Update app.py

app.py

@@ -34,14 +34,16 @@ else: print(f"警告：基础目录 '{BASE_IMAGE_DIR}' 不存在。将无法加
 SUBJECTS = ["subj01", "subj02", "subj05", "subj07"] 
 SENTINEL_TRIAL_INTERVAL = 20
 NUM_TRIALS_PER_RUN = 100 
+LOG_BATCH_SIZE = 5 # <--- 新增：每多少条选择日志进行一次批量上传
 
 DATASET_REPO_ID = "YanmHa/image-aligned-experiment-data"
-INDIVIDUAL_LOGS_FOLDER = "individual_choice_logs" # 单个日志文件夹（如果将来恢复单条保存，此名称仍可用）
-BATCH_LOG_FOLDER = "run_logs_batch" # 用于批量保存的文件夹
+INDIVIDUAL_LOGS_FOLDER = "individual_choice_logs" # 此文件夹可能不再主要使用，但变量保留
+BATCH_LOG_FOLDER = "run_logs_batch" 
 CSS = ".gr-block {margin-top: 4px !important; margin-bottom: 4px !important;} .compact_button { padding: 4px 8px; min-width: auto; }"
 
 # ==== 加载所有可用的目标图片 ====
 master_image_list = []
+# ... (master_image_list 加载逻辑与您上一版代码相同) ...
 if os.path.exists(TARGET_DIR):
     try:
         master_image_list = sorted(
@@ -55,9 +57,10 @@ if os.path.exists(TARGET_DIR):
 elif not os.path.exists(TARGET_DIR) and os.path.exists(BASE_IMAGE_DIR): print(f"错误：目标目录 '{TARGET_DIR}' 未找到。")
 if not master_image_list: print(f"关键错误：由于 '{TARGET_DIR}' 问题，无目标图片，实验无法进行。")
 
+
 # ==== 辅助函数 ====
 def get_next_trial_info(current_trial_idx_in_run, current_run_image_list_for_trial, num_trials_in_this_run_for_trial):
-    # ... (此函数与您上一版代码完全一致, 返回匿名显示标签和内部详细标签) ...
+    # ... (此函数与您上一版代码完全一致) ...
     global TARGET_DIR, METHOD_ROOTS, SUBJECTS, SENTINEL_TRIAL_INTERVAL
     if not current_run_image_list_for_trial or current_trial_idx_in_run >= num_trials_in_this_run_for_trial: return None, current_trial_idx_in_run
     img_filename_original = current_run_image_list_for_trial[current_trial_idx_in_run]
@@ -100,71 +103,53 @@ def get_next_trial_info(current_trial_idx_in_run, current_run_image_list_for_tri
         })
     return trial_info, current_trial_idx_in_run + 1
 
-# --- (原 save_single_log_to_hf_dataset 函数可以删除或注释掉，因为不再每次选择都调用) ---
-# def save_single_log_to_hf_dataset(log_entry, user_identifier_str):
-#     ...
+# def save_single_log_to_hf_dataset(log_entry, user_identifier_str): # <--- 不再需要此函数，注释或删除
+#     pass 
 
-# --- 新增/修改：批量保存累积日志的函数 ---
-def save_collected_logs_batch(list_of_log_entries, user_identifier_str, current_run_no_for_filename):
-    global DATASET_REPO_ID, BATCH_LOG_FOLDER # 使用 BATCH_LOG_FOLDER
+# --- 批量保存累积日志的函数 ---
+def save_collected_logs_batch(list_of_log_entries, user_identifier_str, batch_identifier): # batch_identifier 可以是轮次号或时间戳
+    global DATASET_REPO_ID, BATCH_LOG_FOLDER
     if not list_of_log_entries:
-        print("没有累积的日志可供批量保存。")
-        return True # 没有东西要保存，也算“成功”
+        print("批量保存：没有累积的日志。")
+        return True 
 
     identifier_safe = str(user_identifier_str if user_identifier_str else "unknown_user_session").replace('.', '_').replace(':', '_').replace('/', '_').replace(' ', '_')
-    print(f"用户 {identifier_safe} - 准备批量保存截至轮次 {current_run_no_for_filename} 的 {len(list_of_log_entries)} 条日志...")
+    print(f"用户 {identifier_safe} - 准备批量保存 {len(list_of_log_entries)} 条日志 (批次标识: {batch_identifier})...")
     
