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app.py

@@ -1,92 +1,64 @@
-# 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
+# Initialize Flask app
 sales_revenue_predictor_api = Flask("Sales Revenue Predictor")
 
-# Load the trained machine learning model
+# Load trained 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!"
 
-# Define an endpoint for single property prediction (POST request)
+# -------- Single prediction ----------
 @sales_revenue_predictor_api.post('/v1/revenue')
-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_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)
+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_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
+
+
+# -------- Batch prediction ----------
 @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']
+    try:
+        file = request.files['file']
+        input_data = pd.read_csv(file)
 
-    # Read the CSV file into a Pandas DataFrame
-    input_data = pd.read_csv(file)
+        predicted_logs = model.predict(input_data).tolist()
+        predicted_revenues = [round(float(np.exp(log)), 2) for log in predicted_logs]
 
-    # Make predictions for all properties in the DataFrame (get predicted_product_Store_Sales_Total)
-    predicted_product_Store_Sales_Total = model.predict(input_data).tolist()
+        # Fix: create proper IDs
+        ids = (input_data['Product_Id'].astype(str) + "_" + input_data['Store_Id'].astype(str)).tolist()
+        output_dict = dict(zip(ids, predicted_revenues))
 
-    # Calculate actual revenue
-    predicted_revenue = [round(float(np.exp(log_price)), 2) for log_price in predicted_product_Store_Sales_Total]
+        return jsonify(output_dict)
 
-    # 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
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
 
-    # 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(debug=True)
+    sales_revenue_predictor_api.run(host="0.0.0.0", port=8000, debug=True)