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Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.12-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
+import json
+
+# Set the title of the Streamlit app
+st.title("SuperKart Sales Prediction")
+
+# Section for online prediction
+st.subheader("Predict Single Product Sales")
+
+# Collect user input for product and store features
+product_id = st.text_input("Product ID", value="FD6114")
+product_weight = st.number_input("Product Weight", min_value=0.0, value=12.66, step=0.1)
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, value=0.027, step=0.001)
+product_type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Household', 'Meat', 'Soft Drinks', 'Breads', 'Hard Drinks', 'Others', 'Starchy Foods', 'Breakfast', 'Seafood', 'Fruits and Vegetables', 'Snack Foods'])
+product_mrp = st.number_input("Product MRP", min_value=0.0, value=117.08, step=0.01)
+store_id = st.selectbox("Store ID", ["OUT004", "OUT003", "OUT001", "OUT002"])
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2009, step=1)
+store_size = st.selectbox("Store Size", ["Medium", "High", "Small"])
+store_location_city_type = st.selectbox("Store Location City Type", ["Tier 2", "Tier 1", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Supermarket Type2", "Departmental Store", "Supermarket Type1", "Food Mart"])
+
+
+# Convert user input into a dictionary
+input_data = {
+    "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
+}
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    # Replace with your actual Hugging Face Space backend URL
+    backend_url = "https://retheesh-superkartsalesprediction.hf.space/predict_sales" # Replace with your space URL and endpoint
+
+    try:
+        response = requests.post(backend_url, json=input_data)
+
+        if response.status_code == 200:
+            prediction = response.json().get('predicted_sales')
+            if prediction is not None:
+                st.success(f"Predicted Product Store Sales Total: {prediction:.2f}")
+            else:
+                st.error("Prediction not found in the response.")
+                st.json(response.json()) # Display the full response for debugging
+        else:
+            st.error(f"Error predicting sales. Status code: {response.status_code}")
+            st.write("Response body:", response.text) # Display response text for debugging
+            st.json(response.json()) # Display response json for debugging
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error connecting to the backend API: {e}")

requirements.txt

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+pandas==2.3.1
+requests==2.32.3
+streamlit==1.43.2