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@@ -13,4 +13,4 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 # - `-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:super_kart_predictor_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_sales_predictor_api"]

app.py

@@ -5,23 +5,23 @@ import pandas as pd  # For data manipulation
 from flask import Flask, request, jsonify  # For creating the Flask API
 
 # Initialize the Flask application
-rental_price_predictor_api = Flask("Super kart Predictor")
+superkart_sales_predictor_api = Flask("Super kart Sales Predictor")
 
 # Load the trained machine learning model
 model = joblib.load("super_kart_prediction_model_v1_0.joblib")
 
 # Define a route for the home page (GET request)
-@rental_price_predictor_api.get('/')
+@superkart_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 Airbnb Rental Price Prediction API!"
+    return "Welcome to the Super Kart Sales Prediction API!"
 
 # Define an endpoint for single property prediction (POST request)
-@rental_price_predictor_api.post('/v1/rental')
-def predict_rental_price():
+@superkart_sales_predictor_api.post('/v1/rental')
+def predict_superkart_sales():
     """
     This function handles POST requests to the '/v1/rental' endpoint.
     It expects a JSON payload containing property details and returns
@@ -32,8 +32,8 @@ def predict_rental_price():
 
     # Extract relevant features from the JSON data
     sample = {
+        'Product_Id': property_data['Product_Id'],
         'Product_Weight': property_data['Product_Weight'],
-        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
         'Product_MRP': property_data['Product_MRP'],
         'Product_Sugar_Content': property_data['Product_Sugar_Content'],
         'Product_Type': property_data['Product_Type'],
@@ -47,25 +47,25 @@ def predict_rental_price():
     input_data = pd.DataFrame([sample])
 
     # Make prediction (get log_price)
-    predicted_log_price = model.predict(input_data)[0]
+    predicted_superkart_sales_price = model.predict(input_data)[0]
 
     # Calculate actual price
-    predicted_price = np.exp(predicted_log_price)
+    predicted_price = np.exp(predicted_superkart_sales_price)
 
     # Convert predicted_price to Python float
     predicted_price = round(float(predicted_price), 2)
-    # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
+    # The conversion above is needed as we convert the model prediction (sale revenue) to actual price using np.exp, which returns predictions as NumPy float32 values.
     # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
 
     # Return the actual price
-    return jsonify({'Predicted Price (in dollars)': predicted_price})
+    return jsonify({'Predicted Sale Revenue (in dollars)': predicted_price})
 
 
 # Define an endpoint for batch prediction (POST request)
-@rental_price_predictor_api.post('/v1/rentalbatch')
+@superkart_sales_predictor_api.post('/v1/superKartRevenuebatch')
 def predict_rental_price_batch():
     """
-    This function handles POST requests to the '/v1/rentalbatch' endpoint.
+    This function handles POST requests to the '/v1/superKartRevenuebatch' endpoint.
     It expects a CSV file containing property details for multiple properties
     and returns the predicted rental prices as a dictionary in the JSON response.
     """
@@ -76,18 +76,18 @@ def predict_rental_price_batch():
     input_data = pd.read_csv(file)
 
     # Make predictions for all properties in the DataFrame (get log_prices)
-    predicted_log_prices = model.predict(input_data).tolist()
+    predicted_superkart_sales_revenue = model.predict(input_data).tolist()
 
     # Calculate actual prices
-    predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
+    predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_superkart_sales_revenue]
 
     # Create a dictionary of predictions with property IDs as keys
-    property_ids = input_data['id'].tolist()  # Assuming 'id' is the property ID column
-    output_dict = dict(zip(property_ids, predicted_prices))  # Use actual prices
+    product_ids = input_data['Product_Id'].tolist()  # Assuming 'id' is the property ID column
+    output_dict = dict(zip(product_ids, predicted_prices))  # Use actual prices
 
     # 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__':
-    rental_price_predictor_api.run(debug=True)
+    superkart_sales_predictor_api.run(debug=True)

requirements.txt

@@ -1,8 +1,12 @@
 pandas==2.2.2
 numpy==2.0.2
 scikit-learn==1.6.1
+matplotlib==3.10.0
+seaborn==0.13.2
 xgboost==2.1.4
 joblib==1.4.2
+requests==2.32.3
+huggingface_hub==0.30.1
 Werkzeug==2.2.2
 flask==2.2.2
 gunicorn==20.1.0