Update app.py

app.py

@@ -9,7 +9,7 @@ import io
 from huggingface_hub import HfApi
 from huggingface_hub.hf_api import HfHubHTTPError
 import traceback
-from itertools import combinations
+from itertools import combinations, product
 
 # ==== 全局配置 ====
 # ---- 测试模式开关 ----
@@ -93,12 +93,6 @@ if not os.path.exists(full_subdir_path):
 else:
     print(f"信息：持久化子目录 '{full_subdir_path}' 已存在。")
 
-# (调试代码可以按需保留或移除)
-# print(f"调试：检查 '{PERSISTENT_STORAGE_BASE}' 的内容：")
-# try:
-#     for item in os.listdir(PERSISTENT_STORAGE_BASE): print(f"  - Base Storage Item: {item}")
-# except Exception as e: print(f"  错误：列出 '{PERSISTENT_STORAGE_BASE}' 内容失败: {e}")
-
 GLOBAL_HISTORY_FILE = os.path.join(full_subdir_path, "global_experiment_shown_pairs.json")
 if not (os.path.isdir(full_subdir_path) and os.access(full_subdir_path, os.W_OK)):
      print(f"严重警告：持久化子目录 '{full_subdir_path}' 无效或不可写。")
@@ -106,13 +100,11 @@ print(f"全局历史文件将被加载/保存到: {GLOBAL_HISTORY_FILE}")
 
 global_shown_pairs_cache = {}
 global_history_has_unsaved_changes = False
-exhausted_target_images = set() # <--- 新增：存储已耗尽所有图片对的目标图片
+exhausted_target_images = set()
 
 def load_global_shown_pairs():
     global global_shown_pairs_cache, global_history_has_unsaved_changes, exhausted_target_images
-    # exhausted_target_images 在此版本中不从文件加载，每次启动重置。
-    # 如果需要持久化它，也需要加入到JSON的读写逻辑中。
-    exhausted_target_images = set() # 确保每次加载时重置（如果它不是持久化的）
+    exhausted_target_images = set()
 
     if not GLOBAL_HISTORY_FILE or not os.path.exists(GLOBAL_HISTORY_FILE):
         print(f"信息：全局历史文件 '{GLOBAL_HISTORY_FILE}' 未找到或路径无效。将创建新的空历史记录。")
@@ -146,9 +138,7 @@ def save_global_shown_pairs():
     if not GLOBAL_HISTORY_FILE:
         print("错误：GLOBAL_HISTORY_FILE 未定义。无法保存历史。")
         return False
-    # print(f"--- save_global_shown_pairs --- 当前工作目录: {os.getcwd()}") # 可按需保留
     final_save_path = os.path.abspath(GLOBAL_HISTORY_FILE)
-    # print(f"--- save_global_shown_pairs --- 尝试保存到文件: {final_save_path}") # 可按需保留
     try:
         parent_dir = os.path.dirname(final_save_path)
         if not os.path.exists(parent_dir):
@@ -162,30 +152,16 @@ def save_global_shown_pairs():
             target_img: [sorted(list(pair_fset)) for pair_fset in pairs_set]
             for target_img, pairs_set in global_shown_pairs_cache.items()
         }
-        # print(f"调试 SAVE_GLOBAL: 准备保存到JSON的完整data_to_save内容:\n{json.dumps(data_to_save, indent=2, ensure_ascii=False)}") # 可按需保留
-
+        
         temp_file_path = final_save_path + ".tmp"
         with open(temp_file_path, 'w', encoding='utf-8') as f:
             json.dump(data_to_save, f, ensure_ascii=False, indent=2)
         os.replace(temp_file_path, final_save_path)
         print(f"已成功将全局已展示图片对历史保存到 '{final_save_path}'。")
         global_history_has_unsaved_changes = False
-
-        # (保存后立即读取的调试代码可以按需保留或移除)
-        # print(f"调试：尝试立即读取已保存的文件 '{final_save_path}'...")
-        # try:
-        #     with open(final_save_path, 'r', encoding='utf-8') as f_read: content_read = f_read.read()
-        #     print(f"调试：成功读取文件内容（最多显示前500字符）:\n--BEGIN FILE CONTENT--\n{content_read[:500]}\n--END FILE CONTENT--")
-        #     if not content_read.strip(): print("调试：警告 - 读取到的文件内容为空或只有空白符。")
-        #     try:
-        #         json.loads(content_read)
-        #         print("调试：读取到的文件内容是有效的JSON。")
-        #     except json.JSONDecodeError as jde: print(f"调试：错误 - 读取到的文件内容不是有效的JSON: {jde}")
-        # except FileNotFoundError: print(f"调试：严重错误 - 刚刚保存的文件 '{final_save_path}' 在尝试立即读取时未找到！")
-        # except Exception as e_read: print(f"调试：读取文件 '{final_save_path}' 时发生其他错误: {e_read}")
         return True
     except Exception as e:
-        print(f"���误：保存全局历史文件 '{final_save_path}' 失败: {e}")
+        print(f"错误：保存全局历史文件 '{final_save_path}' 失败: {e}")
         return False
 
