import gradio as gr
import os
import random
import time
from datetime import datetime
from functools import partial
import json
import io
from huggingface_hub import HfApi
from huggingface_hub.hf_api import HfHubHTTPError
import traceback
from itertools import combinations

# ==== 全局配置 (部分保持不变) ====
# ... (BASE_IMAGE_DIR, TARGET_DIR, METHOD_ROOTS, SUBJECTS, etc. 保持不变) ...
BASE_IMAGE_DIR = "/data/images/images"
TARGET_DIR_BASENAME = "gt"
TARGET_DIR = os.path.join(BASE_IMAGE_DIR, TARGET_DIR_BASENAME)

METHOD_ROOTS = []
if os.path.exists(BASE_IMAGE_DIR):
    try:
        METHOD_ROOTS = [
            os.path.join(BASE_IMAGE_DIR, d)
            for d in os.listdir(BASE_IMAGE_DIR)
            if os.path.isdir(os.path.join(BASE_IMAGE_DIR, d)) and \
               d != TARGET_DIR_BASENAME and \
               not d.startswith('.')
        ]
        if not METHOD_ROOTS: print(f"警告：在 '{BASE_IMAGE_DIR}' 中没有找到有效的方法目录 (除了 '{TARGET_DIR_BASENAME}')。")
        else: print(f"已识别的方法根目录: {METHOD_ROOTS}")
    except Exception as e: print(f"错误：在扫描 '{BASE_IMAGE_DIR}' 时发生错误: {e}"); METHOD_ROOTS = []
else: print(f"警告：基础目录 '{BASE_IMAGE_DIR}' 不存在。将无法加载候选图片。")

SUBJECTS = ["subj01", "subj02", "subj05", "subj07"]
SENTINEL_TRIAL_INTERVAL = 20
NUM_TRIALS_PER_RUN = 100
LOG_BATCH_SIZE = 5

DATASET_REPO_ID = "YanmHa/image-aligned-experiment-data"
INDIVIDUAL_LOGS_FOLDER = "individual_choice_logs"
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; }"


# ==== 全局持久化历史记录 ====
DATA_SUBDIR_NAME = "experiment_data_storage"  # 子目录名

# 确保脚本有权限获取CWD并创建目录
# (在Hugging Face Spaces中，通常应用有权限在其工作目录下创建子目录)
base_path = os.getcwd() # 获取当前工作目录，例如 /home/user/app
full_subdir_path = os.path.join(base_path, DATA_SUBDIR_NAME)

if not os.path.exists(full_subdir_path):
    try:
        os.makedirs(full_subdir_path)
        print(f"成功创建子目录: {full_subdir_path}")
    except Exception as e:
        print(f"错误：创建子目录 '{full_subdir_path}' 失败: {e}")
        # 如果创建失败，这里应该有一个处理，比如退出或使用根目录作为后备
        # 为了简单起见，如果失败，后续路径仍然会尝试用这个subdir，可能会出错，或者您可以设置一个标志

GLOBAL_HISTORY_FILE = os.path.join(full_subdir_path, "global_experiment_shown_pairs.json")

# 检查子目录是否真的创建成功了
if not os.path.isdir(full_subdir_path):
     print(f"警告：子目录 '{full_subdir_path}' 未能成功创建或不是一个目录。全局历史文件将尝试保存到根目录。")
     GLOBAL_HISTORY_FILE = os.path.join(base_path, "global_experiment_shown_pairs.json") # 退回到之前的逻辑（根目录）

print(f"全局历史文件将被加载/保存到: {GLOBAL_HISTORY_FILE}") # 打印最终确定的绝对路径

global_shown_pairs_cache = {}
global_history_has_unsaved_changes = False

print(f"调试：检查 '{base_path}' (CWD) 的内容：")
try:
    for item in os.listdir(base_path):
        item_path_full = os.path.join(base_path, item)
        item_type = "DIR" if os.path.isdir(item_path_full) else "FILE"
        print(f"  - CWD Item: {item} ({item_type})")
except Exception as e:
    print(f"  错误：列出CWD '{base_path}' 内容失败: {e}")

