changed plot labeling for mobile, added more documentation

app.py

@@ -434,9 +434,9 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
 
         # control applicant distribution
         # with gr.Group():
-        loc_slider = gr.Slider(0,100, step=1, value=70, label="Mean (Population)")
-        scale_slider = gr.Slider(0, 80, step=1, value=50, label="Standard Deviation (Population)")
-        alpha_slider = gr.Slider(-10, 10, step=1, value=0, label="Skewness (Population)")
+        loc_slider = gr.Slider(0,100, step=1, value=70, label="Mean (Population)", info='Where the center of the distribution is.')
+        scale_slider = gr.Slider(0, 80, step=1, value=50, label="Standard Deviation (Population)", info='How wide the distribution is.')
+        alpha_slider = gr.Slider(-10, 10, step=1, value=0, label="Skewness (Population)", info='How asymmetric the distribution is.')
 
         # simumlation-settings:
         with gr.Group():
@@ -446,29 +446,29 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
           top_n = gr.Slider(1, 40, step=1, value=5, label="Top N", info='How many candidates can be selected.')
 
         judge_error = gr.Slider(0, 20, step=1, value=7, label="Judge Error", info='How much error judges can plausibly commit in their ratings.')
-        judges_attitude = gr.Slider(0, 10, step=.1, value=1.7, label="Judges attitude-range")
+        judges_attitude = gr.Slider(0, 10, step=.1, value=1.7, label="Judges attitude-range", info='How harsh/generous individual judges can be. (Max. skewness of their error distributions)')
 
-        judgment_coarse_graining_true_false = gr.Checkbox(value= True, label="Coarse grain judgements.")
-        judgment_coarse_graining = gr.Slider(0, 30, step=1, value=7, label="Coarse Graining Factor")
+        judgment_coarse_graining_true_false = gr.Checkbox(value= True, label="Coarse grain judgements.", info='Whether judgements are made on a coarser scale.')
+        judgment_coarse_graining = gr.Slider(0, 30, step=1, value=7, label="Coarse Graining Factor", info='Number of ratings on the judgement-scale.')
         num_runs = gr.Slider(10, 1000, step=10,value=100, label="Number of Runs")
 
         # The button to run the simulation
     # Sliders for alpha and beta parameters of the beta distribution
 
       with gr.Column():
-        with gr.Group():
-          population_plot = gr.Plot(label="Applicants quality distribution & judgement errors")
-          gr.Markdown("""Above you can see in red the distribution from which we draw the real qualities of our applicants.
-                         You can alter its **Mean, Scale and Skewness** on the left side. You can also see how large the errors are,
-                         which our judges commit, and how harshly the most harsh and most generous judges judge.
-                         You can alter these values by playing with the **Judge Error** and the **Judge's attitude range** on the left.""")
-        with gr.Group():
+        # with gr.Group():
+        population_plot = gr.Plot(label="Population",render=True)
+        gr.Markdown("""**Applicants quality distribution & judgement errors** – Above you can see in red the distribution from which we draw the real qualities of our applicants.
+                        You can alter its **Mean, Scale and Skewness** on the left side. You can also see how large the errors are,
+                        which our judges commit, and how harshly the most harsh and most generous judges judge.
+                        You can alter these values by playing with the **Judge Error** and the **Judge's attitude range** on the left.""")
+        # with gr.Group():
           # Your existing plot output
-          plot_output = gr.Plot(label="Simulation Results",show_label=True)
-          gr.Markdown("""Above are the results of our simulation. The green bars represent the frequency of the first, second,
-                        and subsequent applicants being selected for the **Top N** spots. The shade of green shows the rank each
-                        applicant achieved in the number of simulations. The grey area indicates the frequency with which applicants,
-                        who were objectively top candidates, failed to be selected into the Top N at all.""")
+        plot_output = gr.Plot(label="Results",show_label=True)
+        gr.Markdown("""**Simulation Results** – Above are the results of our simulation. The green bars represent the frequency of the first, second,
+                      and subsequent applicants being selected for the **Top N** spots. The shade of green shows the rank each
+                      applicant achieved in the number of simulations. The grey area indicates the frequency with which applicants,
+                      who were objectively top candidates, failed to be selected into the Top N at all.""")
 
     # Function call on button click
     run_button.click(