import os import sys import glob import json import random import re from functools import partial from datetime import datetime from collections import defaultdict, Counter import gradio as gr from loguru import logger # --- Global State (unchanged) --- # --- Global State (unchanged) --- GLOBAL_STATE = { "participant_id": None, "data_loaded": False, "all_eval_data": [], "shuffled_indices": [], "current_prompt_index": 0, "current_criterion_index": 0, "image_mapping": {}, "image_dir": "", "evaluation_results": {}, "image_orders": {}, "start_time": None, "end_time": None, "current_ranks": {}, "current_absolute_score": None, # ▼▼▼ 追加 ▼▼▼ "current_absolute_score_worst": None, } # --- Configuration (unchanged) --- BASE_RESULTS_DIR = "./results" LOG_DIR = "./logs" COMBINED_DATA_DIR = "./combined_data" IMAGE_SUBDIR = os.path.join("lapwing", "images") MAPPING_FILENAME = "combination_to_filename.json" CONDITIONS = ["Ours", "w_o_Proto_Loss", "w_o_HitL", "w_o_Tuning", "LLM-based"] CRITERIA = ["Alignment", "Naturalness", "Attractiveness"] CRITERIA_GUIDANCE_JP = [ "テキストと表情がどれだけ一致しているか", "テキストの感情に沿ったセリフを言っていると想像したとき、表情がどれだけ自然か", "テキストの感情に沿ったセリフを言っていると想像したとき、表情がどれだけ魅力的か" ] CRITERIA_GUIDANCE_EN = [ "how well the expression aligns with the text", "imagining the character is speaking a line that matches the emotion of the text, how natural the facial expression is", "imagining the character is speaking a line that matches the emotion of the text, how attractive the facial expression is" ] IMAGE_LABELS = ['A', 'B', 'C', 'D', 'E'] # --- Helper Functions --- def get_image_path_from_prediction(prediction: dict) -> str: if not GLOBAL_STATE["image_mapping"]: logger.error("Image mapping is not loaded.") return "" indices = prediction.get("blendshape_index", {}) if not isinstance(indices, dict): logger.error(f"blendshape_index is not a dictionary: {indices}") return "" sorted_indices = sorted(indices.items(), key=lambda item: int(item[0])) key = ",".join(str(idx) for _, idx in sorted_indices) filename = GLOBAL_STATE["image_mapping"].get(key) if not filename: logger.warning(f"No image found for blendshape key: {key}") return "" return os.path.join(GLOBAL_STATE["image_dir"], filename) # ▼▼▼ 2. prompt_categoryを読み込むように修正 ▼▼▼ def load_evaluation_data(participant_id: str): mapping_path = os.path.join(COMBINED_DATA_DIR, MAPPING_FILENAME) if not os.path.exists(mapping_path): return f"

Error: Mapping file not found at {mapping_path}

", gr.update( interactive=True), gr.update(interactive=False) with open(mapping_path, 'r', encoding='utf-8') as f: GLOBAL_STATE["image_mapping"] = json.load(f)["mapping"] GLOBAL_STATE["image_dir"] = os.path.join(COMBINED_DATA_DIR, IMAGE_SUBDIR) logger.info(f"Successfully loaded image mapping. Image directory: {GLOBAL_STATE['image_dir']}") participant_dir = os.path.join(BASE_RESULTS_DIR, participant_id) if not os.path.isdir(participant_dir): return f"

Error: Participant directory not found: {participant_dir}

", gr.update( interactive=True), gr.update(interactive=False) merged_data = defaultdict(lambda: {"predictions": {}, "category": None}) found_files = 0 for cond in CONDITIONS: cond_dir = os.path.join(participant_dir, cond) pattern = os.path.join(cond_dir, f"{participant_id}_{cond}_*.jsonl") files = glob.glob(pattern) if not files: logger.warning(f"No prediction file found for condition '{cond}' with pattern: {pattern}") continue found_files += 1 with open(files[0], 'r', encoding='utf-8') as f: for line in f: data = json.loads(line) prompt = data["text_prompt"] merged_data[prompt]["predictions"][cond] = data["prediction"] if not merged_data[prompt]["category"]: merged_data[prompt]["category"] = data.get("prompt_category") if found_files != len(CONDITIONS): return f"

Error: Found prediction files for only {found_files}/{len(CONDITIONS)} conditions.

