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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 +1,79 @@
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import requests
+
+# Streamlit UI for Sales Prediction
+st.title("Superkart Total Product Sales Prediction App")
+st.write("This tool predicts the sales of SuperKart store's product based on the property details.")
+
+st.subheader("Enter the details:")
+
+# Collect user input for each feature
+product_id = st.selectbox("Product ID", ["FD", "NC", "DR"])
+product_weight = st.number_input("Product Weight", min_value=0.0, value=10.0, step=0.1)
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, value=0.05, step=0.01)
+product_mrp = st.number_input("Product MRP", min_value=0.0, value=100.0, step=0.1)
+store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1985, max_value=2025, value=2000, step=1)
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+grouped_product_type = st.selectbox("Grouped Product Type", ["Food Items", "Household and Hygiene", "Beverages", "Miscellaneous"])
+
+
+# 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_Id_FD': 1 if product_id == 'FD' else 0,
+    'Product_Id_NC': 1 if product_id == 'NC' else 0,
+    'Product_Sugar_Content_No Sugar': 1 if product_sugar_content == 'No Sugar' else 0,
+    'Product_Sugar_Content_Regular': 1 if product_sugar_content == 'Regular' else 0,
+    'Store_Id_OUT002': 1 if store_id == 'OUT002' else 0,
+    'Store_Id_OUT003': 1 if store_id == 'OUT003' else 0,
+    'Store_Id_OUT004': 1 if store_id == 'OUT004' else 0,
+    'Store_Size_Medium': 1 if store_size == 'Medium' else 0,
+    'Store_Size_Small': 1 if store_size == 'Small' else 0,
+    'Store_Location_City_Type_Tier 2': 1 if store_location_city_type == 'Tier 2' else 0,
+    'Store_Location_City_Type_Tier 3': 1 if store_location_city_type == 'Tier 3' else 0,
+    'Store_Type_Food Mart': 1 if store_type == 'Food Mart' else 0,
+    'Store_Type_Supermarket Type1': 1 if store_type == 'Supermarket Type1' else 0,
+    'Store_Type_Supermarket Type2': 1 if store_type == 'Supermarket Type2' else 0,
+    'Grouped_Product_Type_Food Items': 1 if grouped_product_type == 'Food Items' else 0,
+    'Grouped_Product_Type_Household and Hygiene': 1 if grouped_product_type == 'Household and Hygiene' else 0,
+    'Grouped_Product_Type_Miscellaneous': 1 if grouped_product_type == 'Miscellaneous' else 0,
+    'Store_Age': 2025 - store_establishment_year # Calculate Store_Age
+}])
+
+# Ensure all columns used during training are present in the input_data DataFrame
+# Add dummy columns for any missing one-hot encoded features
+train_cols = ['Product_Weight', 'Product_Allocated_Area', 'Product_MRP', 'Store_Age',
+              'Product_Id_FD', 'Product_Id_NC', 'Product_Sugar_Content_No Sugar',
+              'Product_Sugar_Content_Regular', 'Store_Id_OUT002', 'Store_Id_OUT003',
+              'Store_Id_OUT004', 'Store_Size_Medium', 'Store_Size_Small',
+              'Store_Location_City_Type_Tier 2', 'Store_Location_City_Type_Tier 3',
+              'Store_Type_Food Mart', 'Store_Type_Supermarket Type1',
+              'Store_Type_Supermarket Type2', 'Grouped_Product_Type_Food Items',
+              'Grouped_Product_Type_Household and Hygiene',
+              'Grouped_Product_Type_Miscellaneous']
+
+for col in train_cols:
+    if col not in input_data.columns:
+        input_data[col] = 0
+
+# Reorder columns to match the training data
+input_data = input_data[train_cols]
+
+
+# Predict button
+if st.button("Predict"):
+    response = requests.post("https://Abhilashu/superKart_Total_Sales_Prediction_Backend.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted total sales (in dollars)']
+        st.success(f"The predicted total sales for this product in this store is: {prediction[0]:.2f}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+requests==2.28.1
+streamlit==1.43.2