print(f"调试：检查子目录 '{full_subdir_path}' 是否真的存在且为目录...")
if os.path.exists(full_subdir_path) and os.path.isdir(full_subdir_path):
    print(f"  确认：'{full_subdir_path}' 存在并且是一个目录。")
    print(f"  调试：'{full_subdir_path}' 的内容：")
    try:
        for item in os.listdir(full_subdir_path):
            item_path_in_subdir = os.path.join(full_subdir_path, item)
            item_type_in_subdir = "DIR" if os.path.isdir(item_path_in_subdir) else "FILE"
            print(f"    - Subdir Item: {item} ({item_type_in_subdir})")
    except Exception as e:
        print(f"    错误：列出子目录 '{full_subdir_path}' 内容失败: {e}")
else:
    print(f"  警告：'{full_subdir_path}' 不存在或不是一个目录 (检查结果)。")
    
def load_global_shown_pairs():
    global global_shown_pairs_cache, global_history_has_unsaved_changes
    if os.path.exists(GLOBAL_HISTORY_FILE):
        try:
            with open(GLOBAL_HISTORY_FILE, 'r', encoding='utf-8') as f:
                data_from_file = json.load(f)
                global_shown_pairs_cache = {
                    target_img: {frozenset(pair) for pair in pairs_list}
                    for target_img, pairs_list in data_from_file.items()
                }
            print(f"已成功从 '{GLOBAL_HISTORY_FILE}' 加载全局已展示图片对历史。")
        except Exception as e:
            print(f"错误：加载全局历史文件 '{GLOBAL_HISTORY_FILE}' 失败: {e}。将使用空历史记录。")
            global_shown_pairs_cache = {}
    else:
        print(f"信息：全局历史文件 '{GLOBAL_HISTORY_FILE}' 未找到。将创建新的空历史记录。")
        global_shown_pairs_cache = {}
    global_history_has_unsaved_changes = False # 初始化或加载后，标记为无未保存更改

def save_global_shown_pairs():
    global global_shown_pairs_cache, global_history_has_unsaved_changes # 确保可以修改这个flag
    print(f"--- save_global_shown_pairs --- 当前工作目录: {os.getcwd()}") # 调试信息
    print(f"--- save_global_shown_pairs --- 尝试保存到文件: {os.path.abspath(GLOBAL_HISTORY_FILE)}") # 调试信息
    # print("尝试保存全局图片对历史...") # 调试信息
    try:
        data_to_save = {
            target_img: [sorted(list(pair_fset)) for pair_fset in pairs_set]
            for target_img, pairs_set in global_shown_pairs_cache.items()
        }
        temp_file = GLOBAL_HISTORY_FILE + ".tmp"
        with open(temp_file, 'w', encoding='utf-8') as f:
            json.dump(data_to_save, f, ensure_ascii=False, indent=2)
        os.replace(temp_file, GLOBAL_HISTORY_FILE)
        print(f"已成功将全局已展示图片对历史保存到 '{GLOBAL_HISTORY_FILE}'。")
        global_history_has_unsaved_changes = False # 保存成功后重置标志
        return True
    except Exception as e:
        print(f"错误：保存全局历史文件 '{GLOBAL_HISTORY_FILE}' 失败: {e}")
        return False

load_global_shown_pairs()

# ==== 加载所有可用的目标图片 (保持不变) ====
master_image_list = []
if os.path.exists(TARGET_DIR):
    try:
        master_image_list = sorted(
            [f for f in os.listdir(TARGET_DIR) if f.lower().endswith((".jpg", ".png", ".jpeg"))],
            key=lambda x: int(os.path.splitext(x)[0])
        )
    except ValueError:
        master_image_list = sorted([f for f in os.listdir(TARGET_DIR) if f.lower().endswith((".jpg", ".png", ".jpeg"))])
        if master_image_list: print(f"警告： '{TARGET_DIR}' 文件名非纯数字，按字母排序。")
    if not master_image_list: print(f"警告：在 '{TARGET_DIR}' 中无有效图片。")
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):
    global TARGET_DIR, METHOD_ROOTS, SUBJECTS, SENTINEL_TRIAL_INTERVAL, global_shown_pairs_cache, global_history_has_unsaved_changes

