rem n_fit, add descript, both plots same row

app.py

@@ -170,7 +170,7 @@ def get_avg_prob_from_pipeline_outputs(mask_filled_text, gendered_token, num_pre
     ]
     return round(sum(pronoun_preds) / (EPS + num_preds) * 100, DECIMAL_PLACES)
 
-
+# %%
 def get_figure(df, gender, n_fit=1):
     df = df.set_index('x-axis')
     cols = df.columns
@@ -212,7 +212,6 @@ def predict_gender_pronouns(
     indie_vars,
     split_key,
     normalizing,
-    n_fit,
     input_text,
 ):
     """Run inference on input_text for each model type, returning df and plots of precentage
@@ -265,9 +264,9 @@ def predict_gender_pronouns(
     results_df['female_pronouns'] = female_pronoun_preds
     results_df['male_pronouns'] = male_pronoun_preds
     female_fig = get_figure(results_df.drop(
-        'male_pronouns', axis=1), 'female', n_fit)
+        'male_pronouns', axis=1), 'female')
     male_fig = get_figure(results_df.drop(
-        'female_pronouns', axis=1), 'male', n_fit)
+        'female_pronouns', axis=1), 'male')
 
     return (
         target_text,
@@ -285,29 +284,26 @@ description = """
 
 place_example = [
     MODEL_NAMES[0],
-    ','.join(PLACES),
+    ', '.join(PLACES),
     'PLACE',
     "False",
-    1,
     'Born in PLACE, she was a teacher.'
 ]
 
 date_example = [
     MODEL_NAMES[0],
-    ','.join(DATES),
+    ', '.join(DATES),
     'DATE',
     "False",
-    2,
     'Born in DATE, she was a doctor.'
 ]
 
 
 subreddit_example = [
     MODEL_NAMES[2],
-    ','.join(SUBREDDITS),
+    ', '.join(SUBREDDITS),
     'SUBREDDIT',
     "False",
-    1,
     'I saw on r/SUBREDDIT that she is a hacker.'
 ]
 
@@ -324,8 +320,15 @@ def reddit_fn():
 demo = gr.Blocks()
 with demo:
     gr.Markdown("## Hunt for spurious correlations in our LLMs.")
-    gr.Markdown("Please see a better explanation in another [Space](https://huggingface.co/spaces/emilylearning/causing_gender_pronouns_two).")
+    gr.Markdown("Although genders are relatively evenly distributed across time, place and interests, there are also known gender disparities in terms of access to resources. We suggest that this access disparity can result in dataset selection bias, causing models to learn a surprising range of spurious associations.")
+    gr.Markdown("These spurious associations are often considered undesirable, as they do not match our intuition about the real-world domain from which we derive samples for inference-time prediction.")
+    gr.Markdown("Selection bias of samples into datasets is a zero-sum-game, with even our high quality datasets forced to trade off one for another, thus inducing selection bias into the learned associations of the model.")
+
 
+    gr.Markdown("One intuitive way to see the impact that changing one variable may have upon another is to look for a dose-response relationship, in which a larger intervention in the treatment (the value in text form injected in the otherwise unchanged text sample) produces a larger response in the output (the softmax probability of a gendered pronoun). Specifically, below are examples of sweeping through a spectrum of place, date and subreddit interest (we encourage you to try your own).")
+    
+    gr.Markdown("This requires a spectrum of less to more gender-equal values for each covariate. For date, it’s easy to just use time itself, as gender equality has generally improved with time, so we picked years ranging from 1800 - 1999. For place we used the bottom and top 10 Global Gender Gap ranked countries. And for subreddit, we use subreddit name ordered by subreddits that have an increasingly larger percentage of self-reported female commenters.")
+    #gr.Markdown("Please see a better explanation in another [Space](https://huggingface.co/spaces/emilylearning/causing_gender_pronouns_two).")
     
     with gr.Row():
         x_axis = gr.Textbox(
@@ -347,11 +350,6 @@ with demo:
             label="Normalize?",
             type="index",
         )
-        n_fit = gr.Dropdown(
-            list(range(1, 5)),
-            label="Degree of polynomial fit for dose response trend",
-            type="value",
-        )
     with gr.Row():
         input_text = gr.Textbox(
             lines=5,
@@ -363,7 +361,6 @@ with demo:
     with gr.Row():
         female_fig = gr.Plot(
             type="auto", label="Plot of softmax probability pronouns predicted female.")
-    with gr.Row():
         male_fig = gr.Plot(
             type="auto", label="Plot of softmax probability pronouns predicted male.")
     with gr.Row():
@@ -372,29 +369,32 @@ with demo:
             overflow_row_behaviour="show_ends",
             label="Table of softmax probability for pronouns predictions",
         )
-    gr.Markdown("x-axis sorted by older to more recent dates:")
+    gr.Markdown("X-axis sorted by older to more recent dates:")
     place_gen = gr.Button('Populate fields with a location example')
 
-    gr.Markdown("x-axis sorted by bottom 10 and top 10 Global Gender Gap ranked countries:")
+    gr.Markdown("X-axis sorted by bottom 10 and top 10 Global Gender Gap ranked countries:")
     date_gen = gr.Button('Populate fields with a date example')
 
-    gr.Markdown("x-axis sorted in order of increasing self-identified female participation (see [bburky demo](http://bburky.com/subredditgenderratios/)): ")
+    gr.Markdown("X-axis sorted in order of increasing self-identified female participation (see [bburky demo](http://bburky.com/subredditgenderratios/)): ")
     subreddit_gen = gr.Button('Populate fields with a subreddit example')
 
-    #https://github.com/gradio-app/gradio/issues/690#issuecomment-1118772919
     with gr.Row():
+
         date_gen.click(date_fn, inputs=[], outputs=[model_name,
-                       x_axis, place_holder, to_normalize, n_fit, input_text])
+                       x_axis, place_holder, to_normalize, input_text])
         place_gen.click(place_fn, inputs=[], outputs=[
-                        model_name, x_axis, place_holder, to_normalize, n_fit, input_text])
+                        model_name, x_axis, place_holder, to_normalize, input_text])
         subreddit_gen.click(reddit_fn, inputs=[], outputs=[
-                            model_name, x_axis, place_holder, to_normalize, n_fit, input_text])
+                            model_name, x_axis, place_holder, to_normalize, input_text])
     with gr.Row():
         btn = gr.Button("Hit submit")
         btn.click(
             predict_gender_pronouns,
             inputs=[model_name, x_axis, place_holder,
-                    to_normalize, n_fit, input_text],
+                    to_normalize, input_text],
             outputs=[sample_text, female_fig, male_fig, df])
 
 demo.launch(debug=True)
+
+
+# %%