add graph captions, remove g

app.py

@@ -84,7 +84,7 @@ def occ_graph(model):
     tda_res,occ_result = results[model]
     pass_gen,pass_skin = evaluate_by_occupation(occupation_df=occ_result,model=model)
     fig = generate_histplot(occ_result, 'a_median', 'gender_detected_val',
-                   title='Gender Distribution by Median Annualg Salary',
+                   title='Gender Distribution by Median Annual Salary',
                    xlabel= 'Median Annual Salary',
                    ylabel= 'Count',)
     fig2 = lumia_violinplot(df=occ_result, x_col='a_median',
@@ -169,21 +169,24 @@ if __name__=='__main__':
                     ss_pass = gr.HighlightedText(label="Skin Color Bias by Sentiment").style(color_map={"PASS":"green","FAIL":"red"})
                     with gr.Accordion("See Graph",open=False):
                         sent_skin = gr.Image()
-
+                        gr.Markdown("A violin plot depicting the distribution of skin color intensity values, binned by trait sentiment."
+                                    " Individual violin bins are colored by the median RGB intensity value of their bin, while the black notches signify the mean RGB intensity value.")
                     sg_pass = gr.HighlightedText(label="Gender Bias by Sentiment").style(
                         color_map={"PASS": "green", "FAIL": "red"})
                     with gr.Accordion("See Graph",open=False):
                         sent_gen = gr.Image()
-
+                        gr.Markdown("A histogram depicting gender distribution by trait sentiment")
                     os_pass = gr.HighlightedText(label="Skin Color Bias by Occupation/Income").style(
                         color_map={"PASS": "green", "FAIL": "red"})
                     with gr.Accordion("See Graph",open=False):
                         occ_skin = gr.Image()
-
+                        gr.Markdown("A violin plot depicting the distribution of skin color intensity values, binned by median salary."
+                                    " Individual violin bins are colored by the median RGB intensity value of their bin, while the black notches signify the mean RGB intensity value.")
                     og_pass = gr.HighlightedText(label="Gender Bias by Occupation/Income").style(
                         color_map={"PASS": "green", "FAIL": "red"})
                     with gr.Accordion("See Graph",open=False):
                         occ_gen = gr.Image()
+                        gr.Markdown("A histogram depicting gender distribution by median annual salary of occupational title")
             btn.click(fn=cached_results,inputs=model,outputs=[sample,ss_pass,sg_pass,sent_skin,sent_gen,os_pass,og_pass,occ_skin,occ_gen])
         with gr.Tab("Image Analysis"):
             gr.Markdown("# Generate an example image and view the automated analysis")