     try:
         token = os.getenv("HF_TOKEN")
-        if not token:
-            print("错误：HF_TOKEN 未设置。无法批量保存日志。")
-            return False # 指示保存失败
-        if not DATASET_REPO_ID:
-            print("错误：DATASET_REPO_ID 未配置。无法批量保存日志。")
-            return False # 指示保存失败
+        if not token: print("错误：HF_TOKEN 未设置。无法批量保存日志。"); return False
+        if not DATASET_REPO_ID: print("错误：DATASET_REPO_ID 未配置。无法批量保存日志。"); return False
 
         api = HfApi(token=token)
-        
         timestamp_str = datetime.now().strftime('%Y%m%d_%H%M%S_%f')
-        # 文件名可以指明这是哪一“批”的日志，例如基于当前的轮次号
-        batch_filename = f"batch_user-{identifier_safe}_upto-run{current_run_no_for_filename}_{timestamp_str}.jsonl"
-        # 路径结构: BATCH_LOG_FOLDER / USER_SESSION_ID_SAFE / FILENAME.jsonl
+        batch_filename = f"batch_user-{identifier_safe}_id-{batch_identifier}_{timestamp_str}_logs-{len(list_of_log_entries)}.jsonl"
         path_in_repo = f"{BATCH_LOG_FOLDER}/{identifier_safe}/{batch_filename}" 
 
         jsonl_content = ""
         for log_entry in list_of_log_entries:
             try:
-                if isinstance(log_entry, dict):
-                    jsonl_content += json.dumps(log_entry, ensure_ascii=False) + "\n"
-                else:
-                    print(f"警告：批量保存时，日志条目不是字典格式，跳过：{log_entry}")
+                if isinstance(log_entry, dict): jsonl_content += json.dumps(log_entry, ensure_ascii=False) + "\n"
+                else: print(f"警告：批量保存时，日志条目非字典：{log_entry}")
             except Exception as json_err:
-                print(f"错误：批量保存时，序列化单条日志时出错: {log_entry}. 错误: {json_err}")
-                jsonl_content += json.dumps({"error": "serialization_failed_in_batch", 
-                                             "original_data_preview": str(log_entry)[:100],
-                                             "timestamp": datetime.now().isoformat()}, ensure_ascii=False) + "\n"
+                print(f"错误：批量保存序列化单条日志时出错: {log_entry}. 错误: {json_err}")
+                jsonl_content += json.dumps({"error": "serialization_failed_in_batch", "original_data_preview": str(log_entry)[:100],"timestamp": datetime.now().isoformat()}, ensure_ascii=False) + "\n"
         
-        if not jsonl_content.strip():
-            print(f"用户 {identifier_safe} 截至轮次 {current_run_no_for_filename} 没有可序列化的日志内容。")
-            return True # 没有东西要上传
+        if not jsonl_content.strip(): print(f"用户 {identifier_safe} (批次 {batch_identifier}) 无可序列化日志。"); return True
 
         log_bytes = jsonl_content.encode('utf-8')
         file_like_object = io.BytesIO(log_bytes)
 
         print(f"准备批量上传日志文件: {path_in_repo} ({len(log_bytes)} bytes)")
         api.upload_file(
-            path_or_fileobj=file_like_object,
-            path_in_repo=path_in_repo,
-            repo_id=DATASET_REPO_ID,
-            repo_type="dataset", # 确保与您的DATASET_REPO_ID类型匹配
-            commit_message=f"Batch logs for user {identifier_safe} up to run {current_run_no_for_filename}"
+            path_or_fileobj=file_like_object, path_in_repo=path_in_repo, repo_id=DATASET_REPO_ID, repo_type="dataset",
+            commit_message=f"Batch logs for user {identifier_safe}, batch_id {batch_identifier} ({len(list_of_log_entries)} entries)"
         )
         print(f"批量日志已成功保存到 HF Dataset: {DATASET_REPO_ID}/{path_in_repo}")
-        return True # 指示保存成功
+        return True
     except Exception as e:
-        print(f"批量保存日志 (user {identifier_safe}, up to run {current_run_no_for_filename}) 到 Hugging Face Dataset 时发生严重错误: {e}")
-        traceback.print_exc()
-        return False # 指示保存失败
+        print(f"批量保存日志 (user {identifier_safe}, batch_id {batch_identifier}) 失败: {e}"); traceback.print_exc()
+        return False
 
