Create utils.py

utils.py

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+import pandas as pd
+import gradio as gr
+import csv
+import json
+import os
+import shutil
+from huggingface_hub import Repository
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+SUBJECTS = ["Biology", "Business", "Chemistry", "Computer Science", "Economics", "Engineering",
+            "Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"]
+
+MODEL_INFO = [
+    "Model (CoT)",
+    "Overall",
+    "Biology", "Business", "Chemistry", "Computer Science", "Economics", "Engineering",
+    "Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"]
+
+DATA_TITILE_TYPE = ['markdown', 'number', 'number', 'number', 'number', 'number', 'number',
+                    'number', 'number', 'number', 'number', 'number', 'number', 'number',
+                    'number', 'number']
+
+SUBMISSION_NAME = "mmlu_pro_leaderboard_submission"
+SUBMISSION_URL = os.path.join("https://huggingface.co/datasets/TIGER-Lab/", SUBMISSION_NAME)
+CSV_DIR = "./mmlu_pro_leaderboard_submission/results.csv"
+
+COLUMN_NAMES = MODEL_INFO
+
+LEADERBORAD_INTRODUCTION = """# MMLU-Pro Leaderboard
+    MMLU-Pro dataset, a more robust and challenging massive multi-task understanding dataset tailored to more \
+    rigorously benchmark large language models' capabilities. This dataset contains 12K \
+    complex questions across various disciplines.
+    """
+
+TABLE_INTRODUCTION = """
+    """
+
+LEADERBORAD_INFO = """
+We list the information of the used datasets as follows:<br>
+
+"""
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
+CITATION_BUTTON_TEXT = r""""""
+
+SUBMIT_INTRODUCTION = """# Submit on Science Leaderboard Introduction
+
+## ⚠ Please note that you need to submit the json file with following format:
+
+```json
+{
+    "Model": "[NAME]",
+    "Repo": "https://huggingface.co/[MODEL_NAME],"
+    "Overall": 56.7,
+    "Biology": 23.4, 
+    "Business": 45.6,
+    ...,
+    "Other: 56.7"
+}
+```
+After submitting, you can click the "Refresh" button to see the updated leaderboard(it may takes few seconds).
+
+"""
+
+
+def get_df():
+    repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN)
+    repo.git_pull()
+    df = pd.read_csv(CSV_DIR)
+    df = df.sort_values(by=['Overall'], ascending=False)
+    return df[COLUMN_NAMES]
+
+
+def add_new_eval(
+    input_file,
+):
+    if input_file is None:
+        return "Error! Empty file!"
+
+    upload_data = json.loads(input_file)
+    data_row = [f'[{upload_data["Model"]}]({upload_data["Repo"]})', upload_data['Overall']]
+    for subject in SUBJECTS:
+        data_row += [upload_data[subject]]
+
+    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL,
+                                 use_auth_token=HF_TOKEN, repo_type="dataset")
+    submission_repo.git_pull()
+
+    already_submitted = []
+    with open(CSV_DIR, mode='r') as file:
+        reader = csv.reader(file, delimiter=',')
+        for row in reader:
+            already_submitted.append(row[0])
+
+    if data_row[0] not in already_submitted:
+        with open(CSV_DIR, mode='a', newline='') as file:
+            writer = csv.writer(file)
+            writer.writerow(data_row)
+        
+        submission_repo.push_to_hub()
+        print('Submission Successful')
+    else:
+        print('The entry already exists')
+
+
+def refresh_data():
+    return get_df()
+
+
+