    # ... (函数开始部分与上一版相同，直到哨兵试验逻辑结束) ...
    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]
    target_full_path = os.path.join(TARGET_DIR, img_filename_original)
    trial_number_for_display = current_trial_idx_in_run + 1

    pool = []
    for m_root_path in METHOD_ROOTS:
        method_name = os.path.basename(m_root_path)
        for s_id in SUBJECTS:
            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}"
                pool.append((internal_label, candidate_path))

    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,
                  "right_display_label": "N/A", "right_internal_label": "N/A", "right_path": None}

    is_potential_sentinel_trial = (trial_number_for_display > 0 and trial_number_for_display % SENTINEL_TRIAL_INTERVAL == 0)

    if is_potential_sentinel_trial:
        # 哨兵试验逻辑不变
        if not pool:
            print(f"警告：哨兵图 '{img_filename_original}' (trial {trial_number_for_display}) 无候选。")
        else:
            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)
            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])
            ]
            random.shuffle(candidates_for_sentinel)
            trial_info.update({
                "left_display_label": candidates_for_sentinel[0][0][0],
                "left_path": candidates_for_sentinel[0][0][1],
                "left_internal_label": candidates_for_sentinel[0][1],
                "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 len(pool) < 2:
            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。")
            return None, current_trial_idx_in_run

        target_global_history_set = global_shown_pairs_cache.setdefault(img_filename_original, set())
        all_possible_pairs_in_pool = []
        for c1, c2 in combinations(pool, 2):
            pair_labels_fset = frozenset({c1[0], c2[0]})
            all_possible_pairs_in_pool.append( ((c1, c2), pair_labels_fset) )

        unseen_globally_pairs_with_data = [
            item for item in all_possible_pairs_in_pool
            if item[1] not in target_global_history_set
        ]
        selected_candidates_tuples = None

        if unseen_globally_pairs_with_data:
            chosen_pair_data_and_labels = random.choice(unseen_globally_pairs_with_data)
            selected_candidates_tuples = chosen_pair_data_and_labels[0]
            chosen_pair_frozenset = chosen_pair_data_and_labels[1]
            target_global_history_set.add(chosen_pair_frozenset)
            global_history_has_unsaved_changes = True # <--- 标记全局历史已更新
            # 不再在此处调用 save_global_shown_pairs()
            # print(f"调试：目标 '{img_filename_original}': 新全局唯一对 {chosen_pair_frozenset} 已添加至缓存。未保存更改标志: {global_history_has_unsaved_changes}")
        else:
            # ... (处理所有对都已展示过的情况，与上一版相同) ...
            print(f"警告：目标图 '{img_filename_original}': 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:
                print("将随机选择一个重复的对（全局重复）。")
                chosen_pair_data_and_labels = random.choice(all_possible_pairs_in_pool)
                selected_candidates_tuples = chosen_pair_data_and_labels[0]
            else:
                print(f"错误：即使允许全局重复，也无法从池中选择图片对（池大小 {len(pool)}）。")
                return None, current_trial_idx_in_run

        display_order_candidates = list(selected_candidates_tuples)
        if random.random() > 0.5:
            display_order_candidates = display_order_candidates[::-1]

        trial_info.update({
            "left_display_label": "候选图 1", "left_path": display_order_candidates[0][1], "left_internal_label": display_order_candidates[0][0],
            "right_display_label": "候选图 2", "right_path": display_order_candidates[1][1], "right_internal_label": display_order_candidates[1][0],
        })
    return trial_info, current_trial_idx_in_run + 1

