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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
+
+# Set the title of the Streamlit app
+st.title("SuperKart Revenue Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Input fields for product and store data
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
+
+Product_Sugar_Content = st.selectbox(
+    "Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"]
+)
+
+Product_Allocated_Area = st.number_input(
+    "Product Allocated Area", min_value=0.00, max_value=0.30, value=0.15, step=0.05
+)
+
+Product_MRP = st.slider(
+    "Product MRP", min_value=0, max_value=250, value=100, step=50
+)
+
+Store_Size = st.selectbox(
+    "Store Size", ["High", "Medium", "Small"]
+)
+
+Store_Location_City_Type = st.selectbox(
+    "Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"]
+)
+
+Store_Type = st.selectbox(
+    "Store Type", ["Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"]
+)
+
+Product_Id_char = st.text_input(
+    "Product ID", value="FD306"
+)
+
+Store_Age_Years = st.slider(
+    "Store Age (in years)", min_value=0, max_value=30, value=10, step=1
+)
+
+Product_Type_Category = st.selectbox(
+    "Product Type Category",
+    [
+        "Baking Goods", "Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods",
+        "Fruits and Vegetables", "Hard Drinks", "Health and Hygiene", "Household",
+        "Meat", "Others", "Seafood", "Snack Foods", "Soft Drinks", "Starchy Foods"
+    ]
+)
+
+product_data = {
+    "Product_Weight": Product_Weight,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_MRP": Product_MRP,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Product_Id_char": Product_Id_char,
+    "Store_Age_Years": Store_Age_Years,
+    "Product_Type_Category": Product_Type_Category
+}
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://randhigoswami-my-work-space.hf.space/v1/predict", json=product_data)
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result["Sales"]
+        st.write(f"Predicted Product Store Sales Total: ₹{predicted_sales:.2f}")
+    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