import numpy as np
import joblib
import pandas as pd
from flask import Flask, request, jsonify

# Initialize Flask app
superkart_api = Flask("SuperKart Sales Prediction API")

# Load the trained model
model = joblib.load("backend_files/rental_price_prediction_model_v1_0.joblib")

# Root endpoint
@superkart_api.get('/')
def home():
    return "Welcome to the SuperKart Sales Prediction API!"

# Single prediction endpoint
@superkart_api.post('/v1/sales')
def predict_sales():
    data = request.get_json()

  sample = {
    'Product_Weight': property_data['Product_Weight'],
    'Product_Sugar_Content': property_data['Product_Sugar_Content'],
    'Product_Allocated_Area': property_data['Product_Allocated_Area'],
    'Product_Type': property_data['Product_Type'],
    'Product_MRP': property_data['Product_MRP'],
    'Store_Size': property_data['Store_Size'],
    'Store_Location_City_Type': property_data['Store_Location_City_Type'],
    'Store_Type': property_data['Store_Type'],
    'Store_Age': 2025 - property_data['Store_Establishment_Year']
}

    # Create DataFrame from incoming JSON
    input_df = pd.DataFrame([data])

    # Predict sales
    predicted_sales = model.predict(input_df)[0]
    predicted_sales = round(float(predicted_sales), 2)

    return jsonify({'Predicted Sales (₹)': predicted_sales})

# Batch prediction endpoint
@superkart_api.post('/v1/salesbatch')
def predict_sales_batch():
    file = request.files['file']
    input_df = pd.read_csv(file)

    # Predict for all rows
    predicted_sales = model.predict(input_df)
    predicted_sales = [round(float(s), 2) for s in predicted_sales]

    # If you have Product_Id column to identify rows
    if 'Product_Id' in input_df.columns:
        ids = input_df['Product_Id'].tolist()
    else:
        ids = list(range(1, len(predicted_sales) + 1))

    result = dict(zip(ids, predicted_sales))
    return jsonify(result)

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