 # ==== 主要的 Gradio 事件处理函数 ====
 def process_experiment_step(
@@ -172,7 +157,8 @@ def process_experiment_step(
     s_current_run_image_list_val, s_num_trials_this_run_val,
     action_type=None, choice_value=None, request: gr.Request = None
 ):
-    global master_image_list, NUM_TRIALS_PER_RUN, outputs_ui_components_definition
+    global master_image_list, NUM_TRIALS_PER_RUN, outputs_ui_components_definition, LOG_BATCH_SIZE
+
     output_s_trial_idx = s_trial_idx_val; output_s_run_no = s_run_no_val
     output_s_user_logs = list(s_user_logs_val); output_s_current_trial_data = dict(s_current_trial_data_val) if s_current_trial_data_val else {}
     output_s_user_session_id = s_user_session_id_val; output_s_current_run_image_list = list(s_current_run_image_list_val)
@@ -187,6 +173,7 @@ def process_experiment_step(
     if action_type == "record_choice":
         if output_s_current_trial_data.get("data") and output_s_current_trial_data["data"].get("left_internal_label"):
             chosen_internal_label = (output_s_current_trial_data["data"]["left_internal_label"] if choice_value == "left" else output_s_current_trial_data["data"]["right_internal_label"])
+            # ... (log_entry 创建逻辑，与上一版一致，包含 chosen_method, chosen_subject, chosen_filename) ...
             parsed_chosen_method, parsed_chosen_subject, parsed_chosen_filename = "N/A", "N/A", "N/A"
             if chosen_internal_label == "目标图像": parsed_chosen_method, parsed_chosen_subject, parsed_chosen_filename = "TARGET", "GT", output_s_current_trial_data["data"]["image_id"]
             else:
@@ -194,7 +181,7 @@ def process_experiment_step(
                 if len(parts) == 3: parsed_chosen_method, parsed_chosen_subject, parsed_chosen_filename = parts[0].strip(), parts[1].strip(), parts[2].strip()
                 elif len(parts) == 2: parsed_chosen_method, parsed_chosen_subject = parts[0].strip(), parts[1].strip()
                 elif len(parts) == 1: parsed_chosen_method = parts[0].strip()
-            log_entry = { # ... (log_entry 创建与之前一致) ...
+            log_entry = {
                 "timestamp": datetime.now().isoformat(), "user_identifier": user_identifier_for_logging, "run_no": output_s_run_no, 
                 "image_id": output_s_current_trial_data["data"]["image_id"],
                 "left_internal_label": output_s_current_trial_data["data"]["left_internal_label"], 
@@ -204,81 +191,67 @@ def process_experiment_step(
                 "trial_sequence_in_run": output_s_current_trial_data["data"]["cur_no"], 
                 "is_sentinel": output_s_current_trial_data["data"]["is_sentinel"]
             }
-            output_s_user_logs.append(log_entry) # <--- 累积日志到列表
+            output_s_user_logs.append(log_entry)
             print(f"用户 {user_identifier_for_logging} 记录选择 (img: {log_entry['image_id']})。当前批次日志数: {len(output_s_user_logs)}")
-            # !!! 移除 save_single_log_to_hf_dataset 的调用 !!!
+
+            # !!! 修改：每 LOG_BATCH_SIZE 条选择后尝试批量保存 !!!
+            if len(output_s_user_logs) >= LOG_BATCH_SIZE:
+                print(f"累积日志达到 {LOG_BATCH_SIZE} 条，准备批量保存...")
+                # 使用当前轮次号和时间戳组合作为批次标识符，或者仅用时间戳
+                batch_id_for_filename = f"run{output_s_run_no}_trial{output_s_trial_idx}_count{len(output_s_user_logs)}"
+                save_success = save_collected_logs_batch(list(output_s_user_logs), user_identifier_for_logging, batch_id_for_filename)
+                if save_success:
+                    print("批量日志已成功（或尝试）保存，将清空累积日志列表。")
+                    output_s_user_logs = [] # 清空已保存的日志
+                else:
+                    print("警告：批量日志保存失败。日志将继续累积，下次达到阈值时重试。")
         else:
             # ... (错误处理) ...
-            print(f"用户 {user_identifier_for_logging} 错误：记录选择时数据为空！")
+            print(f"用户 {user_identifier_for_logging} 错误：记录选择时数据为空！") # ... (rest of error handling) ...
             error_ui_updates = create_ui_error_tuple("记录选择时内部错误。", f"用户ID: {user_id_display_text} | 进度：{output_s_trial_idx}/{output_s_num_trials_this_run}")
             return output_s_trial_idx, output_s_run_no, output_s_user_logs, output_s_current_trial_data, output_s_user_session_id, output_s_current_run_image_list, output_s_num_trials_this_run, *error_ui_updates
 
