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app.py

@@ -1,93 +1,66 @@
 # 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
+import joblib  
+import pandas as pd  
+import os 
+from flask import Flask, request, jsonify 
 
 # Initialize the Flask application
-rental_price_predictor_api = Flask("Airbnb Rental Price Predictor")
+sales_predictor_api = Flask("SuperKart Sales Predictor API")
 
 # Load the trained machine learning model
-model = joblib.load("rental_price_prediction_model_v1_0.joblib")
+# CRITICAL FIX: The file name is CORRECTED here to match the file you uploaded!
+MODEL_PATH = os.path.join(os.getcwd(), "best_xgb_pipeline.joblib")
 
-# Define a route for the home page (GET request)
-@rental_price_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!"
-
-# Define an endpoint for single property prediction (POST request)
-@rental_price_predictor_api.post('/v1/rental')
-def predict_rental_price():
-    """
-    This function handles POST requests to the '/v1/rental' endpoint.
-    It expects a JSON payload containing property details and returns
-    the predicted rental price 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 = {
-        'room_type': property_data['room_type'],
-        'accommodates': property_data['accommodates'],
-        'bathrooms': property_data['bathrooms'],
-        'cancellation_policy': property_data['cancellation_policy'],
-        'cleaning_fee': property_data['cleaning_fee'],
-        'instant_bookable': property_data['instant_bookable'],
-        'review_scores_rating': property_data['review_scores_rating'],
-        'bedrooms': property_data['bedrooms'],
-        'beds': property_data['beds']
-    }
-
-    # Convert the extracted data into a Pandas DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make prediction (get log_price)
-    predicted_log_price = model.predict(input_data)[0]
-
-    # Calculate actual price
-    predicted_price = np.exp(predicted_log_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.
-    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
+try:
+    model = joblib.load(MODEL_PATH)
+    print(f"Model loaded successfully from: {MODEL_PATH}")
+except Exception as e:
+    print(f"FATAL ERROR: Could not load model: {e}")
+    model = None 
 
-    # Return the actual price
-    return jsonify({'Predicted Price (in dollars)': predicted_price})
-
-
-# Define an endpoint for batch prediction (POST request)
-@rental_price_predictor_api.post('/v1/rentalbatch')
-def predict_rental_price_batch():
-    """
-    This function handles POST requests to the '/v1/rentalbatch' 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.
-    """
-    # 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 log_prices)
-    predicted_log_prices = model.predict(input_data).tolist()
-
-    # Calculate actual prices
-    predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
-
-    # 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
+@sales_predictor_api.get('/')
+def home():
+    """Returns a simple welcome message for the SuperKart API."""
+    return "Welcome to the SuperKart Sales Prediction API!"
+
+@sales_predictor_api.post('/v1/sales')
+def predict_single_sale():
+    """Handles POST requests for a single sales forecast."""
+    if model is None:
+        return jsonify({'error': 'Internal server error: Model failed to load at startup.'}), 500
+        
+    try:
+        property_data = request.get_json()
+        input_data = pd.DataFrame([property_data])
+        
+        # Predicts actual sales total directly
+        predicted_sales = model.predict(input_data)[0]
+        predicted_sales = round(float(predicted_sales), 2)
+
+        return jsonify({'Predicted Total Sales': predicted_sales})
+    except Exception as e:
+        return jsonify({'error': str(e), 'message': f'Prediction failed: {str(e)}'}), 400
+
+@sales_predictor_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    """Handles POST requests for batch sales forecasts via CSV upload."""
+    if model is None:
+        return jsonify({'error': 'Internal server error: Model failed to load at startup.'}), 500
+        
+    try:
+        file = request.files['file']
+        input_data = pd.read_csv(file)
+        predicted_sales = model.predict(input_data).tolist()
+        final_predictions = [round(float(sale), 2) for sale in predicted_sales]
+
+        property_ids = input_data['id'].tolist()
+        output_dict = dict(zip(property_ids, final_predictions))
+        
+        return jsonify(output_dict)
+    except Exception as e:
+        return jsonify({'error': str(e), 'message': f'Batch prediction failed: {str(e)}'}), 400
 
-    # 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)
+    sales_predictor_api.run(debug=True, host='0.0.0.0', port=7860)