 load_global_shown_pairs()
@@ -209,21 +185,16 @@ if REPEAT_SINGLE_TARGET_FOR_TESTING:
     if not master_image_list:
         print(f"测试模式错误：master_image_list 为空，无法进行重复单一目标图测试。")
     else:
-        # test_target_image = "399162.jpg" # 您可以指定一个特定的测试图片
-        # if test_target_image in master_image_list:
-        #     master_image_list = [test_target_image]
-        #     print(f"测试模式：master_image_list 已成功设置为: {master_image_list}")
-        #     # load_global_shown_pairs_test() # 如果需要，在这里调用
-        # else:
         original_first_image = master_image_list[0]
         master_image_list = [original_first_image]
-        # print(f"测试模式警告：指定测试图片 '{test_target_image}' 不在原始列表中。")
         print(f"测试模式：master_image_list 已被缩减为原列表的第一个图像: {master_image_list}")
-if not master_image_list: # 最终检查
+if not master_image_list:
      print(f"关键错误：无目标图片可用 (master_image_list为空)。实验无法进行。")
-     # exit() # 考虑是否退出
 
 # ==== 辅助函数 ====
+# #############################################################################
+# ############# 函数修改点：get_next_trial_info ################################
+# #############################################################################
 def get_next_trial_info(current_trial_idx_in_run, current_run_image_list_for_trial, num_trials_in_this_run_for_trial):
     global TARGET_DIR, METHOD_ROOTS, SUBJECTS, SENTINEL_TRIAL_INTERVAL
     global global_shown_pairs_cache, global_history_has_unsaved_changes, exhausted_target_images
@@ -235,31 +206,34 @@ def get_next_trial_info(current_trial_idx_in_run, current_run_image_list_for_tri
     target_full_path = os.path.join(TARGET_DIR, img_filename_original)
     trial_number_for_display = current_trial_idx_in_run + 1
 
-    generated_pool = []
-    other_pool = []
+    # ---- MODIFICATION START: Create two separate pools for candidates ----
+    pool_generated_color = []
+    pool_other_methods = []
     
     for m_root_path in METHOD_ROOTS:
         method_name = os.path.basename(m_root_path)
-        is_generated_color = method_name == "generated_images_color"
-    
+        
         subjects_for_method = SUBJECTS
         if method_name.lower() == "takagi":
             if "subj01" in SUBJECTS:
                 subjects_for_method = ["subj01"]
             else:
                 continue
-    
+
         for s_id in subjects_for_method:
             base, ext = os.path.splitext(img_filename_original)
             reconstructed_filename = f"{base}_0{ext}"
             candidate_path = os.path.join(m_root_path, s_id, reconstructed_filename)
             if os.path.exists(candidate_path):
                 internal_label = f"{method_name}/{s_id}/{reconstructed_filename}"
-                if is_generated_color:
-                    generated_pool.append((internal_label, candidate_path))
+                candidate_tuple = (internal_label, candidate_path)
+                
+                # Segregate candidates into the two pools
+                if method_name == "generated_images_color":
+                    pool_generated_color.append(candidate_tuple)
                 else:
-                    other_pool.append((internal_label, candidate_path))
-
+                    pool_other_methods.append(candidate_tuple)
+    # ---- MODIFICATION END: Candidate pools are now populated ----
 
     trial_info = {"image_id": img_filename_original, "target_path": target_full_path, "cur_no": trial_number_for_display, "is_sentinel": False,
                   "left_display_label": "N/A", "left_internal_label": "N/A", "left_path": None,
@@ -268,13 +242,15 @@ def get_next_trial_info(current_trial_idx_in_run, current_run_image_list_for_tri
     is_potential_sentinel_trial = (trial_number_for_display > 0 and trial_number_for_display % SENTINEL_TRIAL_INTERVAL == 0)
 
