Rem deprecated gradio attributes; add esp to avoid /0 error; return examples.

app.py

@@ -34,6 +34,7 @@ NON_GENDERED_TOKEN_ID = 30
 LABEL_DICT = {GENDER_OPTIONS[0]: 9, GENDER_OPTIONS[1]: -9}
 CLASSES = list(LABEL_DICT.keys())
 NON_LOSS_TOKEN_ID = -100
+EPS = 1e-5  # to avoid /0 errors
 
 # Wikibio conts
 START_YEAR = 1800
@@ -289,7 +290,7 @@ def get_tokenized_text_with_metadata(input_text, indie_vars, dataset, male_gende
 def get_avg_prob_from_finetuned_outputs(outputs, is_masked, num_preds, gender):
     preds = torch.softmax(outputs[0][0].cpu(), dim=1, dtype=torch.double)
     pronoun_preds = torch.where(is_masked, preds[:,CLASSES.index(gender)], 0.0)
-    return round(torch.sum(pronoun_preds).item() / num_preds * 100, DECIMAL_PLACES)
+    return round(torch.sum(pronoun_preds).item() / (EPS + num_preds) * 100, DECIMAL_PLACES)
 
 
 def get_avg_prob_from_pipeline_outputs(mask_filled_text, gendered_token_ids, num_preds):
@@ -298,7 +299,7 @@ def get_avg_prob_from_pipeline_outputs(mask_filled_text, gendered_token_ids, num
         for pronoun in top_preds])
         for top_preds in mask_filled_text
     ]
-    return round(sum(pronoun_preds) / num_preds * 100, DECIMAL_PLACES)
+    return round(sum(pronoun_preds) / (EPS + num_preds) * 100, DECIMAL_PLACES)
 
 def get_figure(results, dataset, gender, indie_var_name, include_baseline=True):    
     colors = ['b', 'g', 'c', 'm', 'y', 'r', 'k']  # assert no
@@ -521,7 +522,13 @@ scientist_example = [
     "True",
     'She was a very well regarded scientist and her work won many awards.',
 ]
-
+building_example = [
+    WIKIBIO,
+    [BERT_LIKE_MODELS[0]],
+    "True",
+    "True",
+    "She always walked past the building built in DATE on her way to her job as an elementary school teacher.",
+]
 death_date_example = [
     WIKIBIO,
     BERT_LIKE_MODELS,
@@ -529,8 +536,6 @@ death_date_example = [
     "True",
     'Died in DATE, she was recognized for her great accomplishments to the field of teaching.'
 ]
-
-
 neg_reddit_example = [
     REDDIT,
     [BERT_LIKE_MODELS[0]],
@@ -539,39 +544,32 @@ neg_reddit_example = [
     'She is not good at anything. The work she does is always subpar.'
 ]
 
-
 gr.Interface(
     fn=predict_gender_pronouns,
     inputs=[
-        gr.inputs.Radio(
+        gr.Radio(
             [REDDIT, WIKIBIO],
-            default=WIKIBIO,
             type="value",
             label="Pick 'conditionally' fine-tuned model.",
-            optional=False,
         ),
-        gr.inputs.CheckboxGroup(
+        gr.CheckboxGroup(
             BERT_LIKE_MODELS,
-            default=[BERT_LIKE_MODELS[0]],
             type="value",
             label="Pick optional BERT base uncased model.",
         ),
-        gr.inputs.Dropdown(
+        gr.Dropdown(
             ["False", "True"],
             label="Normalize BERT-like model's predictions to gendered-only?",
-            default = "True",
             type="index",
         ),
-        gr.inputs.Dropdown(
+        gr.Dropdown(
             ["False", "True"],
             label="Include baseline predictions (dashed-lines)?",
-            default = "True",
             type="index",
         ),
-        gr.inputs.Textbox(
+        gr.Textbox(
             lines=5,
             label="Input Text: Sentence about a single person using some gendered pronouns to refer to them.",
-            default="She always walked past the building built in DATE on her way to her job as an elementary school teacher.",
         ),
     ],
     outputs=[
@@ -593,5 +591,5 @@ gr.Interface(
     title=title,
     description=description,
     article=article,
-    # examples=[scientist_example, death_date_example, neg_reddit_example] # Rem for debug
-).launch()
+    examples=[scientist_example, building_example, death_date_example, neg_reddit_example]
+).launch(debug=True)