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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:rental_price_predictor_api"]

app.py

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+
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+
+# Initialize the Flask application
+sales_forecast_api = Flask("SuperKart Sales Forecast API")
+
+# Load the trained machine learning model
+model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@sales_forecast_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Forecast API!"
+
+# Define an endpoint for single prediction (POST request)
+@sales_forecast_api.post('/v1/sales')
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/sales' endpoint.
+    It expects a JSON payload with product and store attributes and returns
+    the predicted product-store sales revenue.
+    """
+    # Get the JSON data
+    data = request.get_json()
+
+    # Extract features based on the data dictionary
+    sample = {
+        '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_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']
+    }
+
+    # Convert to DataFrame
+    input_df = pd.DataFrame([sample])
+
+    # Predict sales
+    predicted_sales = model.predict(input_df)[0]
+    predicted_sales = round(float(predicted_sales), 2)
+
+    return jsonify({'Predicted Product-Store Sales Total': predicted_sales})
+
+# Define an endpoint for batch prediction (POST request)
+@sales_forecast_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    """
+    Handles POST requests to '/v1/salesbatch'.
+    Accepts a CSV file and returns predicted sales totals for each product-store.
+    """
+    # Read the uploaded file
+    file = request.files['file']
+    input_df = pd.read_csv(file)
+
+    # Predict sales for the batch
+    predictions = model.predict(input_df).tolist()
+    predictions = [round(float(pred), 2) for pred in predictions]
+
+    # Use Product_Id and Store_Id as combined key if available
+    if 'Product_Id' in input_df.columns and 'Store_Id' in input_df.columns:
+        keys = input_df['Product_Id'] + "_" + input_df['Store_Id']
+    else:
+        keys = list(range(len(predictions)))
+
+    results = dict(zip(keys, predictions))
+
+    return jsonify(results)
+
+# Run the application
+if __name__ == '__main__':
+    sales_forecast_api.run(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_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8fb7aa467fdbb38a4c2e1f19e93b6f7163c9f3c16dff8ad370aa013e12b38db
+size 107883