Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import pandas as pd
 import json
-from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS, LEADERBOARD_CSS
+from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS, LEADERBOARD_CSS, EXPLANATION
 from init import is_model_on_hub, upload_file, load_all_info_from_dataset_hub
 from utils_display import AutoEvalColumn, fields, make_clickable_model, styled_error, styled_message
 from datetime import datetime, timezone
@@ -11,6 +11,8 @@ LAST_UPDATED = "Nov 22th 2024"
 column_names = {
     "model": "Model",
     "Average WER ⬇️": "Average WER ⬇️",    
+    "Average Female WER ⬇️": "Average Female WER ⬇️",
+    "Average Male WER ⬇️": "Average Male WER ⬇️",
     "RTFx": "RTFx ⬆️️",
     "Bulgarian_female": "Bulgarian female",
     "Bulgarian_male": "Bulgarian male",
@@ -48,6 +50,13 @@ column_names = {
     "Vietnamese_male": "Vietnamese male",
 }
 
+african_cols = ["Ghanain English female", "Kenyan English female", "Kenyan English male", "Nigerian English female", "Nigerian English male"]
+north_american_cols = ["Mainstream US English female", "Mainstream US English male"]
+caribbean_cols = ["Jamaican English female", "Jamaican English male"]
+latin_american_cols = ["Latin American female", "Latin American male"]
+british_cols = ["Irish English female", "Irish English male", "Scottish English male", "Southern British English male"]
+european_cols = ["Eastern European male", "European male", "French female", "Italian female", "Spanish female", "Spanish male", "Catalan female", "Bulgarian female", "Bulgarian male", "Lithuanian male", "Romanian female"]
+asian_cols = ["Chinese female", "Chinese male", "Indonesian female", "Vietnamese female", "Vietnamese male", "Indian English female", "Indian English male"]
 eval_queue_repo, requested_models, csv_results = load_all_info_from_dataset_hub()
 
 if not csv_results.exists():
@@ -72,6 +81,67 @@ for col in original_df.columns:
  
 original_df.rename(columns=column_names, inplace=True)
 original_df.sort_values(by='Average WER ⬇️', inplace=True)
+female_cols = [col for col in original_df.columns if 'female' == col.split(' ')[-1]]
+male_cols = [col for col in original_df.columns if 'male' == col.split(' ')[-1]]
+
+# Create male DataFrame properly
+male_df = original_df[['Model'] + male_cols].copy()  # Create explicit copy with model column
+male_df.loc[:, 'Average Male WER ⬇️'] = male_df[male_cols].mean(axis=1)
+male_df.loc[:, 'Average Male WER ⬇️'] = male_df['Average Male WER ⬇️'].apply(formatter)
+male_df = male_df[['Model', 'Average Male WER ⬇️'] + male_cols]
+
+# Create female DataFrame properly
+female_df = original_df[['Model'] + female_cols].copy()  # Create explicit copy with model column
+female_df.loc[:, 'Average Female WER ⬇️'] = female_df[female_cols].mean(axis=1)
+female_df.loc[:, 'Average Female WER ⬇️'] = female_df['Average Female WER ⬇️'].apply(formatter)
+female_df = female_df[['Model', 'Average Female WER ⬇️'] + female_cols]
+
+african_df = original_df[['Model'] + african_cols].copy()
+african_df.loc[:, 'Average African WER ⬇️'] = african_df[african_cols].mean(axis=1)
+african_df.loc[:, 'Average African WER ⬇️'] = african_df['Average African WER ⬇️'].apply(formatter)
+african_df = african_df[['Model', 'Average African WER ⬇️'] + african_cols]
+
+north_american_df = original_df[['Model'] + north_american_cols].copy()
+north_american_df.loc[:, 'Average North American WER ⬇️'] = north_american_df[north_american_cols].mean(axis=1)
+north_american_df.loc[:, 'Average North American WER ⬇️'] = north_american_df['Average North American WER ⬇️'].apply(formatter)
+north_american_df = north_american_df[['Model', 'Average North American WER ⬇️'] + north_american_cols]
+
+caribbean_df = original_df[['Model'] + caribbean_cols].copy()
+caribbean_df.loc[:, 'Average Caribbean WER ⬇️'] = caribbean_df[caribbean_cols].mean(axis=1)
+caribbean_df.loc[:, 'Average Caribbean WER ⬇️'] = caribbean_df['Average Caribbean WER ⬇️'].apply(formatter)
+caribbean_df = caribbean_df[['Model', 'Average Caribbean WER ⬇️'] + caribbean_cols]
+
+latin_american_df = original_df[['Model'] + latin_american_cols].copy()
+latin_american_df.loc[:, 'Average Latin American WER ⬇️'] = latin_american_df[latin_american_cols].mean(axis=1)
+latin_american_df.loc[:, 'Average Latin American WER ⬇️'] = latin_american_df['Average Latin American WER ⬇️'].apply(formatter)
+latin_american_df = latin_american_df[['Model', 'Average Latin American WER ⬇️'] + latin_american_cols]
+
+british_df = original_df[['Model'] + british_cols].copy()
+british_df.loc[:, 'Average British WER ⬇️'] = british_df[british_cols].mean(axis=1)
+british_df.loc[:, 'Average British WER ⬇️'] = british_df['Average British WER ⬇️'].apply(formatter)
+british_df = british_df[['Model', 'Average British WER ⬇️'] + british_cols]
+
+european_df = original_df[['Model'] + european_cols].copy()
+european_df.loc[:, 'Average European WER ⬇️'] = european_df[european_cols].mean(axis=1)
+european_df.loc[:, 'Average European WER ⬇️'] = european_df['Average European WER ⬇️'].apply(formatter)
+european_df = european_df[['Model', 'Average European WER ⬇️'] + european_cols]
+
+asian_df = original_df[['Model'] + asian_cols].copy()
+asian_df.loc[:, 'Average Asian WER ⬇️'] = asian_df[asian_cols].mean(axis=1)
+asian_df.loc[:, 'Average Asian WER ⬇️'] = asian_df['Average Asian WER ⬇️'].apply(formatter)
+asian_df = asian_df[['Model', 'Average Asian WER ⬇️'] + asian_cols]
+
+
+# add average female and mal to original df and place it after average wer
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average African WER ⬇️', african_df['Average African WER ⬇️'])
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average North American WER ⬇️', north_american_df['Average North American WER ⬇️'])
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average Caribbean WER ⬇️', caribbean_df['Average Caribbean WER ⬇️'])
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average Latin American WER ⬇️', latin_american_df['Average Latin American WER ⬇️'])
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average British WER ⬇️', british_df['Average British WER ⬇️'])
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average European WER ⬇️', european_df['Average European WER ⬇️'])
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average Asian WER ⬇️', asian_df['Average Asian WER ⬇️'])
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average Female WER ⬇️', female_df['Average Female WER ⬇️'])
+original_df.insert(original_df.columns.get_loc('Average WER ⬇️') + 1, 'Average Male WER ⬇️', male_df['Average Male WER ⬇️'])
 
