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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_sales_forecast_api"]

app.py

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+# Import necessary libraries
+import numpy as np
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize the Flask application
+superkart_sales_forecast_api = Flask("SuperKart Sales Forecast API")
+
+# Load the trained machine learning model
+model = joblib.load("superkart_sales_prediction.joblib", mmap_mode=None)
+
+# ---------------- Home Route ----------------
+@superkart_sales_forecast_api.get('/')
+def home():
+    """
+    Handles GET requests to the root URL ('/').
+    Returns a welcome message.
+    """
+    return "Welcome to the SuperKart Sales Forecast API!"
+
+# ---------------- Online Forecast Route ----------------
+@superkart_sales_forecast_api.post('/v1/sales')
+def predict_sales_forecast():
+    """
+    Handles POST requests to '/v1/sales'.
+    Expects a JSON payload of product-store details.
+    Returns the predicted sales total.
+    """
+    try:
+        forecast_data = request.get_json()
+
+        sample = {
+            'Product_Weight': float(forecast_data['Product_Weight']),
+            'Product_MRP': float(forecast_data['Product_MRP']),
+            'Product_Sugar_Content': forecast_data['Product_Sugar_Content'],
+            'Product_Allocated_Area': float(forecast_data['Product_Allocated_Area']),
+            'Product_Type': forecast_data['Product_Type'],
+            'Store_Id': forecast_data['Store_Id'],
+            'Store_Establishment_Year': int(forecast_data['Store_Establishment_Year']),
+            'Store_Size': forecast_data['Store_Size'],
+            'Store_Location_City_Type': forecast_data['Store_Location_City_Type'],
+            'Store_Type': forecast_data['Store_Type']
+        }
+
+        input_df = pd.DataFrame([sample])
+        prediction = model.predict(input_df)
+        predicted_sales = round(float(prediction[0]), 2)
+
+        return jsonify({'predicted_product_store_sales_total': predicted_sales})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+# ---------------- Batch Forecast Route ----------------
+@superkart_sales_forecast_api.post('/v1/salesbatch')
+def predict_sales_forecast_batch():
+    """
+    Handles POST requests to '/v1/salesbatch'.
+    Expects a CSV file with product-store rows.
+    Returns predicted sales totals per row.
+    """
+    try:
+        file = request.files.get('file')
+        if file is None:
+            return jsonify({"error": "No file uploaded"}), 400
+
+        input_df = pd.read_csv(file)
+
+        # Predict using the trained model
+        predictions = model.predict(input_df)
+        input_df["predicted_product_store_sales_total"] = [round(float(x), 2) for x in predictions]
+
+        return jsonify(input_df.to_dict(orient="records"))
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+# ---------------- Run the Flask App ----------------
+if __name__ == '__main__':
+    superkart_sales_forecast_api.run(host='0.0.0.0', port=7860, 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

superkart_sales_prediction.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:45dc1553385a864a27012d09dca9f87661c514c2f4b6c322a85e3a89527da526
+size 208249