# ==== 批量保存用户选择日志函数 (保持不变) ====
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 # 认为无日志即成功

    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:
        token = os.getenv("HF_TOKEN")
        if not token: print("错误：HF_TOKEN 未设置。无法批量保存选择日志。"); return False
        if not DATASET_REPO_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)
        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}"
        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：批量保存选择日志时，条目非字典：{log_entry}")
            except Exception as json_err:
                print(f"错误：批量保存选择日志序列化单条时出错: {log_entry}. 错误: {json_err}")
                jsonl_content += json.dumps({"error": "serialization_failed_in_batch_user_log", "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} (批次 {batch_identifier}) 无可序列化选择日志。"); return True

        log_bytes = jsonl_content.encode('utf-8')
        file_like_object = io.BytesIO(log_bytes)
        print(f"准备批量上传选择日志文件: {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",
            commit_message=f"Batch user choice logs for {identifier_safe}, batch_id {batch_identifier} ({len(list_of_log_entries)} entries)"
        )
        print(f"批量选择日志已成功保存到 HF Dataset: {DATASET_REPO_ID}/{path_in_repo}")
        return True
    except Exception as e:
        print(f"批量保存选择日志 (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(
    s_trial_idx_val, s_run_no_val, s_user_logs_val, s_current_trial_data_val, s_user_session_id_val,
    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, LOG_BATCH_SIZE, global_history_has_unsaved_changes

    # ... (函数开始部分与上一版类似) ...
    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)
    output_s_num_trials_this_run = s_num_trials_this_run_val
    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

    len_ui_outputs = len(outputs_ui_components_definition)
    def create_ui_error_tuple(message, progress_msg_text): return (gr.update(visible=False),) * 3 + ("", "", message, progress_msg_text) + (gr.update(interactive=True), gr.update(interactive=False), gr.update(interactive=False)) + (gr.update(visible=False),)
    def create_no_change_tuple(): return (gr.update(),) * len_ui_outputs
    user_id_display_text = output_s_user_session_id if output_s_user_session_id else "用户ID待分配"


    if action_type == "record_choice":
        # ... (日志记录逻辑与上一版相同) ...
        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"])
            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:
                parts = chosen_internal_label.split('/');
                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 = {
                "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"],
                "right_internal_label": output_s_current_trial_data["data"]["right_internal_label"],
                "chosen_side": choice_value, "chosen_internal_label": chosen_internal_label,
                "chosen_method": parsed_chosen_method, "chosen_subject": parsed_chosen_subject, "chosen_filename": parsed_chosen_filename,
                "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)
            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)}")


            # !!! 修改：当用户日志达到批量大小时，同时尝试保存全局历史（如果它有更改）!!!
            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)}"
                
                # 1. 保存用户选择日志
                user_logs_save_success = save_collected_logs_batch(list(output_s_user_logs), user_identifier_for_logging, batch_id_for_filename)
                if user_logs_save_success:
                    print("批量用户选择日志已成功（或尝试）保存，将清空累积的用户选择日志列表。")
                    output_s_user_logs = [] # 清空已保存的用户日志
                else:
                    print("警告：批量用户选择日志保存失败。选择日志将继续累积，下次达到阈值时重试。")

                # 2. 检查并保存全局图片对历史（如果自上次保存后有更改）
                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...")
                    save_global_shown_pairs() # 此函数内部会在成功保存后将 global_history_has_unsaved_changes 置为 False
                else:
                    print("全局图片对历史自上次保存后无更新，无需保存。")
        else: # 处理记录选择时数据为空的错误
            print(f"用户 {user_identifier_for_logging} 错误：记录选择时数据为空！")
            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}")
            # 返回所有状态变量的当前值以及错误UI更新
            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 逻辑与上一版相同) ...
    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:
            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

            if is_completed_for_restart:
                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
            output_s_current_trial_data = {}
            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:
            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

    # ... (轮次结束处理与上一版相同) ...
    if output_s_trial_idx >= output_s_num_trials_this_run and output_s_num_trials_this_run > 0:
        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。")
        # 检查是否有未保存的全局历史，即使日志批次未满，也可能在轮次结束时考虑保存
        # 但当前逻辑是仅在日志批次满时保存全局历史，这里可以保持一致或添加额外逻辑
        if global_history_has_unsaved_changes:
             print(f"提示：轮次 {output_s_run_no} 结束，仍有未保存的全局图片对历史更改。将在下次日志批量保存时一并处理。")

