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Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working 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
+
+# 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:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:SuperKart_predictor_api"]

SuperKart_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fe6646c64c50003d7be9e910550c26c0a3da5c1007f1b3c5e453864646bbf19c
+size 259782

app.py

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+import numpy as np
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+from datetime import datetime
+
+# Initialize the Flask application
+sales_predictor_api = Flask("Retail Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("retail_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@sales_predictor_api.get('/')
+def home():
+    """
+    Welcome endpoint for the Retail Sales Prediction API
+    """
+    return "Welcome to the Retail Product Sales Prediction API!"
+
+# Define an endpoint for single product prediction (POST request)
+@sales_predictor_api.post('/v1/sales')
+def predict_sales():
+    """
+    Endpoint for predicting sales for a single product-store combination
+    Expects JSON payload with product and store details
+    """
+    try:
+        # Get the JSON data from the request body
+        data = request.get_json()
+        
+        # Calculate derived features
+        current_year = datetime.now().year
+        store_age = current_year - data['store_establishment_year']
+        product_density = data['product_weight'] / (data['product_allocated_area'] + 1e-6)
+        price_per_weight = data['product_mrp'] / (data['product_weight'] + 1e-6)
+        product_size = 'Small' if data['product_weight'] <= 10 else ('Medium' if data['product_weight'] <= 15 else 'Large')
+        store_tier_size = f"{data['store_location_city_type']}_{data['store_size']}"
+        
+        # Prepare input data
+        input_data = pd.DataFrame([{
+            'Product_Weight': data['product_weight'],
+            'Product_Sugar_Content': data['product_sugar_content'],
+            'Product_Allocated_Area': data['product_allocated_area'],
+            'Product_Type': data['product_type'],
+            'Product_MRP': data['product_mrp'],
+            'Store_Id': data['store_id'],
+            'Store_Establishment_Year': data['store_establishment_year'],
+            'Store_Size': data['store_size'],
+            'Store_Location_City_Type': data['store_location_city_type'],
+            'Store_Type': data['store_type'],
+            'Store_Age': store_age,
+            'Product_Density': product_density,
+            'Price_Per_Unit_Weight': price_per_weight,
+            'Product_Size_Category': product_size,
+            'Store_Tier_Size': store_tier_size
+        }])
+        
+        # Make prediction
+        predicted_sales = model.predict(input_data)[0]
+        
+        # Return the predicted sales
+        return jsonify({
+            'predicted_sales': round(float(predicted_sales), 2),
+            'store_age': store_age,
+            'product_density': round(product_density, 2),
+            'price_per_weight': round(price_per_weight, 2)
+        })
+        
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# Define an endpoint for batch prediction (POST request)
+@sales_predictor_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    """
+    Endpoint for batch predictions from CSV file
+    Expects CSV file with multiple product-store combinations
+    """
+    try:
+        # Check if file was uploaded
+        if 'file' not in request.files:
+            return jsonify({'error': 'No file uploaded'}), 400
+            
+        file = request.files['file']
+        
+        # Read CSV file
+        input_data = pd.read_csv(file)
+        
+        # Calculate derived features
+        current_year = datetime.now().year
+        input_data['Store_Age'] = current_year - input_data['Store_Establishment_Year']
+        input_data['Product_Density'] = input_data['Product_Weight'] / (input_data['Product_Allocated_Area'] + 1e-6)
+        input_data['Price_Per_Unit_Weight'] = input_data['Product_MRP'] / (input_data['Product_Weight'] + 1e-6)
+        input_data['Product_Size_Category'] = input_data['Product_Weight'].apply(
+            lambda x: 'Small' if x <= 10 else ('Medium' if x <= 15 else 'Large'))
+        input_data['Store_Tier_Size'] = input_data['Store_Location_City_Type'] + '_' + input_data['Store_Size']
+        
+        # Make predictions
+        predictions = model.predict(input_data)
+        
+        # Prepare results
+        results = []
+        for i, row in input_data.iterrows():
+            results.append({
+                'product_id': row['Product_Id'],
+                'store_id': row['Store_Id'],
+                'predicted_sales': round(float(predictions[i]), 2),
+                'product_type': row['Product_Type'],
+                'store_type': row['Store_Type']
+            })
+            
+        return jsonify({'predictions': results})
+        
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# Run the Flask application
+if __name__ == '__main__':
+    sales_predictor_api.run(host='0.0.0.0', port=5000, debug=True)

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]
+streamlit==1.43.2