     if is_potential_sentinel_trial:
-        if not pool: print(f"警告：哨兵图 '{img_filename_original}' (trial {trial_number_for_display}) 无候选。")
+        # For sentinel trials, we just need one random reconstruction. Combine pools to pick one.
+        combined_pool = pool_generated_color + pool_other_methods
+        if not combined_pool:
+            print(f"警告：哨兵图 '{img_filename_original}' (trial {trial_number_for_display}) 无任何候选图。")
         else:
-            # ... (哨兵试验逻辑不变) ...
             print(f"生成哨兵试验 for '{img_filename_original}' (trial {trial_number_for_display})")
             trial_info["is_sentinel"] = True
             sentinel_candidate_target_tuple = ("目标图像", target_full_path)
-            random_reconstruction_candidate_tuple = random.choice(pool)
+            random_reconstruction_candidate_tuple = random.choice(combined_pool)
             candidates_for_sentinel = [
                 (("目标图像", target_full_path), sentinel_candidate_target_tuple[0]),
                 (("重建图", random_reconstruction_candidate_tuple[1]), random_reconstruction_candidate_tuple[0])
@@ -285,20 +261,22 @@ def get_next_trial_info(current_trial_idx_in_run, current_run_image_list_for_tri
                 "right_display_label": candidates_for_sentinel[1][0][0], "right_path": candidates_for_sentinel[1][0][1], "right_internal_label": candidates_for_sentinel[1][1],
             })
     else: # 常规试验
-        if not generated_pool or not other_pool:
-            print(f"警告：常规图 '{img_filename_original}' (trial {trial_number_for_display}) 候选少于2 (找到 {len(pool)})。此试验无法进行。")
-            # 注意：如果这个目标图片是当前轮次中唯一的图片，或者后续图片也无法生成试验，轮次可能会提前结束。
-            # 我们可以在这里也将其标记为耗尽，以确保它在下一轮开始时被正确过滤。
-            # exhausted_target_images.add(img_filename_original) # 可选：立即标记，以防万一
+        # ---- MODIFICATION START: New check and pairing logic ----
+        # Check if both pools have at least one candidate
+        if not pool_generated_color or not pool_other_methods:
+            print(f"警告：常规图 '{img_filename_original}' (trial {trial_number_for_display}) 候选不足以形成指定对。 "
+                  f"('generated_images_color' 找到 {len(pool_generated_color)} 个, "
+                  f"其他方法找到 {len(pool_other_methods)} 个)。此试验无法进行。")
             return None, current_trial_idx_in_run
 
         target_global_history_set = global_shown_pairs_cache.setdefault(img_filename_original, set())
+        
+        # Generate all pairs by picking one from each pool
         all_possible_pairs_in_pool = []
-        for c1 in generated_pool:
-            for c2 in other_pool:
-                pair_labels_fset = frozenset({c1[0], c2[0]})
-                all_possible_pairs_in_pool.append(((c1, c2), pair_labels_fset))
-
+        for c_color, c_other in product(pool_generated_color, pool_other_methods):
+            pair_labels_fset = frozenset({c_color[0], c_other[0]})
+            all_possible_pairs_in_pool.append( ((c_color, c_other), pair_labels_fset) )
+        # ---- MODIFICATION END: New pairing logic is complete ----
 
         unseen_globally_pairs_with_data = [
             item for item in all_possible_pairs_in_pool if item[1] not in target_global_history_set
@@ -311,14 +289,12 @@ def get_next_trial_info(current_trial_idx_in_run, current_run_image_list_for_tri
             chosen_pair_frozenset = chosen_pair_data_and_labels[1]
             target_global_history_set.add(chosen_pair_frozenset)
             global_history_has_unsaved_changes = True
-            # print(f"调试：目标 '{img_filename_original}': 新全局唯一对 {chosen_pair_frozenset} 已添加。")
-        else: # 所有唯一对都已展示过
-            print(f"警告：目标图 '{img_filename_original}' (trial {trial_number_for_display}): 来自当前池的所有 ({len(all_possible_pairs_in_pool)}) 个候选对均已在全局展示过。")
-            if all_possible_pairs_in_pool: # 确保池中至少能形成一对
+        else:
+            print(f"警告：目标图 '{img_filename_original}' (trial {trial_number_for_display}): 所有 ({len(all_possible_pairs_in_pool)}) 个 'generated_color' vs 'other' 对均已在全局展示过。")
+            if all_possible_pairs_in_pool:
                 print(f"目标图 '{img_filename_original}' 将被标记为已耗尽，未来轮次中将被跳过。")
                 exhausted_target_images.add(img_filename_original)
-                # global_history_has_unsaved_changes = True # 如果 exhausted_target_images 需要持久化，这里也应该标记
-            return None, current_trial_idx_in_run # 返回 None，表示此图片本次无法生成新试验
+            return None, current_trial_idx_in_run
 
