import gradio as gr
import numpy as np
from joblib import load

KNN_model = load('best_knn_model.joblib')

def model_prediction(longitude, latitude, housing_median_age,
                     total_rooms, total_bedrooms, population,
                     households, median_income):
  test_data = np.array([
      [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income]
  ])

  return KNN_model.predict(test_data)[0]

longitude = gr.Slider(-124.35, -114.310, step=0.5, label = "Longitude")
latitude = gr.Slider(32.45, 41.95, step=0.5, label = "Latitude")
housing_median_age = gr.Slider(1, 52, step=1, label = "Housing Median Age")
total_rooms = gr.Slider(2, 3932, step=100, label = "Total Rooms")
total_bedrooms = gr.Slider(1, 6445, step=100, label = "Total Bedrooms")
population = gr.Slider(3, 36820, step=1000, label = "Population")
households = gr.Slider(1, 6082, step=100, label = "Households")
median_income = gr.Slider(0.4999, 15, step=0.5, label = "Median Income")

default_test_data = np.array([
    [-124.35, 32.45, 1, 2, 1, 3, 1, 0.4999]
])

predicted_housing_price = gr.Number(label="Predicted Housing Price", value=KNN_model.predict(default_test_data)[0])

gr.Interface(fn=model_prediction,
             inputs=[longitude, latitude, housing_median_age,
                     total_rooms, total_bedrooms, population,
                     households, median_income],
             outputs=predicted_housing_price
            ).launch()