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Dockerfile

@@ -1,16 +1,16 @@
+# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
-# Set the working directory inside the container
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-# Copy all files from the current directory to the container's working directory
+# Copy all files from the current directory on the host to the container's /app directory
 COPY . .
 
-# Install dependencies from the requirements file without using cache to reduce image size
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
 
-# Define the command to start the application using Gunicorn with 4 worker processes
-# - `-w 4`: Uses 4 worker processes for handling requests
-# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
-# - `app:superkart_api`: Runs the Flask app (Flask app instance is named `superkart_api` inside app.py)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_api"]
+# Define the command to run the Streamlit app on port 7860 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--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

@@ -1,71 +1,57 @@
 
+# Streamlit Web App for SuperKart Sales Forecasting
+import streamlit as st
+import requests
 import numpy as np
-import pandas as pd
-import joblib
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import os
 
-# Initialize Flask app
-superkart_api = Flask("superkart_sales_api")
-CORS(superkart_api)
-
-# Load the trained model pipeline (preprocessing + model)
-model = joblib.load("/content/drive/My Drive/Python/Superkart/deployment_files/superkart_sales_forecast_model_v1_0.joblib")
-
-# Health check route
-@superkart_api.get('/')
-def home():
-    return "✅ Welcome to the SuperKart Sales Prediction API"
-
-# Prediction route
-@superkart_api.post('/v1/predict')
-def predict_sales():
+# Add Logo
+st.image("https://i.postimg.cc/2yM4LgJM/Superkart-notebook-cover-image.png", width=400)
+
+# App Title
+st.title("🛒 SuperKart Sales Forecasting App")
+
+# Instructions
+st.markdown("🔍 Enter product and store attributes to forecast **monthly product sales revenue**.\n\n_All sales are reported in ($) USD._")
+
+# User Inputs
+Product_Weight = st.number_input("Product Weight (oz)", 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 (linear in.)", min_value=0.0, value=100.0)
+Product_MRP = st.number_input("Maximum Retail Price (USD)", min_value=0.0, value=150.0)
+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", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+Store_Age_Years = st.slider("Store Age (years)", min_value=0, max_value=30, value=10)
+Product_Type_Category = st.selectbox("Product Type Category", ["Perishables", "Non Perishables"])
+
+# Apply log1p transform (must match backend model training)
+Product_Allocated_Area_Log = np.log1p(Product_Allocated_Area)
+
+# Prepare JSON payload for the backend
+product_data = {
+    "Product_Weight": str(Product_Weight),
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Allocated_Area": str(Product_Allocated_Area),
+    "Product_MRP": str(Product_MRP),
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Store_Age_Years": str(Store_Age_Years),
+    "Product_Type_Category": Product_Type_Category
+}
+
+# Trigger Prediction
+if st.button("Predict", type='primary'):
     try:
-        # Parse JSON payload
-        data = request.get_json()
-        print("Raw incoming data:", data)
-
-        # Validate expected fields
-        required_fields = [
-            'Product_Weight',
-            'Product_Sugar_Content',
-            'Product_Allocated_Area',
-            'Product_MRP',
-            'Store_Size',
-            'Store_Location_City_Type',
-            'Store_Type',
-            'Store_Age_Years',
-            'Product_Type_Category'
-        ]
-        missing_fields = [f for f in required_fields if f not in data]
-        if missing_fields:
-            return jsonify({'error': f"Missing fields: {missing_fields}"}), 400
-
-        # Convert and transform input
-        sample = {
-            'Product_Weight': float(data['Product_Weight']),
-            'Product_Sugar_Content': data['Product_Sugar_Content'],
-            'Product_Allocated_Area_Log': np.log1p(float(data['Product_Allocated_Area'])),  # transform here
-            'Product_MRP': float(data['Product_MRP']),
-            'Store_Size': data['Store_Size'],
-            'Store_Location_City_Type': data['Store_Location_City_Type'],
-            'Store_Type': data['Store_Type'],
-            'Store_Age_Years': int(data['Store_Age_Years']),
-            'Product_Type_Category': data['Product_Type_Category']
-        }
-
-        input_df = pd.DataFrame([sample])
-        print("Transformed input for model:\n", input_df)
-
-        # Make prediction
-        prediction = model.predict(input_df).tolist()[0]
-        return jsonify({'Predicted_Sales': prediction})
-
+        response = requests.post(
+            "https://thomash007-superkart-sales-forecast-backend2.hf.space/v1/predict",
+            json=product_data
+        )
+        if response.status_code == 200:
+            result = response.json()
+            predicted_sales = result["Predicted_Sales"]
+            st.success(f"📈 Predicted Monthly Sales: **${predicted_sales:,.2f} USD**")
+        else:
+            st.error("❌ API Error: Please verify input values or try again later.")
     except Exception as e:
-        print("❌ Error during prediction:", str(e))
-        return jsonify({'error': f"Prediction failed: {str(e)}"}), 500
-
-# Run the app (for local testing only)
-if __name__ == '__main__':
-    superkart_api.run(debug=True)
+        st.error(f"⚠️ Connection error: {e}")

requirements.txt

@@ -1,16 +1,2 @@
-# Core libraries
-pandas==2.2.2
-numpy==2.0.2
-scikit-learn==1.6.1
-seaborn==0.13.2
-joblib==1.4.2
-xgboost==2.1.4
-
-# Flask web server
-flask==2.2.2
-flask-cors==3.0.10
-gunicorn==20.1.0
-Werkzeug==2.2.2
-
-# For API testing
 requests==2.32.3
+streamlit==1.45.0