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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 Sales Total Predictor")
+st.markdown("Forecasting product sales based on product characteristics and store type.")
+
+# --- API Endpoint Configuration (Replace placeholders with your actual Space URL) ---
+# NOTE: Replace <username>-<repo_id> with the actual ID of your Hugging Face Space.
+API_BASE_URL = "https://Tamilvelan-StoreSalesPredictionBackend.hf.space"
+ONLINE_PREDICTION_URL = f"{API_BASE_URL}/v1/sales"
+# -----------------------------------------------------------------------------------
+
+
+# Section for online prediction
+st.subheader("Predict Single Product-Store Sales")
+
+# --- Collect user input for SuperKart features ---
+
+# Numerical and Engineered Features
+col1, col2 = st.columns(2)
+with col1:
+    product_weight = st.number_input("Product Weight (kg)", min_value=1.0, max_value=25.0, value=12.02, step=0.01)
+    product_mrp = st.number_input("Product MRP ($)", min_value=10.0, max_value=300.0, value=141.6, step=0.01)
+    store_age = st.number_input("Store Age (Years)", min_value=1, max_value=50, value=25, help="Calculated as (Current Year - Store Establishment Year)")
+    
+with col2:
+    # Categorical Features (We assume the input still needs the RAW categorical feature for the full Pipeline to work)
+    product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+    product_type = st.selectbox("Product Type", ['Fruits and Vegetables', 'Snack Foods', 'Soft Drinks', 'Dairy', 'Baking Goods', 'Household', 'Others', 'Meat', 'Frozen Foods', 'Breakfast', 'Canned', 'Starchy Foods', 'Health and Hygiene', 'Fats and Oils', 'Seafood'])
+    store_type = st.selectbox("Store Type", ['Supermarket Type 1', 'Departmental Store', 'Supermarket Type 2', 'Food Mart'])
+
+# Ordinal Encoded Features (Using the user-friendly category, but mapping to the expected ENCODED value)
+# NOTE: The Backend API assumes it receives the ENCODED value (0, 1, 2, 3), not the raw string!
+store_size_map = {"Low": 0, "Medium": 1, "High": 2}
+store_city_map = {"Tier 3": 1, "Tier 2": 2, "Tier 1": 3}
+
+store_size_raw = st.selectbox("Store Size", list(store_size_map.keys()))
+store_location_city_type_raw = st.selectbox("Store Location City Type", list(store_city_map.keys()))
+
+# --- Special Engineered Input for Allocated Area Log ---
+# Since the model expects a LOG-TRANSFORMED value, we must either:
+# 1. Ask the user for the raw value and perform log1p here (cleaner)
+# 2. Ask the user for the log value (less intuitive)
+
+# We will ask for the raw value and transform it before sending to the API
+product_allocated_area_raw = st.number_input("Product Allocated Area (Raw Value)", min_value=0.0, value=0.05, step=0.01)
+product_allocated_area_log = float(np.log1p(product_allocated_area_raw)) # Calculate log1p(x)
+
+# --- Prepare Data Payload ---
+import numpy as np # Import numpy here for log1p calculation
+
+# Convert user input into a dictionary matching the API's expected JSON structure
+input_payload = {
+    'Product_Weight': product_weight,
+    'Product_MRP': product_mrp,
+    'Store_Age': store_age,
+    'Product_Allocated_Area_Log': product_allocated_area_log,
+    
+    # Send the encoded integer values for ordinal features as the API expects them
+    'Store_Size_Encoded': store_size_map[store_size_raw],
+    'Store_Location_City_Type_Encoded': store_city_map[store_location_city_type_raw],
+    
+    # Send the raw string values for nominal features (assuming the model pipeline handles OHE)
+    '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 Sales Total"):
+    try:
+        # Send data to Flask API
+        response = requests.post(ONLINE_PREDICTION_URL, json=input_payload) 
+        
+        if response.status_code == 200:
+            prediction = response.json().get('Predicted Sales Total (in dollars)')
+            if prediction is not None:
+                st.success(f"Predicted Product Sales Total: **${prediction:,.2f}**")
+            else:
+                st.error("Prediction key not found in API response.")
+        else:
+            st.error(f"Error making prediction. Status code: {response.status_code}. Response: {response.text}")
+    except requests.exceptions.RequestException as e:
+        st.error(f"An error occurred while connecting to the API: {e}")
+
+
+st.markdown("---")

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2