update

app.py

@@ -13,8 +13,8 @@ from PIL import Image
 REPO_ID = "agenticx/TxAgentEvalData"
 EVALUATOR_MAP_DICT = "evaluator_map_dict.json"
 TXAGENT_RESULTS_SHEET_BASE_NAME = "TxAgent_Human_Eval_Results_CROWDSOURCED"
-our_methods = ['Q3-8B-qlora-biov13_merged']
-baseline_methods = ['TxAgent-T1-Llama-3.1-8B']
+our_methods = ['txagent']
+baseline_methods = ['Qwen3-8B']
 # Load tool lists from 'tool_lists' subdirectory
 tools_dir = os.path.join(os.getcwd(), 'tool_lists')
 
@@ -718,7 +718,8 @@ def get_next_eval_question(user_info, our_methods, return_user_info=True, includ
     validation_error = validate_required_fields(
         name, email, evaluator_id, specialty_dd, years_exp_radio)
     if validation_error:
-        return None, gr.update(visible=True), gr.update(visible=False), "Wrong info.", None, 0, None
+        # return None, gr.update(visible=True), gr.update(visible=False), "Wrong info.", None, 0, None
+        return None, gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), "Wrong info.", None, 0, None
 
     # 2. 获取评估者问题映射
     question_map_path = hf_hub_download(
@@ -732,6 +733,8 @@ def get_next_eval_question(user_info, our_methods, return_user_info=True, includ
     # 加载问题映射
     with open(question_map_path, 'r') as f:
         question_map = json.load(f)
+    
+    print(f"\033[91m{question_map}\033[0m")
 
     # 获取评估者目录
     evaluator_directory = question_map.get(evaluator_id, None)
@@ -752,7 +755,7 @@ def get_next_eval_question(user_info, our_methods, return_user_info=True, includ
         evaluator_id, all_files, evaluator_directory, our_methods)
 
     if len(full_question_ids_list) == 0:
-        return None, None, None, None, None, 0, None
+        return None, None, None, None, None, 0, None, None, None
 
     # 确定当前问题 ID 并收集模型数据
     full_question_ids_list = sorted(