update urls for qqmm (#38)

- add url for qqmm (5fd164df56de7d1c2a089ff2a330f779c316478f)
- fixed refresh issue (bf1a3f7ccd53bab9be62ccbfb9267ce6817e3498)

urls.csv

@@ -22,3 +22,4 @@ LLaVE-0.5B,https://huggingface.co/zhibinlan/LLaVE-0.5B
 UniME(LLaVA-OneVision-7B-LoRA-Res336),https://huggingface.co/DeepGlint-AI/UniME-LLaVA-OneVision-7B
 UniME(LLaVA-1.6-7B-LoRA-LowRes),https://huggingface.co/DeepGlint-AI/UniME-LLaVA-1.6-7B
 UniME(Phi-3.5-V-LoRA),https://huggingface.co/DeepGlint-AI/UniME-Phi3.5-V-4.2B
+QQMM-embed,https://github.com/QQ-MM/QQMM-embed

utils.py

@@ -101,8 +101,7 @@ Github link: https://github.com/TIGER-AI-Lab/VLM2Vec. \n
 Please send us an email at [email protected], attaching the JSON file. We will review your submission and update the leaderboard accordingly.
 """
 
-MODEL_URLS = pd.read_csv("urls.csv")
-MODEL_URLS = dict(zip(MODEL_URLS['Models'], MODEL_URLS['URL']))
+MODEL_URLS = get_urls("urls.csv")
 
 def create_hyperlinked_names(df):
     def convert_url(url, model_name):
@@ -130,14 +129,22 @@ def create_hyperlinked_names(df):
     df['Models'] = df['Models'].apply(add_link_to_model_name)
     return df
 
-def get_df():
+def fetch_data(url: str) -> pd.DataFrame:
     # fetch the leaderboard data
-    url = "https://huggingface.co/spaces/TIGER-Lab/MMEB/resolve/main/results.csv"
+    if url is None:
+        raise ValueError("URL Not Provided")
+    url = f"https://huggingface.co/spaces/TIGER-Lab/MMEB/resolve/main/{url}"
     response = requests.get(url, headers={"Authorization": f"Bearer {HF_TOKEN}"})
     if response.status_code != 200:
-        import sys
-        sys.exit(f"Error: {response.status_code}")
-    df = pd.read_csv(io.StringIO(response.text))
+        raise requests.HTTPError(f"Failed to fetch data: HTTP status code {response.status_code}")
+    return pd.read_csv(io.StringIO(response.text))
+
+def get_urls(csv: str='urls.csv') -> dict:
+    urls = fetch_data(csv)
+    return dict(zip(urls['Models'], urls['URL']))
+
+def get_df(csv="results.csv"):
+    df = fetch_data(csv)
     df.to_csv(CSV_DIR, index=False) # update local file
     df['Model Size(B)'] = df['Model Size(B)'].apply(process_model_size)
     df = df.sort_values(by=['Overall'], ascending=False)
@@ -184,6 +191,7 @@ def add_new_eval(input_file):
 
 def refresh_data():
     df = get_df()
+    MODEL_URLS = get_urls()
     return df[COLUMN_NAMES]