", gr.update( interactive=True), gr.update(interactive=False) GLOBAL_STATE["all_eval_data"] = [ {"prompt": p, "predictions": d["predictions"], "category": d["category"]} for p, d in merged_data.items() if len(d["predictions"]) == len(CONDITIONS) ] # ▲▲▲ END OF UPDATE ▲▲▲ if not GLOBAL_STATE["all_eval_data"]: return "

Error: No valid evaluation data could be loaded.

", gr.update( interactive=True), gr.update(interactive=False) GLOBAL_STATE["shuffled_indices"] = list(range(len(GLOBAL_STATE["all_eval_data"]))) random.shuffle(GLOBAL_STATE["shuffled_indices"]) GLOBAL_STATE["current_prompt_index"] = 0 GLOBAL_STATE["current_criterion_index"] = 0 GLOBAL_STATE["data_loaded"] = True GLOBAL_STATE["start_time"] = datetime.now() for i in range(len(GLOBAL_STATE["all_eval_data"])): prompt_text = GLOBAL_STATE["all_eval_data"][i]["prompt"] GLOBAL_STATE["evaluation_results"][prompt_text] = {} logger.info(f"Loaded and merged data for {len(GLOBAL_STATE['all_eval_data'])} prompts.") done_msg = "

Data loaded successfully. Please proceed to the 'Evaluation' tab. / データの読み込みに成功しました。「評価」タブに進んでください。

" return done_msg, gr.update(interactive=False, visible=False), gr.update(interactive=True) # --- Core Logic --- def _create_button_updates(): updates = [] for img_label in IMAGE_LABELS: selected_rank = GLOBAL_STATE["current_ranks"].get(img_label) for rank_val in range(1, 6): if rank_val == selected_rank: updates.append(gr.update(variant='primary')) else: updates.append(gr.update(variant='secondary')) return updates def handle_rank_button_click(image_label, rank): if GLOBAL_STATE["current_ranks"].get(image_label) == rank: GLOBAL_STATE["current_ranks"][image_label] = None else: GLOBAL_STATE["current_ranks"][image_label] = rank return _create_button_updates() def handle_absolute_score_click(score): if GLOBAL_STATE["current_absolute_score"] == score: GLOBAL_STATE["current_absolute_score"] = None else: GLOBAL_STATE["current_absolute_score"] = score updates = [] for i in range(1, 8): if i == GLOBAL_STATE["current_absolute_score"]: updates.append(gr.update(variant='primary')) else: updates.append(gr.update(variant='secondary')) return updates # ▼▼▼ 追加 ▼▼▼ def handle_absolute_score_worst_click(score): if GLOBAL_STATE["current_absolute_score_worst"] == score: GLOBAL_STATE["current_absolute_score_worst"] = None else: GLOBAL_STATE["current_absolute_score_worst"] = score updates = [] for i in range(1, 8): if i == GLOBAL_STATE["current_absolute_score_worst"]: updates.append(gr.update(variant='primary')) else: updates.append(gr.update(variant='secondary')) return updates # ▼▼▼ 1. UIフリーズ問題を修正 ▼▼▼ # ▼▼▼ 修正後の display_current_prompt_and_criterion 関数 ▼▼▼ def display_current_prompt_and_criterion(): if not GLOBAL_STATE["data_loaded"] or GLOBAL_STATE["current_prompt_index"] >= len(GLOBAL_STATE["all_eval_data"]): done_msg = "

All prompts have been evaluated! Please proceed to the 'Export' tab.
すべてのプロンプトの評価が完了しました！「エクスポート」タブに進んでください。

