def homework06_solution(theta0, theta1, theta2, learning_rate):
  import numpy as np
  import pandas as pd

  def logistic_predict(b0, b1, b2, x1, x2):
    z = b0 + b1*x1 + b2*x2
    p = 1 / (1 + np.exp(-z))
    return p
  def binary_cross_entropy(X, y, parameter_list): 
      entropy_loss = 0
      for n in range(0,len(X)):
          # calculate the linear output given the current weights
          z = parameter_list[0] + parameter_list[1]*X[n][0]+ parameter_list[2]*X[n][1]
          
          # calculate sigmoid output given z 
          p = 1 / (1 + np.exp(-z))

          # calculate the binary cross-entropy
          predict_1 = y[n] * np.log(p)
          predict_0 = (1-y[n]) * np.log(1-p)
          entropy_loss += -(predict_1 + predict_0) / len(X)
      return entropy_loss
      
  def get_logistic_results(data, theta0, theta1, theta2):
    ## (2) make logistic 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 = logistic_predict(theta0, theta1, theta2, x1, x2)
      y_hat_list.append(y_hat)

      ## (3) calculate gradients
      theta0_grad = theta0_grad - 1/len(data)*(y - y_hat)*1.0

      theta1_grad = theta1_grad - 1/len(data)*(y - y_hat)*x1

      theta2_grad = theta2_grad - 1/len(data)*(y - y_hat)*x2

    data['y_hat'] = y_hat_list

    data['y-y_hat'] = data['y'] - data['y_hat']
    data['(y-y_hat)*x_1'] = data['y-y_hat']*data.iloc[:,0]
    data['(y-y_hat)*x_2'] = data['y-y_hat']*data.iloc[:,1]
    return data, theta0_grad, theta1_grad, theta2_grad

  ## (1) load data
  X = np.array([[9,9], [0,0], [8,6], [3,3], [7,8], [0.5,0.5], [5,6], [6,7]])
  y = [1,0,1,0,1,0,1,1]
  data = pd.DataFrame(X, columns=['X1','X2'])
  data['y'] = y

  ## (2) get regression table, gradients q3.2
  data, theta0_grad, theta1_grad, theta2_grad = get_logistic_results(data, theta0, theta1, theta2)


  ### (3) summarize gradient results for question 3.2

  data_t = data.T

  data_t = data_t.round(5)
  
  data_t.insert(loc=0, column='Name', value=['X1', 'X2', 'y', 'y_hat', 'y-y_hat', '(y-y_hat)*x_1', '(y-y_hat)*x_2'])
  

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

  q3_loss  = binary_cross_entropy(X, y, [theta0, theta1, theta2])
  
  ### summarize gradient results for question Q3.3

  ### update parameter using gradient descent Q3.4
  
  theta0_new = theta0 - learning_rate*theta0_grad
  theta1_new = theta1 - learning_rate*theta1_grad
  theta2_new = theta2 - learning_rate*theta2_grad


  ### (6) summarize gradient results for question 3.4

  q3_loss_new = binary_cross_entropy(X, y, [theta0_new, theta1_new, theta2_new])
  
  ### (7) return all results for Gradio visualization
  data_final = data_t.T
  print(data_final['y-y_hat'].values[1:])
  y_diff_sum = data_final['y-y_hat'].values[1:].sum()
  y_diff_x1_sum = data_final['(y-y_hat)*x_1'].values[1:].sum()
  y_diff_x2_sum = data_final['(y-y_hat)*x_2'].values[1:].sum()
  return data_t.T, q3_loss, y_diff_sum, y_diff_x1_sum, y_diff_x2_sum, theta0_grad, theta1_grad , theta2_grad, theta0_new, theta1_new, theta2_new, q3_loss_new

import numpy as np

import gradio as gr


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


### configure outputs
set_output_q3a = gr.outputs.Dataframe(type='pandas', label ='Question 3.2')
set_output_q3b = gr.outputs.Textbox(label ='Question 3.1: Initial BCE loss')
set_output_q3c = gr.outputs.Textbox(label ='Question 3.2: sum(y-y_hat)')
set_output_q3d = gr.outputs.Textbox(label ='Question 3.2: sum((y-y_hat)*x_1)')
set_output_q3e = gr.outputs.Textbox(label ='Question 3.2: sum((y-y_hat)*x_2)')
set_output_q4a0 = gr.outputs.Textbox(label ='Question 3.3: theta0_grad')
set_output_q4a1 = gr.outputs.Textbox(label ='Question 3.3: theta1_grad')
set_output_q4a2 = gr.outputs.Textbox(label ='Question 3.3: theta2_grad')

set_output_q4b0 = gr.outputs.Textbox(label ='Question 3.4: theta0_new: updated by gradient descent')
set_output_q4b1 = gr.outputs.Textbox(label ='Question 3.4: theta1_new: updated by gradient descent')
set_output_q4b2 = gr.outputs.Textbox(label ='Question 3.4: theta2_new: updated by gradient descent')

set_output_q4b4 = gr.outputs.Textbox(label ='Question 4 (4): New BCE after update the parameters using gradient descent')

### configure Gradio
interface = gr.Interface(fn=homework06_solution, 
                         inputs=[set_theta0, set_theta1, set_theta2, set_ita], 
                         outputs=[set_output_q3a, set_output_q3b, set_output_q3c, set_output_q3d, set_output_q3e,
                                  set_output_q4a0, set_output_q4a1, set_output_q4a2, 
                                  set_output_q4b0, set_output_q4b1, set_output_q4b2,
                                  set_output_q4b4],
                         
                         title="CSCI4750/5750(hw06): Linear Regression/Optimization", 
                         description= "Click examples below for a quick demo",
                         theme = 'huggingface',
                         layout = 'horizontal',
                         live=True
                         )


interface.launch(debug=True)