import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsRegressor
from joblib import dump, load
import gradio as gr

scaler = load('scaler_lab4.joblib')
KNN_Regressor = load('knn_lab4.joblib')


## Building a Fubction for prediction:

def predictPrice(input1, input2, input3, input4, input5, input6, input7, input8):
    features = np.array([[input1, input2, input3, input4, input5, input6, input7, input8]])
    features_scaled = scaler.transform(features)
    prediction = KNN_Regressor.predict(features_scaled)
    return round(prediction.item(), 2)

    
## Buidling inputs and outputs:

input1 = gr.Slider(-124.35, -114.31, step=5, label = "Longitude")
input2 = gr.Slider(32.54, 41.95, step=5, label = "Latitude")
input3 = gr.Slider(1, 52.0, step=5, label = "Housing_median_age (Year)")
input4 = gr.Slider(1, 39320.0, step=5, label = "Total_rooms")
input5 = gr.Slider(1, 6445.0, step=5, label = "Total_bedrooms")
input6 = gr.Slider(1, 35682.0, step=5, label = "Population")
input7 = gr.Slider(1, 6082.0, step=5, label = "Households")
input8 = gr.Slider(0, 15.0, step=5, label = "Median_income")

output1 = gr.Textbox(label = "House Value")


##title Putting it all together:

gr.Interface(fn=predictPrice, inputs=[input1, input2, input3, input4, input5, input6, input7, input8],
             outputs=output1).launch(show_error=True, share=True)