Update app.py

app.py

@@ -218,26 +218,26 @@ def gradient_descent(n_samples=100, intercept=4, slope=3, intercept_random=4, sl
 
 
 #### Define input component
-input_sample = gr.inputs.Slider(1, 5000, step=50, default=100, label='N samples')
-input_intercept = gr.inputs.Slider(1, 8, step=0.5, default=4, label='(Baseline) Intercept')
-input_slope = gr.inputs.Slider(-8, 8, step=0.5, default=2.8, label='(Baseline) Slope')
+input_sample = gr.Slider(1, 5000, step=50, default=100, label='N samples')
+input_intercept = gr.Slider(1, 8, step=0.5, default=4, label='(Baseline) Intercept')
+input_slope = gr.Slider(-8, 8, step=0.5, default=2.8, label='(Baseline) Slope')
 
-input_intercept_random = gr.inputs.Slider(-8, 8, step=0.5, default=-7.5, label='(Random) Intercept')
-input_slope_random = gr.inputs.Slider(-8, 8, step=0.5, default=7.5, label='(Random) Slope')
+input_intercept_random = gr.Slider(-8, 8, step=0.5, default=-7.5, label='(Random) Intercept')
+input_slope_random = gr.Slider(-8, 8, step=0.5, default=7.5, label='(Random) Slope')
 
-input_gradients = gr.inputs.Checkbox(label="Apply Gradient Descent")
+input_gradients = gr.Checkbox(label="Apply Gradient Descent")
 #input_gradients_type = gr.inputs.CheckboxGroup(['Batch GradientDescient', 'Stochastic GradientDescent', 'Mini-Batch GradientDescent'],label="Type of Gradient Descent")
-input_gradients_type = gr.inputs.Dropdown(['Batch GradientDescent', 'Stochastic GradientDescent', 'Mini-Batch GradientDescent'],label="Type of Gradient Descent")
+input_gradients_type = gr.Dropdown(['Batch GradientDescent', 'Stochastic GradientDescent', 'Mini-Batch GradientDescent'],label="Type of Gradient Descent")
 
 
-input_batchsize = gr.inputs.Slider(1, 64, step=1, default=32, label='Batch size for Mini-BatchGD')
+input_batchsize = gr.Slider(1, 64, step=1, default=32, label='Batch size for Mini-BatchGD')
 
-input_learningrate = gr.inputs.Slider(0,2, step=0.001, default=0.001, label='Learning Rate')
-input_iteration = gr.inputs.Slider(1, 1000, step=2, default=100, label='Iteration')
+input_learningrate = gr.Slider(0,2, step=0.001, default=0.001, label='Learning Rate')
+input_iteration = gr.Slider(1, 1000, step=2, default=100, label='Iteration')
 
 
 #### Define output component
-output_plot1 = gr.outputs.Image(label="Regression plot")
+output_plot1 = gr.Image(label="Regression plot")
 
 
 ### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider