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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: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"]

app.py

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+
+import numpy as np
+import pandas as pd
+import joblib
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+
+# Initialize Flask app
+superkart_api = Flask("superkart_sales_api")
+CORS(superkart_api)
+
+# Load the trained model pipeline (preprocessing + model)
+model = joblib.load("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():
+    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})
+
+    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)

requirements.txt

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+# 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

superkart_sales_forecast_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a2c321b5c4f98cc85c8d11df8310e8bee3f18ffba2a4f2026a55d079885d6764
+size 63805219