         display_order_candidates = list(selected_candidates_tuples)
         if random.random() > 0.5:
@@ -334,7 +310,7 @@ def save_single_log_to_hf_dataset(log_entry, user_identifier_str):
     global DATASET_REPO_ID, INDIVIDUAL_LOGS_FOLDER
     if not isinstance(log_entry, dict):
         print(f"错误：单个日志条目不是字典格式，无法保存：{log_entry}")
-        return False # 返回失败
+        return False
     current_user_id = user_identifier_str if user_identifier_str else "unknown_user_session"
     identifier_safe = str(current_user_id).replace('.', '_').replace(':', '_').replace('/', '_')
     print(f"用户 {identifier_safe} - 准备保存单条日志 for image {log_entry.get('image_id', 'Unknown')}...")
@@ -342,12 +318,11 @@ def save_single_log_to_hf_dataset(log_entry, user_identifier_str):
         token = os.getenv("HF_TOKEN")
         if not token:
             print("错误：环境变量 HF_TOKEN 未设置。无法保存单条日志到Dataset。")
-            return False # 返回失败
+            return False
         if not DATASET_REPO_ID:
             print("错误：DATASET_REPO_ID 未配置。无法保存单条日志到Dataset。")
-            return False # 返回失败
+            return False
         api = HfApi(token=token)
-        # ... (文件名和路径生成逻辑不变) ...
         image_id_safe_for_filename = os.path.splitext(log_entry.get("image_id", "unknown_img"))[0].replace('.', '_').replace(':', '_').replace('/', '_')
         file_creation_timestamp_str = datetime.now().strftime('%Y%m%d_%H%M%S_%f')
         unique_filename = (f"run{log_entry.get('run_no', 'X')}_trial{log_entry.get('trial_sequence_in_run', 'Y')}_img{image_id_safe_for_filename}_{file_creation_timestamp_str}.json")
@@ -357,10 +332,8 @@ def save_single_log_to_hf_dataset(log_entry, user_identifier_str):
             json_content = json.dumps(log_entry, ensure_ascii=False, indent=2)
         except Exception as json_err:
             print(f"错误：序列化单条日志时出错: {log_entry}. 错误: {json_err}")
-            # 即使序列化失败，也尝试记录一个最小化的错误日志
             error_log_content = {"error": "serialization_failed_single", "original_data_keys": list(log_entry.keys()) if isinstance(log_entry, dict) else None, "timestamp": datetime.now().isoformat()}
             json_content = json.dumps(error_log_content, ensure_ascii=False, indent=2)
-            # return False # 序列化失败也认为是一种保存失败
 
         log_bytes = json_content.encode('utf-8')
         file_like_object = io.BytesIO(log_bytes)
@@ -373,22 +346,22 @@ def save_single_log_to_hf_dataset(log_entry, user_identifier_str):
             commit_message=(f"Log choice: img {log_entry.get('image_id', 'N/A')}, run {log_entry.get('run_no', 'N/A')}, trial {log_entry.get('trial_sequence_in_run', 'N/A')} by {identifier_safe}")
         )
         print(f"单条日志已成功保存到 HF Dataset: {DATASET_REPO_ID}/{path_in_repo}")
-        return True # 返回成功
+        return True
     except HfHubHTTPError as hf_http_error:
         print(f"保存单条日志到 Hugging Face Dataset 时发生 HTTP 错误 (可能被限流或权限问题): {hf_http_error}")
         traceback.print_exc()
-        return False # 返回失败
+        return False
     except Exception as e:
         print(f"保存单条日志 (image {log_entry.get('image_id', 'Unknown')}, user {identifier_safe}) 到 Hugging Face Dataset 时发生严重错误: {e}")
         traceback.print_exc()
-        return False # 返回失败
+        return False
 