" empty_button_updates = [gr.update(variant='secondary')] * 25 empty_abs_updates = [gr.update(variant='secondary')] * 7 return [ gr.update(value="Finished! / 完了!"), gr.update(value=""), gr.update(value=done_msg), gr.update(value="", visible=False), *[gr.update(value=None)] * 5, *empty_button_updates, gr.update(visible=False), # abs_group_best *empty_abs_updates, gr.update(visible=False), # abs_group_worst *empty_abs_updates, gr.update(interactive=False), gr.update(interactive=False) ] prompt_idx = GLOBAL_STATE["shuffled_indices"][GLOBAL_STATE["current_prompt_index"]] criterion_idx = GLOBAL_STATE["current_criterion_index"] current_data = GLOBAL_STATE["all_eval_data"][prompt_idx] prompt_text = current_data["prompt"] criterion_name = CRITERIA[criterion_idx] progress_text = f"Prompt {GLOBAL_STATE['current_prompt_index'] + 1} / {len(GLOBAL_STATE['all_eval_data'])} - **{criterion_name}**" prompt_display_text = f"## \"{prompt_text}\"" guidance_text = f"### Please rank the 5 images based on **{CRITERIA_GUIDANCE_EN[criterion_idx]}**.
5つの画像を、**「{CRITERIA_GUIDANCE_JP[criterion_idx]}」**を基準にランキング付けしてください。" if criterion_idx == 0: GLOBAL_STATE["image_orders"] = {} if criterion_name not in GLOBAL_STATE["image_orders"]: conditions_shuffled = random.sample(CONDITIONS, len(CONDITIONS)) GLOBAL_STATE["image_orders"][criterion_name] = conditions_shuffled current_image_order = GLOBAL_STATE["image_orders"][criterion_name] image_updates = [] for cond_name in current_image_order: prediction = current_data["predictions"][cond_name] img_path = get_image_path_from_prediction(prediction) image_updates.append(gr.update(value=img_path if img_path and os.path.exists(img_path) else None)) saved_ranks_dict = GLOBAL_STATE["evaluation_results"].get(prompt_text, {}).get("ranks", {}).get(criterion_name) if saved_ranks_dict: label_to_condition = {label: cond for label, cond in zip(IMAGE_LABELS, current_image_order)} condition_to_label = {v: k for k, v in label_to_condition.items()} GLOBAL_STATE["current_ranks"] = { condition_to_label[cond]: rank for cond, rank in saved_ranks_dict.items() if cond in condition_to_label } else: GLOBAL_STATE["current_ranks"] = {label: None for label in IMAGE_LABELS} button_updates = _create_button_updates() # --- Absolute Score (Best) --- is_alignment_criterion = (criterion_name == "Alignment") abs_group_update = gr.update(visible=is_alignment_criterion) saved_abs_score = GLOBAL_STATE["evaluation_results"].get(prompt_text, {}).get("absolute_score") GLOBAL_STATE["current_absolute_score"] = saved_abs_score if is_alignment_criterion else None abs_button_updates = [] for i in range(1, 8): variant = 'primary' if i == GLOBAL_STATE["current_absolute_score"] else 'secondary' abs_button_updates.append(gr.update(variant=variant)) # --- Absolute Score (Worst) --- abs_group_worst_update = gr.update(visible=is_alignment_criterion) saved_abs_score_worst = GLOBAL_STATE["evaluation_results"].get(prompt_text, {}).get("absolute_score_worst") GLOBAL_STATE["current_absolute_score_worst"] = saved_abs_score_worst if is_alignment_criterion else None abs_button_worst_updates = [] for i in range(1, 8): variant = 'primary' if i == GLOBAL_STATE["current_absolute_score_worst"] else 'secondary' abs_button_worst_updates.append(gr.update(variant=variant)) return [ gr.update(value=progress_text), gr.update(value=prompt_display_text), gr.update(value=guidance_text), gr.update(value="", visible=False), *image_updates, *button_updates, abs_group_update, *abs_button_updates, abs_group_worst_update, *abs_button_worst_updates, gr.update( interactive=(GLOBAL_STATE["current_prompt_index"] > 0 or GLOBAL_STATE["current_criterion_index"] > 0)), gr.update(interactive=True) ] # ▼▼▼ 修正後の validate_and_navigate 関数 ▼▼▼ def validate_and_navigate(): ranks = GLOBAL_STATE["current_ranks"] error_msg = None criterion_name = CRITERIA[GLOBAL_STATE["current_criterion_index"]] is_alignment_criterion = (criterion_name == "Alignment") # --- Validation --- if any(r is None for r in ranks.values()): error_msg = "Please rank all 5 images. / 5つすべての画像を評価してください。" elif 1 not in ranks.values(): error_msg = "You must assign a rank of '1' to at least one image. / 最低1つは「1位」を付けてください。" elif is_alignment_criterion and GLOBAL_STATE["current_absolute_score"] is None: error_msg = "Please provide an absolute score for the BEST matching image (1-7). / 最も一致している画像について、絶対評価（1～7）を選択してください。" elif is_alignment_criterion and GLOBAL_STATE["current_absolute_score_worst"] is None: error_msg = "Please provide an absolute score for the WORST matching image (1-7). / 最も一致していない画像について、絶対評価（1～7）を選択してください。" # ▼▼▼ 変更箇所 ここから ▼▼▼ elif ( is_alignment_criterion and GLOBAL_STATE["current_absolute_score"] is not None and GLOBAL_STATE["current_absolute_score_worst"] is not None and GLOBAL_STATE["current_absolute_score_worst"] > GLOBAL_STATE["current_absolute_score"] ): error_msg = ( "The score for the WORST matching image cannot be higher than the score for the BEST matching image.
" "「最も一致していない画像」のスコアが「最も一致している画像」のスコアを上回ることはできません。" ) # ▲▲▲ 変更箇所 ここまで ▲▲▲ if error_msg: # The number of components to update is now 53 (1 tab + 52 eval components) no_change_updates = [gr.update()] * 53 no_change_updates[4] = gr.update( # error_display is the 5th component (index 4) value=f"