 COLS = [c.name for c in fields(AutoEvalColumn)]
 TYPES = [c.type for c in fields(AutoEvalColumn)]
@@ -80,14 +150,16 @@ TYPES = [c.type for c in fields(AutoEvalColumn)]
 with gr.Blocks(css=LEADERBOARD_CSS) as demo:
     # gr.HTML(BANNER, elem_id="banner")
     # Write a header with the title
-    gr.Markdown("<h1>🤗 Open Automatic Speech Recognition on EdAcc Dataset</h1>", elem_classes="markdown-text")
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+    gr.Markdown("<h1>🤗 Testing Model Robustness on EdAcc</h1>", elem_classes="markdown-text")
+
+    
+    gr.Markdown(EXPLANATION, elem_classes="markdown-text")
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0):
             # Add column filter dropdown
             column_filter = gr.Dropdown(
-                choices=["All"] + [v for k,v in column_names.items() if k != "model"],
+                choices=["All", "Female", "Male", "African", "North American", "Caribbean", "Latin American", "British", "European", "Asian"] + [v for k,v in column_names.items() if k != "model"],
                 label="Filter by column", 
                 multiselect=True,
                 value=["All"],
@@ -104,8 +176,38 @@ with gr.Blocks(css=LEADERBOARD_CSS) as demo:
             
             # Update table columns when filter changes
             def update_table(cols):
-                if "All" in cols:
-                    return gr.Dataframe(value=original_df)
+                # Dictionary mapping view names to their corresponding dataframes
+                view_mapping = {
+                    "All": original_df,
+                    "Female": female_df,
+                    "Male": male_df,
+                    "African": african_df,
+                    "North American": north_american_df,
+                    "Caribbean": caribbean_df,
+                    "Latin American": latin_american_df,
+                    "British": british_df,
+                    "European": european_df,
+                    "Asian": asian_df
+                }
+
+                # Handle special views
+                selected_special_views = [view for view in view_mapping.keys() if view in cols]
+                
+                if selected_special_views:
+                    # Start with the first selected view's columns
+                    result_cols = set(view_mapping[selected_special_views[0]].columns)
+                    
+                    # Take union of columns from all selected views
+                    for view in selected_special_views[1:]:
+                        result_cols.update(view_mapping[view].columns)
+                    
+                    # Convert to list and ensure "Model" is first
+                    result_cols = ["Model"] + sorted(list(result_cols - {"Model"}))
+                    
+                    # Merge all relevant columns from original_df
+                    return gr.Dataframe(value=original_df[result_cols])
+                
+                # If no special view is selected, return filtered columns from original df
                 selected_cols = ["Model"] + cols  # Always include the Model column
                 return gr.Dataframe(value=original_df[selected_cols])