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Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+# 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
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# 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
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# 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"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,38 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart Retail Store Sales Predictor App")
+
+# Section for online prediction
+st.subheader("This tool predicts SuperKart Retail Store Sales based on the product and store details. Enter the required information below.")
+
+st.subheader("Enter the Product and Store details:")
+
+# Collect user input
+Product_Type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy","Household","Baking Goods","Canned","Health and Hygiene","Meat","Soft Drinks","Breads","Hard Drinks","Others","Starchy Foods","Breakfast","Seafood"])
+Product_Sugar_Content = st.selectbox("Product Sugar Content",["Low Sugar","Regular","No Sugar"])
+Store_Type = st.selectbox("Store Type",["Supermarket Type2","Supermarket Type1","Departmental Store","Food Mart"])
+Product_Weight = st.number_input("Product Weight (Lbs):",min_value = 5.00, step = 0.01, value = 25.00)
+Product_MRP = st.number_input("Product MRP ($):",min_value = 40.00, step = 0.02, value = 240.00)
+Product_Allocated_Area = st.number_input("Product Allocated Area (%):",min_value = 0.001, step = 0.001, value = 0.3000)
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    "Product_Weight" : Product_Weight,
+    "Product_Allocated_Area" : Product_Allocated_Area,
+    "Product_MRP" : Product_MRP,
+    "Product_Sugar_Content" : Product_Sugar_Content,
+    "Product_Type" : Product_Type,
+    "Store_Type" : Store_Type
+    }])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://<username>-<repo_id>.hf.space/v1/superkart", json=input_data)  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Store Sales (in dollars)']
+        st.success(f"Predicted Store Sales (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.32.4
+streamlit==1.43.2