Update miniapp_leaderboard.py

miniapp_leaderboard.py

@@ -9,11 +9,11 @@ import gradio as gr
 import pandas as pd
 from huggingface_hub import HfApi, hf_hub_download
 
-APP_NAME = "MiniApp"
+APP_NAME = "MiniApp v2 - DEBUG"
 
-HF_TOKEN = os.environ.get("HF_TOKEN")
-LEADERBOARD_DATASET = os.environ.get("LEADERBOARD_DATASET", "").strip()
-OWNER_REVIEW_TOKEN = os.environ.get("OWNER_REVIEW_TOKEN", "").strip()
+HF_TOKEN = os.environ.get("HF_TOKEN") or ""
+LEADERBOARD_DATASET = (os.environ.get("LEADERBOARD_DATASET") or "").strip()
+OWNER_REVIEW_TOKEN = os.environ.get("OWNER_REVIEW_TOKEN") or ""
 
 PENDING_PREFIX = "pending/"
 APPROVED_PREFIX = "approved/"
@@ -39,71 +39,68 @@ def _slug(s: str):
     s = re.sub(r"[^a-z0-9]+", "-", (s or "").lower())
     return s.strip("-") or "model"
 
-
 def _load_df(prefix: str):
-    if not HF_TOKEN or not LEADERBOARD_DATASET:
-        return pd.DataFrame(columns=["rank"] + COLUMNS)
-
-    api = _api()
     try:
-        files = api.list_repo_files(repo_id=LEADERBOARD_DATASET, repo_type="dataset")
-    except Exception:
+        if not HF_TOKEN or not LEADERBOARD_DATASET:
+            return pd.DataFrame(columns=["rank"] + COLUMNS)
+
+        api = _api()
+        files = api.list_repo_files(
+            repo_id=LEADERBOARD_DATASET,
+            repo_type="dataset"
+        )
+
+        files = [f for f in files if f.startswith(prefix) and f.endswith(".json")]
+        rows = []
+
+        for f in files:
+            try:
+                path = hf_hub_download(
+                    repo_id=LEADERBOARD_DATASET,
+                    repo_type="dataset",
+                    filename=f,
+                    token=HF_TOKEN,
+                )
+                with open(path, "r") as fp:
+                    rows.append(json.load(fp))
+            except Exception:
+                continue
+
+        if not rows:
+            return pd.DataFrame(columns=["rank"] + COLUMNS)
+
+        df = pd.DataFrame(rows)
+
+        for c in COLUMNS:
+            if c not in df.columns:
+                df[c] = ""
+
+        for c in NUMERIC_COLS:
+            df[c] = pd.to_numeric(df[c], errors="coerce")
+
+        df = df.sort_values(by="avg", ascending=False).reset_index(drop=True)
+
+        ranks = []
+        for i in range(len(df)):
+            r = i + 1
+            if r == 1:
+                ranks.append("🥇 1")
+            elif r == 2:
+                ranks.append("🥈 2")
+            elif r == 3:
+                ranks.append("🥉 3")
+            else:
+                ranks.append(str(r))
+
+        df.insert(0, "rank", ranks)
+        df["rank"] = df["rank"].astype(str)
+
+        return df[["rank"] + COLUMNS]
+
+    except Exception as e:
+        print("LOAD ERROR:", e)
         return pd.DataFrame(columns=["rank"] + COLUMNS)
 
-    files = [f for f in files if f.startswith(prefix) and f.endswith(".json")]
-    rows = []
-
-    for f in files:
-        try:
-            path = hf_hub_download(
-                repo_id=LEADERBOARD_DATASET,
-                repo_type="dataset",
-                filename=f,
-                token=HF_TOKEN,
-            )
-            with open(path, "r") as fp:
-                rows.append(json.load(fp))
-        except Exception:
-            continue
-
-    if not rows:
-        return pd.DataFrame(columns=["rank"] + COLUMNS)
-
-    df = pd.DataFrame(rows)
-
-    for c in COLUMNS:
-        if c not in df.columns:
-            df[c] = ""
-
-    for c in NUMERIC_COLS:
-        df[c] = pd.to_numeric(df[c], errors="coerce")
-
-    # 按 avg 排序
-    df = df.sort_values(by="avg", ascending=False).reset_index(drop=True)
-
-    # 排序
-    df = df.sort_values(by="avg", ascending=False).reset_index(drop=True)
-    
-    # 生成排名
-    ranks = []
-    for i in range(len(df)):
-        rank_number = i + 1
-        if rank_number == 1:
-            ranks.append("🥇 1")
-        elif rank_number == 2:
-            ranks.append("🥈 2")
-        elif rank_number == 3:
-            ranks.append("🥉 3")
-        else:
-            ranks.append(str(rank_number))
-    
-    df.insert(0, "rank", ranks)
-    
-    # 强制 rank 为字符串类型
-    df["rank"] = df["rank"].astype(str)
-
-    return df[["rank"] + COLUMNS]
-
 def refresh():
     return _load_df(APPROVED_PREFIX)