Create app.py

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_predictor_api = Flask("Superkart Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("superkart_pred_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@sales_predictor_api.get('/')
+def home():
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    return "Welcome to the Superkart Sales Prediction API!"
+
+# Define an endpoint for single property prediction (POST request)
+@sales_predictor_api.post('/v1/sales')
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/sales' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted sales as a JSON response.
+    """
+    # Get the JSON data from the request body
+    property_data = request.get_json()
+
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': property_data['Product_Weight'],
+        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
+        'Product_Type': property_data['Product_Type'],
+        'Product_MRP': property_data['Product_MRP'],
+        'Store_Id': property_data['Store_Id'],
+        'Store_Establishment_Year': property_data['Store_Establishment_Year'],
+        'Store_Size': property_data['Store_Size'],
+        'Store_Type': property_data['Store_Type']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_sales = model.predict(input_data)[0]
+
+    # Return the actual price
+    return jsonify({'Predicted Sales (in dollars)': predicted_sales})
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    sales_predictor_api.run(debug=True)