{error_msg}

", visible=True) return no_change_updates # ... (Rank tie-breaking validation logic is unchanged) ... sorted_ranks = sorted(list(ranks.values())) rank_counts = Counter(sorted_ranks) i = 0 while i < len(sorted_ranks): current_rank = sorted_ranks[i] count = rank_counts[current_rank] if i + count < len(sorted_ranks): next_rank = sorted_ranks[i + count] expected_next_rank = current_rank + count if next_rank < expected_next_rank: error_msg = f"Ranking rule violation (tie-breaking). After {count} instance(s) of rank '{current_rank}', the next rank must be >= {expected_next_rank}, but it is '{next_rank}'. / 順位付けのルール違反です。'{current_rank}'位が{count}つあるため、次の順位は{expected_next_rank}位以上である必要がありますが、'{next_rank}'位が入力されています。" break i += count if error_msg: no_change_updates = [gr.update()] * 53 no_change_updates[4] = gr.update( value=f"

{error_msg}

", visible=True) return no_change_updates # --- End of Validation --- prompt_idx = GLOBAL_STATE["shuffled_indices"][GLOBAL_STATE["current_prompt_index"]] current_data = GLOBAL_STATE["all_eval_data"][prompt_idx] prompt_text = current_data["prompt"] current_image_order = GLOBAL_STATE["image_orders"][criterion_name] label_to_condition = {label: cond for label, cond in zip(IMAGE_LABELS, current_image_order)} ranks_by_condition = {label_to_condition[label]: rank for label, rank in ranks.items()} if "ranks" not in GLOBAL_STATE["evaluation_results"][prompt_text]: GLOBAL_STATE["evaluation_results"][prompt_text]["ranks"] = {} if "orders" not in GLOBAL_STATE["evaluation_results"][prompt_text]: GLOBAL_STATE["evaluation_results"][prompt_text]["orders"] = {} GLOBAL_STATE["evaluation_results"][prompt_text]["ranks"][criterion_name] = ranks_by_condition GLOBAL_STATE["evaluation_results"][prompt_text]["orders"][criterion_name] = current_image_order if is_alignment_criterion: GLOBAL_STATE["evaluation_results"][prompt_text]["absolute_score"] = GLOBAL_STATE["current_absolute_score"] GLOBAL_STATE["evaluation_results"][prompt_text]["absolute_score_worst"] = GLOBAL_STATE[ "current_absolute_score_worst"] logger.info( f"Saved rank for P:{GLOBAL_STATE['participant_id']}, Prompt:'{prompt_text}', Criterion:{criterion_name}, Ranks:{ranks_by_condition}") GLOBAL_STATE["current_criterion_index"] += 1 if GLOBAL_STATE["current_criterion_index"] >= len(CRITERIA): GLOBAL_STATE["current_criterion_index"] = 0 GLOBAL_STATE["current_prompt_index"] += 1 if GLOBAL_STATE["current_prompt_index"] >= len(GLOBAL_STATE["all_eval_data"]): GLOBAL_STATE["end_time"] = datetime.now() eval_panel_updates = display_current_prompt_and_criterion() # Activate export tab on completion return [gr.update(interactive=True)] + eval_panel_updates else: # Keep export tab state as is return [gr.update()] + display_current_prompt_and_criterion() def navigate_previous(): GLOBAL_STATE["current_criterion_index"] -= 1 if GLOBAL_STATE["current_criterion_index"] < 0: GLOBAL_STATE["current_criterion_index"] = len(CRITERIA) - 1 GLOBAL_STATE["current_prompt_index"] -= 1 GLOBAL_STATE["current_prompt_index"] = max(0, GLOBAL_STATE["current_prompt_index"]) return display_current_prompt_and_criterion() # ▼▼▼ 修正後の export_results 関数 ▼▼▼ def export_results(participant_id, alignment_reason, naturalness_reason, attractiveness_reason, optional_comment): if not alignment_reason.strip() or not naturalness_reason.strip() or not attractiveness_reason.strip(): error_msg = "

Please fill in the reasoning for all three criteria (Alignment, Naturalness, Attractiveness). / 3つの評価基準（一致度, 自然さ, 魅力度）すべての判断理由を記入してください。

" return None, error_msg if not participant_id: return None, "

Participant ID is missing. / 参加者IDがありません。

" output_dir = os.path.join(BASE_RESULTS_DIR, participant_id) os.makedirs(output_dir, exist_ok=True) filename = f"evaluation_results_{participant_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" filepath = os.path.join(output_dir, filename) duration = (GLOBAL_STATE["end_time"] - GLOBAL_STATE["start_time"]).total_seconds() if GLOBAL_STATE.get( "start_time") and GLOBAL_STATE.get("end_time") else None prompt_to_category = {item["prompt"]: item["category"] for item in GLOBAL_STATE["all_eval_data"]} final_results_list = [] for prompt, data in GLOBAL_STATE["evaluation_results"].items(): if not data: continue ranks_data = data.get("ranks", {}) orders_data = data.get("orders", {}) final_results_list.append({ "prompt": prompt, "prompt_category": prompt_to_category.get(prompt), "image_order_alignment": orders_data.get("Alignment", []), "image_order_naturalness": orders_data.get("Naturalness", []), "image_order_attractiveness": orders_data.get("Attractiveness", []), "alignment_ranks": ranks_data.get("Alignment", {}), "naturalness_ranks": ranks_data.get("Naturalness", {}), "attractiveness_ranks": ranks_data.get("Attractiveness", {}), "alignment_absolute_score": data.get("absolute_score"), # ▼▼▼ 追加 ▼▼▼ "alignment_absolute_score_worst": data.get("absolute_score_worst") }) export_data = { "metadata": { "participant_id": participant_id, "export_timestamp": datetime.now().isoformat(), "total_prompts_evaluated": len(final_results_list), "evaluation_duration_seconds": duration, "reasoning": { "alignment": alignment_reason, "naturalness": naturalness_reason, "attractiveness": attractiveness_reason, }, "optional_comment": optional_comment, }, "results": final_results_list } try: with open(filepath, 'w', encoding='utf-8') as f: json.dump(export_data, f, ensure_ascii=False, indent=2) logger.info(f"Successfully exported results to: {filepath}") except Exception as e: logger.error(f"Failed to write export file: {e}") return None, f"

An error occurred during file export: {e}

" upload_link = "https://drive.google.com/drive/folders/1ujIPF-67Y6OG8qBm1TYG3FsmuYxqSAcR?usp=drive_link" status_message = f"""

エクスポートが完了しました。/ Export complete.