+    # --- "start_experiment" 逻辑 ---
     if action_type == "start_experiment":
         is_first = (output_s_num_trials_this_run == 0 and output_s_trial_idx == 0 and output_s_run_no == 1)
         is_completed_for_restart = (output_s_num_trials_this_run > 0 and output_s_trial_idx >= output_s_num_trials_this_run)
-        
         if is_first or is_completed_for_restart:
-            # ... (开始新轮次的逻辑，与之前代码一致，例如：随机选择图片，重置trial_idx等) ...
-            # ... (注意：当 is_completed_for_restart 时，output_s_run_no 应该已经增加了) ...
-            # output_s_user_logs 不在这里重置，它会累积
-            if not master_image_list: error_ui = create_ui_error_tuple("错误: 无可用目标图片!", f"用户ID: {user_id_display_text} | 进度: 0/0"); return 0, output_s_run_no, output_s_user_logs, {}, output_s_user_session_id, [], 0, *error_ui # output_s_user_logs is passed as is
+            if not master_image_list: error_ui = create_ui_error_tuple("错误: 无可用目标图片!", f"用户ID: {user_id_display_text} | 进度: 0/0"); return 0, output_s_run_no, output_s_user_logs, {}, output_s_user_session_id, [], 0, *error_ui
             
-            # 这段逻辑用于确定是否是真正的“下一轮”并增加轮次号
-            # 如果是点击“开始实验”按钮且上一轮刚结束，则轮次号增加
+            # 如果是完成一轮后重启，并且此时累积的日志未达到 LOG_BATCH_SIZE，它们会保留到下一批
+            # 轮次号的增加在这里是正确的，用于显示
             if is_completed_for_restart: 
-                # 此时 output_s_trial_idx 指向的是刚完成轮次的总数，output_s_run_no 是刚完成的轮次号
-                # 所以，下一轮的轮次号应该是 output_s_run_no + 1
-                # 但 process_experiment_step 的状态管理是，它返回的值会成为下一次调用的输入值
-                # 所以，如果这里要开始新轮次，应该在这里增加 output_s_run_no
-                 output_s_run_no += 1 # 增加轮次号为新的一轮
-
+                output_s_run_no += 1
+            
             num_avail = len(master_image_list); run_size = min(num_avail, NUM_TRIALS_PER_RUN)
             if run_size == 0: error_ui = create_ui_error_tuple("错误: 采样图片数为0!", f"用户ID: {user_id_display_text} | 进度: 0/0"); return 0, output_s_run_no, output_s_user_logs, {}, output_s_user_session_id, [], 0, *error_ui
             
             output_s_current_run_image_list = random.sample(master_image_list, run_size)
             output_s_num_trials_this_run = run_size
-            output_s_trial_idx = 0 # 新轮次从0开始
-            # output_s_user_logs 不重置
+            output_s_trial_idx = 0
+            # output_s_user_logs 不在这里重置，它会持续累积直到达到 LOG_BATCH_SIZE
             output_s_current_trial_data = {}
             
