Update app.py

app.py

@@ -2,14 +2,13 @@ def homework04_solution(theta0, theta1, theta2, learning_rate):
   import numpy as np
   import pandas as pd
 
-  print("check1")
   print(theta0, theta1, theta2, learning_rate)
 
   def linear_predict(b0, b1, b2, x1, x2):
     y_hat = b0 + b1*x1 + b2*x2
     return y_hat
 
-  print("check2")
+    
   def get_linear_results(data, theta0, theta1, theta2):
     ## (2) make linear prediction
     y_hat_list = []
@@ -37,14 +36,12 @@ def homework04_solution(theta0, theta1, theta2, learning_rate):
     data['(y-y_hat)^2'] = data['y-y_hat']*data['y-y_hat']
     return data, theta0_grad, theta1_grad, theta2_grad
 
-  print("check3")
   ## (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 
 
-  print("check4")
   ## (2) get regression table, gradients
   data, theta0_grad, theta1_grad, theta2_grad = get_linear_results(data, theta0, theta1, theta2)
 
@@ -55,25 +52,21 @@ def homework04_solution(theta0, theta1, theta2, learning_rate):
 
   data_t = data_t.round(2)
   
-  print("check5")
   data_t.insert(loc=0, column='Name', value=['X1', 'X2', 'y', 'y_hat', 'y-y_hat', '(y-y_hat)^2'])
 
-  print(data_t)
-  print(data_t.columns)
+  data_t.columns = ['Name', '0', '1', '2', '3', '4']
   
 
   ### (4) summarize gradient results for question 3b
 
   MSE = data['(y-y_hat)^2'].mean()
 
-  print("check6")
   q3_mse  = MSE
   
   ### summarize gradient results for question 4 (2)
 
   ### update parameter using gradient descent  4 (3)
   
-  print("check7")
   theta0_new = theta0 - learning_rate*theta0_grad
   theta1_new = theta1 - learning_rate*theta1_grad
   theta2_new = theta2 - learning_rate*theta2_grad
@@ -98,21 +91,20 @@ 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=0.1)
+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=0.1)
 
-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)
+#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)
 
 
-print("check9")
 ### configure outputs
-set_output_q3a = gr.Dataframe(type='pandas', datatype="number", label ='Question 3a')
+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')
@@ -125,7 +117,6 @@ set_output_q4b2 = gr.Textbox(label ='Question: What\'s theta2_new: updated by gr
 set_output_q4b4 = gr.Textbox(label ='Question: What\'s New MSE after update the parameters using gradient descent')
 
 
-print("check10")
 ### configure Gradio
 interface = gr.Interface(fn=homework04_solution, 
                          inputs=[set_theta0, set_theta1, set_theta2, set_ita],