Update app.py

app.py

@@ -38,6 +38,7 @@ def add_new_eval(
     submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
     submission_repo.git_pull()
     filename = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    update_time = now.strftime("%Y-%m-%d")  # Capture update time
     now = datetime.datetime.now()
     with open(f'{SUBMISSION_NAME}/{filename}.zip','wb') as f:
         f.write(input_file)
@@ -103,7 +104,10 @@ def add_new_eval(
         new_data.append("User Upload")
     else:
         new_data.append(team_name)
-    new_data.append(contact_email)
+
+    new_data.append(contact_email) # Add contact email [private]
+    new_data.append(update_time)  # Add the update time
+
     csv_data.loc[col] = new_data
     csv_data = csv_data.to_csv(CSV_DIR, index=False)
     submission_repo.push_to_hub()
@@ -114,7 +118,7 @@ def get_normalized_df(df):
     # final_score = df.drop('name', axis=1).sum(axis=1)
     # df.insert(1, 'Overall Score', final_score)
     normalize_df = df.copy().fillna(0.0)
-    for column in normalize_df.columns[1:-2]:
+    for column in normalize_df.columns[1:-3]:
         min_val = NORMALIZE_DIC[column]['Min']
         max_val = NORMALIZE_DIC[column]['Max']
         normalize_df[column] = (normalize_df[column] - min_val) / (max_val - min_val)
@@ -166,7 +170,7 @@ def calculate_selected_score_i2v(df, selected_columns):
 def get_final_score(df, selected_columns):
     normalize_df = get_normalized_df(df)
     #final_score = normalize_df.drop('name', axis=1).sum(axis=1)
-    for name in normalize_df.drop('Model Name (clickable)', axis=1).drop('Source', axis=1).drop('Mail', axis=1):
+    for name in normalize_df.drop('Model Name (clickable)', axis=1).drop('Source', axis=1).drop('Mail', axis=1).drop('Date',axis=1):
         normalize_df[name] = normalize_df[name]*DIM_WEIGHT[name]
     quality_score = normalize_df[QUALITY_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in QUALITY_LIST])
     semantic_score = normalize_df[SEMANTIC_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in SEMANTIC_LIST ])
@@ -246,6 +250,7 @@ def get_baseline_df():
     df = get_final_score(df, checkbox_group.value)
     df = df.sort_values(by="Selected Score", ascending=False)
     present_columns = MODEL_INFO + checkbox_group.value
+    print(present_columns)
     df = df[present_columns]
     df = convert_scores_to_percentage(df)
     return df
@@ -320,7 +325,7 @@ def convert_scores_to_percentage(df):
     # 对DataFrame中的每一列（除了'name'列）进行操作
 
     if 'Source' in df.columns:
-        skip_col =2
+        skip_col =3
     else:
         skip_col =1
     for column in df.columns[skip_col:]:  # 假设第一列是'name'
@@ -649,4 +654,4 @@ with block:
         data_run.click(on_filter_model_size_method_change, inputs=[checkbox_group], outputs=data_component)
 
 
-block.launch()
+block.launch(server_name="0.0.0.0").queue()