        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”继续或开始新批次。", prog_text))
        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的逻辑，与上一版相同，调用 get_next_trial_info 不需要 user_id for history) ...
    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

    trial_info, next_s_trial_idx_for_state = get_next_trial_info(
        output_s_trial_idx,
        output_s_current_run_image_list,
        output_s_num_trials_this_run
    )

    if trial_info is None:
        print(f"错误：用户 {user_identifier_for_logging}，轮次 {output_s_run_no}，试验 {output_s_trial_idx}: get_next_trial_info 返回 None。")
        error_msg_display = "无法加载下一个试验，可能是因为候选图片不足或所有唯一组合已用尽。"
        if len(METHOD_ROOTS) * len(SUBJECTS) < 2 :
            error_msg_display = "候选图片来源不足，无法形成对比试验。"
        error_ui_updates = create_ui_error_tuple(error_msg_display, f"用户ID: {user_id_display_text} | 进度：{output_s_trial_idx}/{output_s_num_trials_this_run}")
        output_s_current_trial_data = {"data": None}
        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

    output_s_current_trial_data = {"data": trial_info}
    prog_text = f"用户ID: {output_s_user_session_id} | 进度：{trial_info['cur_no']}/{output_s_num_trials_this_run} | 第 {output_s_run_no} 轮"
    ui_show_target_updates = list(create_no_change_tuple())
    ui_show_target_updates[0]=gr.update(value=trial_info["target_path"],visible=True); ui_show_target_updates[1]=gr.update(value=None,visible=False); ui_show_target_updates[2]=gr.update(value=None,visible=False)
    ui_show_target_updates[3]=""; ui_show_target_updates[4]=""; ui_show_target_updates[5]="请观察原图…"; ui_show_target_updates[6]=prog_text
    ui_show_target_updates[7]=gr.update(interactive=False); ui_show_target_updates[8]=gr.update(interactive=False); ui_show_target_updates[9]=gr.update(interactive=False)
    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_target_updates
    time.sleep(3)
    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=False); ui_show_candidates_updates[4]=gr.update(value=trial_info["right_display_label"], visible=False)
    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 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 和 程序入口 (保持不变) ====
# ... (welcome_page_markdown, handle_agree_and_start, gr.Blocks, if __name__ == "__main__": etc.) ...
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。
    * 系统先展示一张 **目标图像**，持续 3 秒。
    * 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):
    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。")
    if age is None: error_messages_list.append("年龄 不能为空。")
    elif not (isinstance(age, (int, float)) and 1 <= age <= 120):
        try: num_age = float(age);
        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() == "其他": error_messages_list.append("学历 必须选择。")
    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)))
    s_education = str(education).strip().replace(" ","_").replace("/","_").replace("\\","_")
    user_id_str = f"N-{s_name}_G-{s_gender}_A-{s_age}_E-{s_education}"
    print(f"用户信息收集完毕，生成用户ID: {user_id_str}")
    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)
    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:
        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 张三 或 User001)", placeholder="例如：张三 -> ZS"); user_gender_input = gr.Radio(label="性别", choices=["男", "女"])
        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); user_education_input = gr.Dropdown(label="学历", choices=["其他","初中及以下","高中（含中专）", "大专（含在读）", "本科（含在读）", "硕士（含在读）", "博士（含在读）"])
        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。")
        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.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()
        with gr.Row(): btn_start = gr.Button("开始试验 / 下一轮")
        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
    ]
    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
    ]
    event_outputs = [
        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, *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_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)

if __name__ == "__main__":
    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。")
        if not METHOD_ROOTS: print(f"警告: '{BASE_IMAGE_DIR}' 无候选方法子目录。")
        else: print(f"方法根目录: {METHOD_ROOTS}")
        if not SUBJECTS: print("警告: SUBJECTS 列表为空。")
        else: print(f"Subjects: {SUBJECTS}")
        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。")

        path_to_allow_serving_from = BASE_IMAGE_DIR
        allowed_paths_list = []
        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}' ({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()