 # ==== 批量保存用户选择日志函数 (确保返回 True/False) ====
 def save_collected_logs_batch(list_of_log_entries, user_identifier_str, batch_identifier):
     global DATASET_REPO_ID, BATCH_LOG_FOLDER
     if not list_of_log_entries:
         print("批量保存用户日志：没有累积的日志。")
-        return True # 没有日志也算“成功”完成此操作
+        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 {len(list_of_log_entries)} 条选择日志 (批次标识: {batch_identifier})...")
     try:
@@ -400,7 +373,6 @@ def save_collected_logs_batch(list_of_log_entries, user_identifier_str, batch_id
             print("错误：DATASET_REPO_ID 未配置。无法批量保存选择日志。")
             return False
         api = HfApi(token=token)
-        # ... (文件名和内容生成逻辑不变) ...
         timestamp_str = datetime.now().strftime('%Y%m%d_%H%M%S_%f')
         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}"
@@ -415,7 +387,7 @@ def save_collected_logs_batch(list_of_log_entries, user_identifier_str, batch_id
 
         if not jsonl_content.strip():
             print(f"用户 {identifier_safe} (批次 {batch_identifier}) 无可序列化选择日志。")
-            return True # 没有内容可保存也算“成功”
+            return True
 
         log_bytes = jsonl_content.encode('utf-8')
         file_like_object = io.BytesIO(log_bytes)
@@ -456,12 +428,10 @@ def process_experiment_step(
     user_ip_fallback = request.client.host if request else "unknown_ip"
     user_identifier_for_logging = output_s_user_session_id if output_s_user_session_id else user_ip_fallback
     
-    # outputs_ui_components_definition 长度现在是 11
-    # (target_img, left_img, right_img, left_lbl, right_lbl, status_text, progress_text, btn_start, btn_left, btn_right, file_out_placeholder)
     len_ui_outputs = len(outputs_ui_components_definition)
 
     def create_ui_error_tuple(message, progress_msg_text, stop_experiment=False):
-        btn_start_interactive = not stop_experiment # 如果停止实验，开始按钮也禁用
+        btn_start_interactive = not stop_experiment
         btn_choices_interactive = not stop_experiment
         return (gr.update(visible=False),) * 3 + \
                ("", "") + \
@@ -474,7 +444,6 @@ 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"):
-            # ... (log_entry 创建逻辑不变) ...
             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"])
             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"]
@@ -496,7 +465,6 @@ def process_experiment_step(
             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)}")
             
-            # 尝试批量保存日志
             if len(output_s_user_logs) >= LOG_BATCH_SIZE:
                 print(f"累积用户选择日志达到 {LOG_BATCH_SIZE} 条，准备批量保存...")
                 batch_id_for_filename = f"run{output_s_run_no}_trialidx{output_s_trial_idx}_logcount{len(output_s_user_logs)}"
@@ -506,14 +474,12 @@ def process_experiment_step(
                     output_s_user_logs = []
                 else:
                     print("严重错误：批量用户选择日志保存失败。实验无法继续。")
-                    # 更新UI以显示错误并停止实验
                     error_message_ui = "错误：日志保存失败，可能是网络问题或API限流。实验已停止，请联系管理员。"
                     progress_message_ui = f"用户ID: {user_id_display_text} | 实验因错误停止在第 {output_s_run_no} 轮，试验 {output_s_trial_idx+1}"
                     error_ui_updates = create_ui_error_tuple(error_message_ui, progress_message_ui, stop_experiment=True)
                     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
                 
-                # 尝试保存全局历史
                 if global_history_has_unsaved_changes:
                     print("检测到全局图片对历史自上次保存后有更新，将一并保存...")
                     if not save_global_shown_pairs():
@@ -525,18 +491,15 @@ def process_experiment_step(
                                output_s_user_session_id, output_s_current_run_image_list, output_s_num_trials_this_run, *error_ui_updates
         else:
             print(f"用户 {user_identifier_for_logging} 错误：记录选择时当前试验数据为空或缺少internal_label！")
-            # ... (这里的错误处理保持不变，因为它不是API保存错误) ...
-            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}", stop_experiment=False) # 这种错误不一定停止实验
+            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}", stop_experiment=False)
             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 和后续的试验获取逻辑基本保持不变) ...
-    # --- 在 start_experiment 开始新轮次前，或在轮次结束时，也需要检查并尝试保存剩余日志和全局历史 ---
     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_completed_for_restart: # 如果是完成一轮后准备开始下一轮
-            if output_s_user_logs: # 尝试保存上一轮剩余的日志
+        if is_completed_for_restart:
+            if output_s_user_logs:
                 print(f"轮次 {output_s_run_no-1} 结束，尝试保存剩余的 {len(output_s_user_logs)} 条用户选择日志...")
                 batch_id_for_filename = f"run{output_s_run_no-1}_final_logcount{len(output_s_user_logs)}"
                 if not save_collected_logs_batch(list(output_s_user_logs), user_identifier_for_logging, batch_id_for_filename):
@@ -546,7 +509,7 @@ def process_experiment_step(
                     error_ui_updates = create_ui_error_tuple(error_message_ui, progress_message_ui, stop_experiment=True)
                     return output_s_trial_idx, output_s_run_no-1, 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
-                output_s_user_logs = [] # 清空
+                output_s_user_logs = []
             
             if global_history_has_unsaved_changes:
                 print("轮次结束，尝试保存全局图片对历史...")
@@ -558,29 +521,25 @@ def process_experiment_step(
                     return output_s_trial_idx, output_s_run_no-1, 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
         
