Update app.py

app.py

@@ -313,10 +313,10 @@ intro_html = """
     <h1>On Rankings</h1>
     <p>One of the central experiences of being an academic is the experience of being ranked. We are ranked when we apply for graduate school, or maybe already for a master's degree. We are ranked when we're up for faculty positions. We are ranked when we submit abstracts for conferences. And when we publish papers, we do so, of course, in journals that are ranked. The places where we work, the departments, are ranked, of course, as well. But although rankings apparently are catnip to academics, and probably everybody else as well, we do have some agreement. Most people probably share the intuition that there's something weird or iffy about rankings, and the suspicion that maybe often they are not as informative as there are some beings absolutely everywhere who suggest.</p>
 """
-comment_distribution_image = """<p>This is the disrtribution from which our applicants will be sampled:</p>"""
+comment_distribution_image = """<p>This is the distribution from which our applicants will be sampled:</p>"""
 
 # Building the interface
-with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+with gr.Blocks(theme=gr.themes.Monochrome(primary_hue="red", secondary_hue="pink",spacing_size="sm",text_size="lg", radius_size="none")) as demo:
   with gr.Column():
     gr.HTML(intro_html)
     with gr.Row():
@@ -324,14 +324,15 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
         run_button = gr.Button("Run Simulations!")
 
         # control applicant distribution
-        with gr.Group():
-          alpha_slider = gr.Slider(0.1, 5, step=0.1, value=1.4, label="Alpha (β Distribution)")
-          beta_slider = gr.Slider(0.1, 5, step=0.1,value=2.7, label="Beta (β Distribution)")
+        # with gr.Group():
+        alpha_slider = gr.Slider(0.1, 5, step=0.1, value=1.4, label="Alpha (β Distribution)")
+        beta_slider = gr.Slider(0.1, 5, step=0.1,value=2.7, label="Beta (β Distribution)")
         # simumlation-settings:
-        num_applicants = gr.Slider(10, 300, step=10, value=100, label="Number of Applicants")
-        num_judges = gr.Slider(1, 100, step=1, value=7, label="Number of Judges")
-        ratings_per_applicant = gr.Slider(1, 5, step=1, value=3, label="Ratings per Applicant", info='how many different ratings each application gets.')
-        top_n = gr.Slider(1, 40, step=1, value=5, label="Top N")
+        with gr.Group():
+          num_applicants = gr.Slider(10, 300, step=10, value=100, label="Number of Applicants")
+          num_judges = gr.Slider(1, 100, step=1, value=7, label="Number of Judges")
+          ratings_per_applicant = gr.Slider(1, 5, step=1, value=3, label="Ratings per Applicant", info='how many different ratings each application gets.')
+          top_n = gr.Slider(1, 40, step=1, value=5, label="Top N")
 
         judge_error = gr.Slider(0, 10, step=1, value=2, label="Judge Error")
         judgment_coarse_graining_true_false = gr.Checkbox(value= True, label="Coarse grain judgements.")
@@ -343,8 +344,8 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
 
       with gr.Column():
         with gr.Group():
+          # gr.HTML(comment_distribution_image)
           beta_plot = gr.Plot(label="Applicants quality distribution")
-          gr.HTML(comment_distribution_image)
 
         # Your existing plot output
         plot_output = gr.Plot(label="Simulation Results",show_label=True)
@@ -354,7 +355,7 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
     run_button.click(
         run_simulation_and_plot,
         inputs=[num_runs, num_applicants, num_judges, ratings_per_applicant, top_n, alpha_slider, beta_slider, judge_error, judgment_coarse_graining,judgment_coarse_graining_true_false],
-        outputs=[plot_output]
+        outputs=[plot_output], scroll_to_output = True
     )
 
     alpha_slider.change(plot_beta_distribution, inputs=[alpha_slider, beta_slider,judge_error], outputs=[beta_plot])
@@ -364,5 +365,4 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
     demo.load(plot_beta_distribution, inputs=[alpha_slider, beta_slider,judge_error], outputs=[beta_plot])
 
 if __name__ == "__main__":
-    demo.launch(debug=True)
-
+    demo.launch(debug=True)