def homework04_solution(theta0, theta1, theta2, learning_rate):
  import numpy as np
  import pandas as pd

  print(theta0, theta1, theta2, learning_rate)

  def linear_predict(b0, b1, b2, x1, x2):
    y_hat = b0 + b1*x1 + b2*x2
    return y_hat

    
  def get_linear_results(data, theta0, theta1, theta2):
    ## (2) make linear prediction
    y_hat_list = []

    theta0_grad = 0
    theta1_grad = 0
    theta2_grad = 0
    for i in range(len(data)):
      x1 = data.iloc[i,0]
      x2 = data.iloc[i,1]
      y = data.iloc[i,2]
      y_hat = linear_predict(theta0, theta1, theta2, x1, x2)
      y_hat_list.append(y_hat)

      ## (3) calculate gradients
      theta0_grad = theta0_grad - 2/len(data)*(y - theta0 - theta1*x1 - theta2*x2)*1.0

      theta1_grad = theta1_grad - 2/len(data)*(y - theta0 - theta1*x1 - theta2*x2)*x1

      theta2_grad = theta2_grad - 2/len(data)*(y - theta0 - theta1*x1 - theta2*x2)*x2

    data['y_hat'] = y_hat_list

    data['y-y_hat'] = data['y'] - data['y_hat']
    data['(y-y_hat)^2'] = data['y-y_hat']*data['y-y_hat']
    return data, theta0_grad, theta1_grad, theta2_grad

  ## (1) load data
  X = np.array([[15,20], [30,16], [12,6.5], [13,20], [18,18]])
  y = [4.9, 5.8,6.5,7.3,7.2]
  data = pd.DataFrame(X, columns=['X1','X2'])
  data['y'] = y 

  ## (2) get regression table, gradients
  data, theta0_grad, theta1_grad, theta2_grad = get_linear_results(data, theta0, theta1, theta2)


  ### (3) summarize gradient results for question 3a

  data_t = data.T

  data_t = data_t.round(2)
  
  data_t.insert(loc=0, column='Name', value=['X1', 'X2', 'y', 'y_hat', 'y-y_hat', '(y-y_hat)^2'])

  data_t.columns = ['Name', '0', '1', '2', '3', '4']
  

  ### (4) summarize gradient results for question 3b

  MSE = data['(y-y_hat)^2'].mean()

  q3_mse  = MSE
  
  ### summarize gradient results for question 4 (2)

  ### update parameter using gradient descent  4 (3)
  
  theta0_new = theta0 - learning_rate*theta0_grad
  theta1_new = theta1 - learning_rate*theta1_grad
  theta2_new = theta2 - learning_rate*theta2_grad


  ### (5) recalculate linear regression table using new gradients
  data4,_,_,_ = get_linear_results(data, theta0_new, theta1_new, theta2_new)


  ### (6) summarize gradient results for question 4 (4)

  MSE = data4['(y-y_hat)^2'].mean()

  q4_mse  = MSE
  
  ### (7) return all results for Gradio visualization
  return data_t, q3_mse, theta0_grad, theta1_grad , theta2_grad, theta0_new, theta1_new, theta2_new, q4_mse

import numpy as np

import gradio as gr


### configure inputs
set_theta0 = gr.Number(value=0.1)
set_theta1 = gr.Number(value=0.1)
set_theta2 = gr.Number(value=0.1)
set_ita = gr.Number(value=0.001)

#print("check8")
#set_theta0 = gr.Slider(minimum = -10, maximum=10, step=0.01, value=0.1)
#set_theta1 = gr.Slider(minimum = -10, maximum=10, step=0.01, value=0.1)
#set_theta2 = gr.Slider(minimum = -10, maximum=10, step=0.01, value=0.1)
#set_ita = gr.Slider(minimum = 0, maximum=10, step=0.01, value=0.01)


### configure outputs
set_output_q3a = gr.Dataframe(type='pandas', label ='Question 3a')
set_output_q3b = gr.Textbox(label ='Question: What\'s Initial MSE loss')
set_output_q4a0 = gr.Textbox(label ='Question: What\'s theta0_grad')
set_output_q4a1 = gr.Textbox(label ='Question: What\'s theta1_grad')
set_output_q4a2 = gr.Textbox(label ='Question: What\'s theta2_grad')

set_output_q4b0 = gr.Textbox(label ='Question: What\'s theta0_new: updated by gradient descent')
set_output_q4b1 = gr.Textbox(label ='Question: What\'s theta1_new: updated by gradient descent')
set_output_q4b2 = gr.Textbox(label ='Question: What\'s theta2_new: updated by gradient descent')

set_output_q4b4 = gr.Textbox(label ='Question: What\'s New MSE after update the parameters using gradient descent')


### configure Gradio
interface = gr.Interface(fn=homework04_solution, 
                         inputs=[set_theta0, set_theta1, set_theta2, set_ita], 
                         outputs=[set_output_q3a, set_output_q3b, 
                                  set_output_q4a0, set_output_q4a1, set_output_q4a2, 
                                  set_output_q4b0, set_output_q4b1, set_output_q4b2,
                                  set_output_q4b4],
                         title="CSCI4750/5750(gradient descent): Linear Regression/Optimization", 
                         description= "Click examples below for a quick demo",
                         theme = 'huggingface'
                         )


interface.launch(debug=True)