-        # ... (后续的 start_experiment 逻辑不变，如选择图片，重置状态等) ...
         if is_first or is_completed_for_restart:
             if is_completed_for_restart: output_s_run_no += 1
             available_master_images = [img for img in master_image_list if img not in exhausted_target_images]
             print(f"开始轮次 {output_s_run_no}: 从 {len(master_image_list)}个总目标图片中筛选，可用图片 {len(available_master_images)}个 (已排除 {len(exhausted_target_images)}个已耗尽图片).")
             if not available_master_images:
-                # ... (所有图片耗尽的逻辑不变) ...
                 msg = "所有目标图片的所有唯一图片对均已展示完毕！感谢您的参与。"
                 prog_text = f"用户ID: {user_id_display_text} | 实验完成！"
-                # 在这里，如果还有未保存的日志，也应该尝试保存
                 if output_s_user_logs:
                     print(f"最终轮次结束，尝试保存剩余的 {len(output_s_user_logs)} 条用户选择日志...")
-                    batch_id_for_filename = f"run{output_s_run_no-1}_final_logcount{len(output_s_user_logs)}" # 使用上一轮的run_no
-                    save_collected_logs_batch(list(output_s_user_logs), user_identifier_for_logging, batch_id_for_filename) # 即使失败，也继续显示完成信息
+                    batch_id_for_filename = f"run{output_s_run_no-1}_final_logcount{len(output_s_user_logs)}"
+                    save_collected_logs_batch(list(output_s_user_logs), user_identifier_for_logging, batch_id_for_filename)
                     output_s_user_logs = []
                 if global_history_has_unsaved_changes:
                     print("实验最终结束，尝试保存全局图片对历史...")
-                    save_global_shown_pairs() # 即使失败，也继续
+                    save_global_shown_pairs()
 
-                ui_updates = list(create_ui_error_tuple(msg, prog_text, stop_experiment=True)) # 停止实验
+                ui_updates = list(create_ui_error_tuple(msg, prog_text, stop_experiment=True))
                 return 0, output_s_run_no, [], {}, output_s_user_session_id, [], 0, *tuple(ui_updates)
 
-            # ... (后续 start_experiment 逻辑：选择图片，重置 trial_idx, user_logs, current_trial_data 等)
             if REPEAT_SINGLE_TARGET_FOR_TESTING and available_master_images:
                 print(f"测试模式 (重复单一目标图) 已激活。")
                 single_image_to_repeat = available_master_images[0]
@@ -592,44 +551,31 @@ def process_experiment_step(
                 current_run_max_trials = NUM_TRIALS_PER_RUN
                 run_size = min(num_really_avail, current_run_max_trials)
                 if run_size == 0:
-                    error_ui = create_ui_error_tuple("错误: 可用图片采样数为0!", f"用户ID: {user_id_display_text} | 进度: 0/0", stop_experiment=False) # 不一定停止
+                    error_ui = create_ui_error_tuple("错误: 可用图片采样数为0!", f"用户ID: {user_id_display_text} | 进度: 0/0", stop_experiment=False)
                     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(available_master_images, run_size)
                 output_s_num_trials_this_run = run_size
             
             output_s_trial_idx = 0
-            output_s_current_trial_data = {} # 新轮次重置当前试验数据
-            # output_s_user_logs 已经在 is_completed_for_restart 分支中被清空（如果保存成功）
-            # 如果是 is_first，它本身就是空的
-            if is_first: # 只有第一次启动时分配新用户ID
+            output_s_current_trial_data = {}
+            if is_first:
                 timestamp_str = datetime.now().strftime('%Y%m%d%H%M%S%f'); random_val = random.randint(10000, 99999)
-                if not output_s_user_session_id: # 确保只在没有 session_id 时创建 (例如从 handle_agree_and_start 那里已经有了)
+                if not output_s_user_session_id:
                      output_s_user_session_id = f"user_{timestamp_str}_{random_val}"; user_identifier_for_logging = output_s_user_session_id
                 else:
-                    user_identifier_for_logging = output_s_user_session_id # 使用从用户信息处获得的ID
+                    user_identifier_for_logging = output_s_user_session_id
                 print(f"用户会话ID: {output_s_user_session_id}")
             print(f"开始/继续轮次 {output_s_run_no} (用户ID: {output_s_user_session_id}). 本轮共 {output_s_num_trials_this_run} 个试验。")
-        else: # 中途点击开始
+        else:
             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
 
