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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 manipualation
+from flask import Flask, jsonify, request # For creating Flask API
+
+# Intialize the Flask application
+# Correcting the inconsistent variable name to SuperKart_predictions_api
+SuperKart_predictions_api = Flask("SuperKart K model")
+
+# Load the serialized model
+# Ensure the model file path is correct relative to where the app runs in the container
+try:
+    model = joblib.load("best_superkart_sales_model.joblib")
+    print("Model loaded successfully.")
+except FileNotFoundError:
+    print("Error: Model file 'best_superkart_sales_model.joblib' not found.")
+    model = None # Set model to None if loading fails
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
+
+
+# Define a route for home page(GET request)
+@SuperKart_predictions_api.route('/') # Use the correct variable name here
+def home():
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    return "Welcome to SuperKart!"
+
+# Define an endpoint
+@SuperKart_predictions_api.route('/v1/SuperKart', methods=['POST']) # Use the correct variable name and specify POST method
+def predict():
+    """
+    This function handles POST requests to the '/v1/SuperKart' endpoint.
+    It expects a JSON payload containing the data for prediction,
+    uses the loaded model to make predictions, and returns the predictions.
+    """
+    # Check if the model was loaded successfully
+    if model is None:
+        return jsonify({"error": "Model is not available. Cannot make predictions."}), 500
+
+    try:
+        # Get the JSON data from the request
+        # The data is expected to be a list of dictionaries, where each dictionary
+        # represents a single data point with features.
+        data = request.get_json()
+
+        # Convert the JSON data to a pandas DataFrame
+        # Ensure the column names in the JSON match the feature names expected by the model's preprocessor
+        input_df = pd.DataFrame(data)
+
+        # Make predictions using the loaded model
+        # The loaded model object (which is a Pipeline) handles both preprocessing and prediction
+        predictions = model.predict(input_df)
+
+        # Convert the numpy array of predictions to a list for JSON serialization
+        predictions_list = predictions.tolist()
+
+        # Return the predictions as a JSON response
+        return jsonify({"predictions": predictions_list})
+
+    except Exception as e:
+        # Return an error response if an exception occurs during processing (e.g., invalid input data)
+        return jsonify({"error": f"An error occurred during prediction: {e}"}), 400 # Use 400 for client-side errors
+
+
+# This block is for local development and testing only.
+# When deployed with Gunicorn on Hugging Face Spaces, this code is not executed.
+if __name__ == "__main__":
+    # Run the Flask app locally on port 7860 (common for Hugging Face Spaces)
+    # host="0.0.0.0" makes the server accessible externally (needed in Docker containers)
+    # debug=False for production deployment
+    SuperKart_predictions_api.run(host="0.0.0.0", port=7860, debug=False)