-            # 只有在会话ID为空（即应用刚启动，用户第一次点击“同意”后，再点击“开始实验”）时才生成新的基于时间戳的ID
-            # 或者，如果策略是每轮实验都用新的用户ID，则可以在这里（is_first or is_completed_for_restart）重新生成
-            # 当前代码中，user_session_id 是在欢迎页的 handle_agree_and_start 中设置的，这里不再修改它
-            # user_identifier_for_logging 会使用这个ID
+            # 用户会话ID在欢迎页已设置，这里不再修改，除非有特殊需求
             print(f"开始/继续轮次 {output_s_run_no} (用户ID: {output_s_user_session_id}). 随机选择 {output_s_num_trials_this_run} 张图片.")
-
-        else: # 在轮次中途点击了开始按钮
+        else: # 在轮次中途点击开始
             print(f"用户 {user_identifier_for_logging} 在第 {output_s_run_no} 轮，试验 {output_s_trial_idx} 点击开始，但轮次未完成。忽略。")
             no_change_ui = create_no_change_tuple()
             return output_s_trial_idx, output_s_run_no, output_s_user_logs, output_s_current_trial_data, output_s_user_session_id, output_s_current_run_image_list, output_s_num_trials_this_run, *no_change_ui
-
-    # --- 轮次结束处理 ---
+            
+    # --- 轮次结束的UI处理 (不再包含批量保存逻辑) ---
     if output_s_trial_idx >= output_s_num_trials_this_run and output_s_num_trials_this_run > 0:
-        logs_for_this_run_count = sum(1 for log in output_s_user_logs if log.get("run_no") == output_s_run_no) # 粗略计算本轮日志数
-        if logs_for_this_run_count > 0 or not output_s_user_logs: # 如果本轮有日志，或总日志为空（可能刚开始）
-             print(f"用户 {output_s_user_session_id} 完成第 {output_s_run_no} 轮。累积日志数: {len(output_s_user_logs)}")
-
-        # 检查是否达到批量保存的条件
-        if output_s_run_no > 0 and output_s_run_no % 10 == 0:
-            if output_s_user_logs: # 只有当有日志时才尝试保存
-                print(f"达到第 {output_s_run_no} 轮，准备批量保存累积的日志...")
-                save_success = save_collected_logs_batch(list(output_s_user_logs), user_identifier_for_logging, output_s_run_no)
-                if save_success:
-                    print("批量日志已成功（或尝试）保存，将清空累积日志列表。")
-                    output_s_user_logs = [] # 清空已保存的日志
-                else:
-                    print("警告：批量日志保存失败。日志将继续累积。")
-            else:
-                print(f"第 {output_s_run_no} 轮结束，但累积日志为空，无需批量保存。")
-        
+        # 仅打印完成信息，实际的批量保存由 record_choice 中的计数器触发
+        print(f"用户 {output_s_user_session_id} 已完成第 {output_s_run_no} 轮。等待下一批或下一轮开始。")
         prog_text = f"用户ID: {output_s_user_session_id} | 进度：{output_s_num_trials_this_run}/{output_s_num_trials_this_run} | 第 {output_s_run_no} 轮 🎉"
-        ui_updates = list(create_ui_error_tuple(f"🎉 第 {output_s_run_no} 轮完成！请点击“开始试验 / 下一轮”开始新的轮次。", prog_text))
+        ui_updates = list(create_ui_error_tuple(f"🎉 第 {output_s_run_no} 轮完成！请点击“开始试验 / 下一轮”继续或开始新批次。", prog_text))
+        # ... (UI 更新与之前一致) ...
         ui_updates[7]=gr.update(interactive=True); ui_updates[8]=gr.update(interactive=False); ui_updates[9]=gr.update(interactive=False)
         ui_updates[0]=gr.update(value=None,visible=False); ui_updates[1]=gr.update(value=None,visible=False); ui_updates[2]=gr.update(value=None,visible=False)
         yield output_s_trial_idx, output_s_run_no, output_s_user_logs, output_s_current_trial_data, output_s_user_session_id, output_s_current_run_image_list, output_s_num_trials_this_run, *ui_updates; return
-    
-    # --- 获取并显示下一个试验 ---
-    # ... (与上一版代码相同，确保 prog_text 使用了新的 user_id_display_text 或 output_s_user_session_id) ...
+
+    # --- 获取并显示下一个试验 (与之前一致) ---
+    # ... (省略这部分代码，与您上一版相同，确保 prog_text 使用了更新后的用户ID格式) ...
     if not output_s_current_run_image_list or output_s_num_trials_this_run == 0:
         error_ui = create_ui_error_tuple("错误: 无法加载试验图片 (列表为空)", f"用户ID: {user_id_display_text} | 进度: N/A")
         return output_s_trial_idx, output_s_run_no, output_s_user_logs, {"data": None}, output_s_user_session_id, [], 0, *error_ui
@@ -302,12 +275,14 @@ def process_experiment_step(
     ui_show_candidates_updates[7]=gr.update(interactive=False); ui_show_candidates_updates[8]=gr.update(interactive=True); ui_show_candidates_updates[9]=gr.update(interactive=True)
     yield next_s_trial_idx_for_state, output_s_run_no, output_s_user_logs, output_s_current_trial_data, output_s_user_session_id, output_s_current_run_image_list, output_s_num_trials_this_run, *ui_show_candidates_updates
 
