Update app.py

app.py

@@ -7,7 +7,8 @@ import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
 from huggingface_hub import snapshot_download
 from collections import defaultdict
-
+from datasets import Dataset, DatasetDict
+from huggingface_hub import HfApi
 from src.about import (
     CITATION_BUTTON_LABEL,
     CITATION_BUTTON_TEXT,
@@ -32,6 +33,7 @@ from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REP
 from src.populate import get_evaluation_queue_df, get_leaderboard_df
 from src.submission.submit import add_new_eval
 
+HF_DATASET_REPO = "JunJiaGuo/Vid_result"
 
 def restart_space():
     API.restart_space(repo_id=REPO_ID)
@@ -164,7 +166,7 @@ def evaluate_uploaded_json(user_file, model_name):
             class_accuracy[category] = 0  
 
 
-    save_class_accuracy_to_csv(model_name, class_accuracy)
+    save_class_accuracy_to_hf_dataset(model_name, class_accuracy)
 
     class_accuracy_str = "\n".join(
         [
@@ -180,18 +182,21 @@ def evaluate_uploaded_json(user_file, model_name):
         f"Class-wise Accuracy:\n{class_accuracy_str}"
     )
 
-def save_class_accuracy_to_csv(model_name, class_accuracy):
- 
+def save_class_accuracy_to_hf_dataset(model_name, class_accuracy):
+    """将模型的 class 正确率存入 Hugging Face Dataset"""
+
+    # 创建 Pandas DataFrame
     data = {"Model Name": model_name}
     for cls in CLASS_LIST:
-        data[cls] = class_accuracy.get(cls, 0)  
+        data[cls] = class_accuracy.get(cls, 0)
+
     df = pd.DataFrame([data])
 
+    # 转换为 Hugging Face Dataset
+    dataset = Dataset.from_pandas(df)
 
-    if not os.path.exists(CSV_FILE):
-        df.to_csv(CSV_FILE, index=False)
-    else:
-        df.to_csv(CSV_FILE, mode='a', header=False, index=False)  
+    # 推送数据到 Hugging Face Dataset
+    dataset.push_to_hub(HF_DATASET_REPO, split="train")