# 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
from flask_cors import CORS

# Initialize the Flask application
superkart_api = Flask("SuperKart Revenue Predictor")
CORS(superkart_api)

#model path needs to be updated to root once this is pushed
model_path = "deployment_files/Backend/SuperKart_Model_V1_0.joblib"

# Load the trained machine learning model
model = joblib.load("model_path")

# Health check route
@superkart_api.get('/')
def home():
    return "Welcome to SuperKart Sales Prediction"


    # Prediction route
@superkart_api.post('/v1/predict')
def predict_sales():
    try:
        # Parse JSON payload
        data = request.get_json()
        print("Raw incoming data:", data)

        # Validate expected fields
        required_fields = [
            'Product_Weight',
            'Product_Sugar_Content',
            'Product_Allocated_Area',
            'Product_MRP',
            'Store_Size',
            'Store_Location_City_Type',
            'Store_Type',
            'Store_Age_Years',
            'Product_Type_Category'
        ]
        missing_fields = [f for f in required_fields if f not in data]
        if missing_fields:
            return jsonify({'error': f"Missing fields: {missing_fields}"}), 400

        # Convert and transform input
        sample = {
            'Product_Weight': float(data['Product_Weight']),
            'Product_Sugar_Content': data['Product_Sugar_Content'],
            'Product_Allocated_Area_Log': np.log1p(float(data['Product_Allocated_Area'])),  # log-transform
            'Product_MRP': float(data['Product_MRP']),
            'Store_Size': data['Store_Size'],
            'Store_Location_City_Type': data['Store_Location_City_Type'],
            'Store_Type': data['Store_Type'],
            'Store_Age_Years': int(data['Store_Age_Years']),
            'Product_Type_Category': data['Product_Type_Category']
        }

        input_df = pd.DataFrame([sample])
        print("Transformed input for model:\n", input_df)

        # Make prediction
        prediction = model.predict(input_df).tolist()[0]
        return jsonify({'Predicted_Sales': prediction})

    except Exception as e:
        print("Error during prediction:", str(e))
        return jsonify({'error': f"Prediction failed: {str(e)}"}), 500

# Run the app (for local testing only)
if __name__ == '__main__':
    superkart_api.run(debug=True)