-
+# ==== Gradio UI 定义 ====
 welcome_page_markdown = """
 ## 欢迎加入实验！
 您好！非常感谢您抽出宝贵时间参与我们的视觉偏好评估实验。您的选择将帮助我们改进重建算法，让机器生成的图像更贴近人类视觉体验!
+
 1.  **实验目的**
     通过比较两幅 重建图像 与原始 目标图像 的相似度。
+
 2.  **操作流程**
     * 点击下方的「我已阅读并同意开始实验」按钮。
     * 然后点击主实验界面的「开始试验 / 下一轮」按钮。
@@ -315,17 +290,20 @@ welcome_page_markdown = """
     * 随后自动切换到 **两张重建图像**。
     * 根据刚才的观察记忆，选出您认为与目标图像最相似的一张。
     * 选择后系统会自动进入下一轮比较。
+
 3.  **温馨提示**
     * 请勿刷新或关闭页面，以免中断实验。
     * 若图片加载稍有延迟，请耐心等待；持续异常可联系邮箱 yangminghan@bupt.edu.cn。
     * 本实验将保护您的任何个人隐私信息，所有数据仅用于学术研究，请您认真选择和填写。
+
 4.  **奖励说明**
     * 完成全部轮次后，请截图记录您所完成的实验总数（可累积,页面左下角将显示进度,请保证截取到为您分配的ID，轮次）。
     * 将截图发送至邮箱 yangminghan@bupt.edu.cn，我们将在核验后发放奖励。
+
 再次感谢您的参与与支持！您每一次认真选择都对我们的研究意义重大。祝您一切顺利，实验愉快！
 """
-
 def handle_agree_and_start(name, gender, age, education, request: gr.Request):
+    # ... (此函数与您上一版代码完全一致) ...
     error_messages_list = []
     if not name or str(name).strip() == "": error_messages_list.append("姓名 不能为空。")
     if gender is None or str(gender).strip() == "": error_messages_list.append("性别 必须选择。")
@@ -335,16 +313,11 @@ def handle_agree_and_start(name, gender, age, education, request: gr.Request):
         except (ValueError, TypeError): error_messages_list.append("年龄必须是一个有效的数字。")
         else: 
             if not (1 <= num_age <= 120): error_messages_list.append("年龄必须在 1 到 120 之间。")
-    if education is None or str(education).strip() == "" or str(education).strip() == "其他" and not name: # 稍微修改逻辑，如果选了“其他”但没填姓名，也可能需要提示
-        # 更简单的逻辑：如果education是None或空字符串
-        if education is None or str(education).strip() == "":
-             error_messages_list.append("学历 必须选择。")
-
+    if education is None or str(education).strip() == "": error_messages_list.append("学历 必须选择。") # 修正：如果选“其他”但希望是有效输入，这里的逻辑可能需要调整
     if error_messages_list:
         full_error_message = "请修正以下错误：\n" + "\n".join([f"- {msg}" for msg in error_messages_list])
         print(f"用户输入验证失败: {full_error_message}")
         return gr.update(), False, gr.update(visible=True), gr.update(visible=False), full_error_message
-            
     s_name = str(name).strip().replace(" ","_").replace("/","_").replace("\\","_")
     s_gender = str(gender).strip().replace(" ","_").replace("/","_").replace("\\","_")
     s_age = str(int(float(age))) 
@@ -354,37 +327,28 @@ def handle_agree_and_start(name, gender, age, education, request: gr.Request):
     return user_id_str, True, gr.update(visible=False), gr.update(visible=True), ""
 