-
-    # --- 获取下一个试验并更新UI的逻辑 ---
-    # ... (这部分逻辑与您上一版包含while循环尝试获取有效试验的代码基本相同) ...
-    # ... (确保所有 yield 和 return 都返回7个状态变量 + 11个UI更新) ...
-    # ... (并且在循环结束仍未找到 trial_info 时，也尝试保存剩余日志和全局历史) ...
     current_actual_trial_index_for_get_next = output_s_trial_idx
-    # (上一段 action_type == "record_choice" 已经处理了日志保存，所以这里主要处理 trial_info 获取)
-
+    
     if current_actual_trial_index_for_get_next >= output_s_num_trials_this_run and output_s_num_trials_this_run > 0:
-        # (这个条件实际上在 record_choice 后，或 start_experiment 的 is_completed_for_restart 后，output_s_trial_idx 可能已经等于或超过)
-        # (这里的逻辑主要是为了在 yield 返回前，如果恰好是轮次结束，则正确显示结束信息)
-        # (大部分轮次结束逻辑已移到 action_type == "record_choice" 和 "start_experiment" 的 is_completed_for_restart 部分)
-        print(f"调试: 在获取下一个试验前，检测到轮次 {output_s_run_no} 可能已结束 (idx: {current_actual_trial_index_for_get_next} >= num_trials: {output_s_num_trials_this_run})")
-        # (如果确实结束，应该已经在前面返回了，这里是一个保险或冗余检查)
         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”继续。", prog_text, stop_experiment=False)) # 完成一轮不立即停止
+        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。", prog_text, stop_experiment=False))
         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, {"data": None}, output_s_user_session_id, output_s_current_run_image_list, output_s_num_trials_this_run, *ui_updates; return
@@ -654,24 +600,20 @@ def process_experiment_step(
         
         if _trial_info_candidate is not None:
             trial_info = _trial_info_candidate
-            output_s_trial_idx = _returned_next_idx # 更新主状态的 trial_idx
+            output_s_trial_idx = _returned_next_idx
             break
         else:
             print(f"信息：目标图 '{current_target_image_for_trial}' 无法生成有效试验。尝试列表中的下一个。")
             next_s_trial_idx_for_state_loop +=1
             output_s_trial_idx = next_s_trial_idx_for_state_loop
-            # exhausted_target_images 应该在 get_next_trial_info 内部被更新
 
     if trial_info is None:
         print(f"轮次 {output_s_run_no} 中没有更多可用的有效试验了。结束本轮。")
-        # 尝试保存剩余日志
         if output_s_user_logs:
             print(f"轮次 {output_s_run_no} 无更多有效试验，尝试保存剩余 {len(output_s_user_logs)} 条日志...")
             batch_id_for_filename = f"run{output_s_run_no}_no_more_trials_logcount{len(output_s_user_logs)}"
             if not save_collected_logs_batch(list(output_s_user_logs), user_identifier_for_logging, batch_id_for_filename):
-                 # 即使这里保存失败，也应该让用户知道轮次结束了，但可能需要更强的错误提示
-                print("严重错误：保存剩余日志失败。���验可能需要停止。")
-                # 可以选择在这里返回一个停止实验的UI更新
+                print("严重错误：保存剩余日志失败。实验可能需要停止。")
             output_s_user_logs = []
         if global_history_has_unsaved_changes:
             print("轮次无更多有效试验，尝试保存全局图片对历史...")
@@ -697,17 +639,13 @@ def process_experiment_step(
     
     ui_show_candidates_updates = list(create_no_change_tuple())
     ui_show_candidates_updates[0]=gr.update(value=None,visible=False); ui_show_candidates_updates[1]=gr.update(value=trial_info["left_path"],visible=True); ui_show_candidates_updates[2]=gr.update(value=trial_info["right_path"],visible=True)
-    ui_show_candidates_updates[3]=gr.update(value=trial_info["left_display_label"], visible=True); ui_show_candidates_updates[4]=gr.update(value=trial_info["right_display_label"], visible=True) # 改回 True
+    ui_show_candidates_updates[3]=gr.update(value=trial_info["left_display_label"], visible=True); ui_show_candidates_updates[4]=gr.update(value=trial_info["right_display_label"], visible=True)
     ui_show_candidates_updates[5]="请选择更像原图的一张"; ui_show_candidates_updates[6]=prog_text
     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 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_show_candidates_updates
 
