Update app.py

app.py

@@ -186,20 +186,20 @@ def gradient_descent(intercept=4, slope=3, intercept_random=4, slope_random=3, g
 
 
 #### Define input component
-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_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=4, label='(Random) Intercept')
-input_slope_random = gr.inputs.Slider(-8, 8, step=0.5, default=-4.5, label='(Random) Slope')
+input_intercept_random = gr.Slider(-8, 8, step=0.5, default=4, label='(Random) Intercept')
+input_slope_random = gr.Slider(-8, 8, step=0.5, default=-4.5, label='(Random) Slope')
 
-input_gradients = gr.inputs.Checkbox(label="Apply Gradient Descent")
+input_gradients = gr.Checkbox(label="Apply Gradient Descent")
 
-input_learningrate = gr.inputs.Slider(0,1, step=0.001, default=0.01, label='Learning Rate')
-input_iteration = gr.inputs.Slider(1, 1000, step=5, default=200, label='Iteration')
+input_learningrate = gr.Slider(0,1, step=0.001, default=0.01, label='Learning Rate')
+input_iteration = gr.Slider(1, 1000, step=5, default=200, 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