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app.py

@@ -3,47 +3,91 @@ 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 Flask app
+
+# Initialize the Flask application
 sales_revenue_predictor_api = Flask("Sales Revenue Predictor")
 
-# Load trained model
+# Load the trained machine learning model
 model = joblib.load("sales_revenue_prediction_model_v1_0.joblib")
 
+# Define a route for the home page (GET request)
 @sales_revenue_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 Sales Revenue Prediction API!"
 
-# -------- Single prediction ----------
+# Define an endpoint for single property prediction (POST request)
 @sales_revenue_predictor_api.post('/v1/revenue')
-def predict_revenue():
-    try:
-        product_store_data = request.get_json()
-
-        # Extract required features
-        sample = {
-            'Product_Id': product_store_data['Product_Id'],
-            'Product_Weight': product_store_data['Product_Weight'],
-            'Product_Sugar_Content': product_store_data['Product_Sugar_Content'],
-            'Product_Allocated_Area': product_store_data['Product_Allocated_Area'],
-            'Product_Type': product_store_data['Product_Type'],
-            'Product_MRP': product_store_data['Product_MRP'],
-            'Store_Id': product_store_data['Store_Id'],
-            'Store_Establishment_Year': product_store_data['Store_Establishment_Year'],
-            'Store_Size': product_store_data['Store_Size'],
-            'Store_Location_City_Type': product_store_data['Store_Location_City_Type'],
-            'Store_Type': product_store_data['Store_Type']
-        }
-
-        input_data = pd.DataFrame([sample])
-
-        # Predict
-        predicted_log = model.predict(input_data)[0]
-        predicted_revenue = round(float(np.exp(predicted_log)), 2)
-
-        return jsonify({'Predicted Revenue (in dollars)': predicted_revenue})
-
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
+def predict_rental_price():
+    """
+    This function handles POST requests to the '/v1/revenue' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted sales revenue as a JSON response.
+    """
+    # Get the JSON data from the request body
+    product_store_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Id': product_store_data['Product_Id'],
+        'Product_Weight': product_store_data['Product_Weight'],
+        'Product_Sugar_Content': product_store_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': product_store_data['Product_Allocated_Area'],
+        'Product_Type': product_store_data['Product_Type'],
+        'Product_MRP': product_store_data['Product_MRP'],
+        'Store_Id': product_store_data['Store_Id'],
+        'Store_Establishment_Year': product_store_data['Store_Establishment_Year'],
+        'Store_Size': product_store_data['Store_Size'],
+        'Store_Location_City_Type': product_store_data['Store_Location_City_Type'],
+        'Store_Type': product_store_data['Store_Type']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get product_store_sales_total )
+    predicted_product_Store_Sales_Total = model.predict(input_data)[0]
+
+    # Calculate actual revenue
+    predicted_revenue = np.exp(predicted_product_Store_Sales_Total)
+
+    # Convert predicted_revenue to Python float
+    predicted_revenue = round(float(predicted_revenue), 2)
+   
+    # Return the actual revenue
+    return jsonify({'Predicted Revenue (in dollars)': predicted_revenue})
+
+
+# Define an endpoint for batch prediction (POST request)
+@sales_revenue_predictor_api.post('/v1/revenuebatch')
+def predict_revenue_batch():
+    """
+    This function handles POST requests to the '/v1/revenuebatch' endpoint.
+    It expects a CSV file containing property details for multiple properties
+    and returns the predicted revenue as a dictionary in the JSON response.
+    """
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the CSV file into a Pandas DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions for all properties in the DataFrame (get predicted_product_Store_Sales_Total)
+    predicted_product_Store_Sales_Total = model.predict(input_data).tolist()
+
+    # Calculate actual revenue
+    predicted_revenue = [round(float(np.exp(log_price)), 2) for log_price in predicted_product_Store_Sales_Total]
+
+    # Create a dictionary of predictions with product IDs as keys
+    ids = str(input_data['Product_Id']+input_data['Store_Id']).tolist()  
+    output_dict = dict(zip(ids, predicted_revenue))  # Use actual revenue
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict
 
+# Run the Flask application in debug mode if this script is executed directly
 if __name__ == '__main__':
-    sales_revenue_predictor_api.run(host="0.0.0.0", port=8000, debug=True)
+    sales_revenue_predictor_api.run(debug=True)