-# ==== Gradio UI 定义 和 程序入口 (与您上一版相同，包含下载按钮等) ====
-# ... (welcome_page_markdown, handle_agree_and_start, handle_download_history_file) ...
-# ... (with gr.Blocks(...) as demo: ...) ...
-# ... (if __name__ == "__main__": ...) ...
-# (此处省略这部分，因为它们与您包含下载按钮的上一版代码相同)
-def handle_download_history_file(): # 确保这个函数在UI定义之前
+# ==== Gradio UI 定义 和 程序入口 ====
+def handle_download_history_file():
     global GLOBAL_HISTORY_FILE
     if os.path.exists(GLOBAL_HISTORY_FILE):
         try:
@@ -785,21 +723,21 @@ with gr.Blocks(css=CSS, title="图像重建主观评估") as demo:
     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。")
         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", value="", visible=True, interactive=False, max_lines=1); btn_left = gr.Button("选择左图 (更相似)", interactive=False, elem_classes="compact_button") # left_lbl 改回 visible=True
-            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",value="", visible=True, interactive=False, max_lines=1); btn_right = gr.Button("选择右图 (更相似)", interactive=False, elem_classes="compact_button") # right_lbl 改回 visible=True
+            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", value="", visible=True, 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",value="", visible=True, 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() # 初始为空
+        with gr.Row(): progress_text = gr.Markdown()
         with gr.Row():
             btn_start = gr.Button("开始试验 / 下一轮")
             btn_download_json = gr.Button("下载JSON历史记录")
         json_download_output = gr.File(label="下载的文件会在此处提供", interactive=False)
-        file_out_placeholder = gr.File(label=" ", visible=False, interactive=False) # 保持这个占位符
+        file_out_placeholder = gr.File(label=" ", visible=False, interactive=False)
 
     outputs_ui_components_definition = [
         target_img, left_img, right_img, left_lbl, right_lbl, status_text, progress_text,
         btn_start, btn_left, btn_right, file_out_placeholder
-    ] # 确保这里是 11 个组件
+    ]
     click_inputs_base = [
         s_trial_index, s_run_no, s_user_logs, s_current_trial_data, s_user_session_id,
         s_current_run_image_list, s_num_trials_this_run
@@ -815,33 +753,13 @@ with gr.Blocks(css=CSS, title="图像重建主观评估") as demo:
     btn_right.click(fn=partial(process_experiment_step, action_type="record_choice", choice_value="right"), inputs=click_inputs_base, outputs=event_outputs, queue=True)
     btn_download_json.click(fn=handle_download_history_file, inputs=None, outputs=[json_download_output, status_text])
 
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
 if __name__ == "__main__":
-    # ... (您的 __main__ 部分代码保持不变，包含所有路径检查和 Gradio 启动) ...
     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���片。")
         if not METHOD_ROOTS: print(f"警告: '{BASE_IMAGE_DIR}' 无候选方法子目录。")
         if not SUBJECTS: print("警告: SUBJECTS 列表为空。")
-        print(f"用户选择日志保存到 Dataset: '{DATASET_REPO_ID}' 的 '{BATCH_LOG_FOLDER}/ 文件夹") # 修正日志文件夹描述
+        print(f"用户选择日志保存到 Dataset: '{DATASET_REPO_ID}' 的 '{BATCH_LOG_FOLDER}/ 文件夹")
         if not os.getenv("HF_TOKEN"): print("警告: HF_TOKEN 未设置。日志无法保存到Hugging Face Dataset。\n      请在 Space Secrets 中设置 HF_TOKEN。")
         else: print("HF_TOKEN 已找到。")
         print(f"全局图片对历史将从 '{GLOBAL_HISTORY_FILE}' 加载/保存到此文件。")
@@ -865,9 +783,9 @@ if __name__ == "__main__":
             except Exception as e_mkdir_main:
                 print(f"错误：在 main 中创建目录 '{PERSISTENT_STORAGE_BASE}' 失败: {e_mkdir_main}")
 
-        final_allowed_paths = list(set(allowed_paths_list)) # 去重
+        final_allowed_paths = list(set(allowed_paths_list))
         if final_allowed_paths:
-            print(f"Gradio `demo.launch()` 配置最终 allowed_paths: {final_allowed_paths}")
+            print(f"Gradio demo.launch() 配置最终 allowed_paths: {final_allowed_paths}")
         else:
             print("警告：没有有效的 allowed_paths 被配置。Gradio文件访问可能受限。")