 with gr.Blocks(css=CSS, title="图像重建主观评估") as demo:
-    s_show_experiment_ui = gr.State(False)
-    s_trial_index = gr.State(0); s_run_no = gr.State(1); s_user_logs = gr.State([])
-    s_current_trial_data = gr.State({}); s_user_session_id = gr.State(None)
+    # ... (所有 State 变量定义，与您上一版代码相同) ...
+    s_show_experiment_ui = gr.State(False); s_trial_index = gr.State(0); s_run_no = gr.State(1)
+    s_user_logs = gr.State([]); s_current_trial_data = gr.State({}); s_user_session_id = gr.State(None)
     s_current_run_image_list = gr.State([]); s_num_trials_this_run = gr.State(0)
-
+    
     welcome_container = gr.Column(visible=True) 
     experiment_container = gr.Column(visible=False)
 
     with welcome_container:
+        # ... (欢迎页UI，与您上一版代码相同) ...
         gr.Markdown(welcome_page_markdown)
-        with gr.Row():
-            user_name_input = gr.Textbox(label="请输入您的姓名或代号 (例如 ZS 或 User001)", placeholder="例如：张三 -> ZS") # 修改了label
-            user_gender_input = gr.Radio(label="性别", choices=["男", "女", "其他", "不愿透露"], value="不愿透露")
-        with gr.Row():
-            user_age_input = gr.Number(label="年龄 (请输入1-120的整数)", minimum=1, maximum=120, step=1, value=25) # 修改了label和value
-            user_education_input = gr.Dropdown(label="学历", choices=["其他","初中及以下","高中（含中专）", "大专", "本科", "硕士", "博士（含在读）"], value="本科") # 修改了选项和value
+        with gr.Row(): user_name_input = gr.Textbox(label="请输入您的姓名或代号 (例如 ZS 或 User001)", placeholder="例如：张三 -> ZS"); user_gender_input = gr.Radio(label="性别", choices=["男", "女", "其他", "不愿透露"], value="不愿透露")
+        with gr.Row(): user_age_input = gr.Number(label="年龄 (请输入1-120的整数)", minimum=1, maximum=120, step=1, value=25); user_education_input = gr.Dropdown(label="学历", choices=["其他","初中及以下","高中（含中专）", "大专", "本科", "硕士", "博士（含在读）"], value="本科")
         welcome_error_msg = gr.Markdown(value="")
         btn_agree_and_start = gr.Button("我已阅读上述说明并同意参与实验")
 
     with experiment_container:
-        gr.Markdown("## 🧠 图像重建主观评估实验")
-        gr.Markdown(f"每轮实验大约有 {NUM_TRIALS_PER_RUN} 次比较。")
+        # ... (实验主界面UI，与您上一版代码相同，包括新的按钮布局和隐藏的标签) ...
+        gr.Markdown("## 🧠 图像重建主观评估实验"); gr.Markdown(f"每轮实验大约有 {NUM_TRIALS_PER_RUN} 次比较。")
         with gr.Row():
-            with gr.Column(scale=1, min_width=300):
-                left_img = gr.Image(label="左候选图", visible=False, height=400, interactive=False)
-                left_lbl = gr.Textbox(label="左图信息", visible=False, interactive=False, max_lines=1) 
-                btn_left = gr.Button("选择左图 (更相似)", interactive=False, elem_classes="compact_button")
-            with gr.Column(scale=1, min_width=300):
-                right_img = gr.Image(label="右候选图", visible=False, height=400, interactive=False)
-                right_lbl = gr.Textbox(label="右图信息", visible=False, interactive=False, max_lines=1)
-                btn_right = gr.Button("选择右图 (更相似)", interactive=False, elem_classes="compact_button")
+            with gr.Column(scale=1, min_width=300): left_img = gr.Image(label="左候选图", visible=False, height=400, interactive=False); left_lbl = gr.Textbox(label="左图信息", visible=False, interactive=False, max_lines=1); btn_left = gr.Button("选择左图 (更相似)", interactive=False, elem_classes="compact_button")
+            with gr.Column(scale=1, min_width=300): right_img = gr.Image(label="右候选图", visible=False, height=400, interactive=False); right_lbl = gr.Textbox(label="右图信息", visible=False, interactive=False, max_lines=1); btn_right = gr.Button("选择右图 (更相似)", interactive=False, elem_classes="compact_button")
         with gr.Row(): target_img = gr.Image(label="目标图像 (观察3秒后消失)", visible=False, height=400, interactive=False)
         with gr.Row(): status_text = gr.Markdown(value="请点击“开始试验 / 下一轮”按钮。")
         with gr.Row(): progress_text = gr.Markdown()
@@ -404,16 +368,14 @@ with gr.Blocks(css=CSS, title="图像重建主观评估") as demo:
         s_current_run_image_list, s_num_trials_this_run, *outputs_ui_components_definition 
     ]
 
