
+# output
+st.title("SuperKart Sales Forecast")
+st.subheader("X_test Predictions")
+st.dataframe(pd.DataFrame({"Predicted Sales": preds}))