clean code

app.py

@@ -1,20 +1,11 @@
-"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space."""
-import ast
-import argparse
-import glob
-import pickle
-
+"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space.""" 
+import argparse  
 import gradio as gr
 import numpy as np
 import pandas as pd
 import gradio as gr
-import pandas as pd
-from pathlib import Path
-import json
-from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS
-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
+import pandas as pd 
+from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS  
 
 LAST_UPDATED = "Feb 27th 2024"
 
@@ -54,15 +45,14 @@ model_info = {
 } 
 
 
-# Formats the columns
-def formatter(x):
-    if type(x) is str:
-        x = x
-    else: 
-        x = round(x, 2)
-    return x
 
- 
+def make_clickable_model(model_name, model_info):
+    if model_info[model_name]['hf_name'].startswith("http"):
+        link = model_info[model_name]['hf_name']
+    else:
+        link = f"https://huggingface.co/{model_info[model_name]['hf_name']}"
+    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_info[model_name]["pretty_name"]}</a>'
+
 
 def build_demo(original_df, TYPES):
     with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
@@ -116,7 +106,8 @@ if __name__ == "__main__":
         if col == "model":
             original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x, model_info)))
         else:
-            original_df[col] = original_df[col].apply(formatter) # For numerical values
+            pass 
+            # original_df[col] = original_df[col].apply(formatter) # For numerical values
     
     # Define the first column explicitly, add 'Overall' as the second column, and then append the rest excluding 'Overall'
     new_order = [original_df.columns[0], 'Overall'] + [col for col in original_df.columns if col not in [original_df.columns[0], 'Overall']]

init.py

@@ -1,95 +0,0 @@
-import os 
-import pandas as pd
-from constants import EVAL_REQUESTS_PATH
-from pathlib import Path
-from huggingface_hub import HfApi, Repository
-
-TOKEN_HUB = os.environ.get("TOKEN_HUB", None)
-QUEUE_REPO = os.environ.get("QUEUE_REPO", None)
-QUEUE_PATH = os.environ.get("QUEUE_PATH", None)
-
-hf_api = HfApi(
-    endpoint="https://huggingface.co", 
-    token=TOKEN_HUB, 
-)
-
-def load_all_info_from_dataset_hub():
-    eval_queue_repo = None
-    results_csv_path = None
-    requested_models = None
-
-    passed = True
-    if TOKEN_HUB is None:
-        passed = False
-    else:
-        print("Pulling evaluation requests and results.")
-
-        eval_queue_repo = Repository(
-            local_dir=QUEUE_PATH,
-            clone_from=QUEUE_REPO,
-            use_auth_token=TOKEN_HUB,
-            repo_type="dataset",
-        )
-        eval_queue_repo.git_pull()
-        
-        # Local directory where dataset repo is cloned + folder with eval requests
-        directory = QUEUE_PATH / EVAL_REQUESTS_PATH
-        requested_models = get_all_requested_models(directory)
-        requested_models = [p.stem for p in requested_models]
-        # Local directory where dataset repo is cloned
-        csv_results = get_csv_with_results(QUEUE_PATH)
-        # csv_results = pd.read_json(QUEUE_PATH, lines=True)
-        if csv_results is None:
-            passed = False
-    if not passed:
-        print("No HuggingFace token or result path provided. Skipping evaluation requests and results.")
-
-    return eval_queue_repo, requested_models, csv_results
-
-
-def upload_file(requested_model_name, path_or_fileobj):
-    dest_repo_file = Path(EVAL_REQUESTS_PATH) / path_or_fileobj.name
-    dest_repo_file = str(dest_repo_file)
-    hf_api.upload_file(
-            path_or_fileobj=path_or_fileobj,
-            path_in_repo=str(dest_repo_file),
-            repo_id=QUEUE_REPO,
-            token=TOKEN_HUB,
-            repo_type="dataset",
-            commit_message=f"Add {requested_model_name} to eval queue")
-
-def get_all_requested_models(directory):
-    directory = Path(directory)
-    all_requested_models = list(directory.glob("*.txt"))
-    return all_requested_models
-
-def get_csv_with_results(directory):
-    directory = Path(directory)
-    all_csv_files = list(directory.glob("*.csv"))
-    latest = [f for f in all_csv_files if f.stem.endswith("latest")]
-    if len(latest) != 1:
-        return None
-    return latest[0]
-
-
-
-def is_model_on_hub(model_name, revision="main") -> bool:
-    try:
-        model_name = model_name.replace(" ","")
-        author = model_name.split("/")[0]
-        model_id = model_name.split("/")[1]
-        if len(author) == 0 or len(model_id) == 0:
-            return False, "is not a valid model name. Please use the format `author/model_name`."
-    except Exception as e:
-        return False, "is not a valid model name. Please use the format `author/model_name`."
-
-    try:
-        models = list(hf_api.list_models(author=author, search=model_id))
-        matched = [model_name for m in models if m.modelId == model_name]
-        if len(matched) != 1:
-            return False, "was not found on the hub!"
-        else:
-            return True, None
-    except Exception as e:
-        print(f"Could not get the model from the hub.: {e}")
-        return False, "was not found on hub!"

utils_display.py

@@ -1,43 +0,0 @@
-from dataclasses import dataclass
-
-# These classes are for user facing column names, to avoid having to change them 
-# all around the code when a modif is needed 
-@dataclass
-class ColumnContent:
-    name: str
-    type: str 
-
-def fields(raw_class):
-    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
-
-@dataclass(frozen=True)
-class AutoEvalColumn: # Auto evals column
-    model = ColumnContent("Model", "markdown")
-    avg_wer = ColumnContent("Average WER ⬇️", "number")    
-    rtf = ColumnContent("RTF (1e-3) ⬇️", "number")
-    ami_wer = ColumnContent("AMI", "number")
-    e22_wer = ColumnContent("Earnings22", "number")
-    gs_wer = ColumnContent("Gigaspeech", "number")
-    lsc_wer = ColumnContent("LS Clean", "number")
-    lso_wer = ColumnContent("LS Other", "number")
-    ss_wer = ColumnContent("SPGISpeech", "number")
-    tl_wer = ColumnContent("Tedlium", "number")
-    vp_wer = ColumnContent("Voxpopuli", "number")
-    cv_wer = ColumnContent("Common Voice", "number")                       
-
-
-def make_clickable_model(model_name, model_info):
-    if model_info[model_name]['hf_name'].startswith("http"):
-        link = model_info[model_name]['hf_name']
-    else:
-        link = f"https://huggingface.co/{model_info[model_name]['hf_name']}"
-    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_info[model_name]["pretty_name"]}</a>'
-
-def styled_error(error):
-    return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
-
-def styled_warning(warn):
-    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
-
-def styled_message(message):
-    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"