-    btn_agree_and_start.click(
-        fn=handle_agree_and_start, 
-        inputs=[user_name_input, user_gender_input, user_age_input, user_education_input],
-        outputs=[s_user_session_id, s_show_experiment_ui, welcome_container, experiment_container, welcome_error_msg]
-    )
+    btn_agree_and_start.click(fn=handle_agree_and_start, inputs=[user_name_input, user_gender_input, user_age_input, user_education_input], outputs=[s_user_session_id, s_show_experiment_ui, welcome_container, experiment_container, welcome_error_msg])
     btn_start.click(fn=partial(process_experiment_step, action_type="start_experiment"), inputs=click_inputs_base, outputs=event_outputs, queue=True)
     btn_left.click(fn=partial(process_experiment_step, action_type="record_choice", choice_value="left"), inputs=click_inputs_base, outputs=event_outputs, queue=True)
     btn_right.click(fn=partial(process_experiment_step, action_type="record_choice", choice_value="right"), inputs=click_inputs_base, outputs=event_outputs, queue=True)
 
+# ==== 程序入口 ====
 if __name__ == "__main__":
+    # ... (与您上一版相同的启动检查和打印逻辑, 确保 allowed_paths 正确) ...
     if not master_image_list: print("\n关键错误：程序无法启动，因无目标图片。"); exit()
     else:
         print(f"从 '{TARGET_DIR}' 加载 {len(master_image_list)} 张目标图片。每轮选 {NUM_TRIALS_PER_RUN} 张。")
@@ -422,8 +384,9 @@ if __name__ == "__main__":
         if not SUBJECTS: print("警告: SUBJECTS ���表为空。")
         else: print(f"Subjects: {SUBJECTS}")
         print(f"日志保存到 Dataset: '{DATASET_REPO_ID}'")
-        if BATCH_LOG_FOLDER: print(f"  - 批量日志文件夹: '{BATCH_LOG_FOLDER}/'") # 使用 BATCH_LOG_FOLDER
-        if INDIVIDUAL_LOGS_FOLDER: print(f"  - 单个选择日志文件夹: '{INDIVIDUAL_LOGS_FOLDER}/'")
+        if BATCH_LOG_FOLDER: print(f"  - 批量日志文件夹: '{BATCH_LOG_FOLDER}/'")
+        # INDIVIDUAL_LOGS_FOLDER 的打印可以保留或移除，因为现在主要用批量
+        # if INDIVIDUAL_LOGS_FOLDER: print(f"  - 单个选择日志文件夹 (可能未使用): '{INDIVIDUAL_LOGS_FOLDER}/'") 
         if not os.getenv("HF_TOKEN"): print("警告: HF_TOKEN 未设置。日志无法保存。\n      请在 Space Secrets 中设置 HF_TOKEN。")
         else: print("HF_TOKEN 已找到。")
         path_to_allow_serving_from = BASE_IMAGE_DIR
@@ -431,7 +394,7 @@ if __name__ == "__main__":
         if os.path.exists(path_to_allow_serving_from) and os.path.isdir(path_to_allow_serving_from):
             allowed_paths_list.append(os.path.abspath(path_to_allow_serving_from))
             print(f"Gradio `demo.launch()` 配置 allowed_paths: {allowed_paths_list}")
-        else: print(f"关键警告：图片基础目录 '{path_to_allow_serving_from}' 不存在或非目录。")
+        else: print(f"关键警告：图片基础目录 '{path_to_allow_serving_from}' ({os.path.abspath(path_to_allow_serving_from) if path_to_allow_serving_from else 'N/A'}) 不存在或非目录。")
         print("启动 Gradio 应用...")
         if allowed_paths_list: demo.launch(allowed_paths=allowed_paths_list)
         else: demo.launch()