上のボタンからJSONファイルをダウンロードし、指定された場所にアップロードして実験を終了してください。ご協力ありがとうございました。

Please download the JSON file and upload it to the designated location. Thank you for your cooperation.

アップロード先 / Upload to: {upload_link}

""" return gr.update(value=filepath, visible=True), status_message ## ▼▼▼ 修正後の create_gradio_interface 関数 ▼▼▼ def create_gradio_interface(): css = """ .gradio-container { font-family: 'Arial', sans-serif; } .feedback { padding: 10px; border-radius: 5px; font-weight: bold; text-align: center; margin-top: 10px; } .feedback.green { background-color: #e6ffed; color: #2f6f4a; } .feedback.red { background-color: #ffe6e6; color: #b30000; } .image-label { font-size: 2.5em; font-weight: bold; margin-bottom: 10px; color: #333; } .prompt-display { text-align: center; margin-bottom: 5px; padding: 15px; background-color: #f0f8ff; border-radius: 8px; } .prompt-sub-guidance { text-align: center; font-size: 0.9em; color: #555; margin-top: 5px; margin-bottom: 15px; } .rank-instruction { color: #D32F2F; font-size: 1.1em; text-align: left; margin-bottom: 20px; padding: 15px; border: 1px solid #f5c6cb; border-radius: 8px; background-color: #f8d7da; line-height: 1.6; } .rank-instruction ul { padding-left: 20px; margin: 0; } .rank-guidance { text-align: center; margin-bottom: 10px; font-size: 1.2em; } .rank-btn-row { justify-content: center; gap: 5px !important; } .rank-btn { min-width: 65px !important; max-width: 65px !important; height: 45px !important; font-size: 1.2em !important; font-weight: bold !important; border-radius: 8px !important; border: 1px solid #ccc !important; } .rank-btn.secondary { background: #f0f0f0 !important; color: #333 !important; } .rank-btn.secondary:hover { background: #e0e0e0 !important; border-color: #bbb !important; } .absolute-eval-group { border: 1px solid #ddd; border-radius: 8px; padding: 15px; margin-top: 20px; } """ with gr.Blocks(title="Expression Evaluation Experiment", css=css) as app: gr.Markdown("# Text-to-Expression Evaluation Experiment / テキストからの表情生成 評価実験") with gr.Tabs() as tabs: with gr.TabItem("1. Setup / セットアップ") as tab_setup: gr.Markdown("## (A) Participant Information / 参加者情報") gr.Markdown("Please enter your participant ID and click 'Confirm'. / 参加者IDを入力して「確定」を押してください。") with gr.Row(): participant_id_input = gr.Textbox(label="Participant ID", placeholder="e.g., P01") confirm_id_btn = gr.Button("Confirm / 確定", variant="primary") setup_warning = gr.Markdown(visible=False) with gr.Group(visible=False) as setup_main_group: gr.Markdown("---") gr.Markdown("## (B) Instructions & Data Loading / 注意事項とデータ読み込み") gr.Markdown( """

注意事項 / Instructions

""") gr.Markdown( "Click the button below to load your evaluation data. / 下のボタンを押して、評価データを読み込んでください。") load_data_btn = gr.Button("Load Data / データ読み込み", variant="primary") setup_status = gr.Markdown("Waiting to start...") with gr.TabItem("2. Evaluation / 評価", interactive=False) as tab_evaluation: progress_text = gr.Markdown("Prompt 0 / 0") image_components = [] rank_buttons = [] with gr.Row(equal_height=False): for label in IMAGE_LABELS: with gr.Column(scale=1): with gr.Group(): gr.Markdown(f"
{label}
") img = gr.Image(type="filepath", show_label=False, height=300) image_components.append(img) with gr.Row(elem_classes="rank-btn-row"): rank_list = ["1位", "2位", "3位", "4位", "5位"] for rank_val in range(1, 6): btn = gr.Button(str(rank_list[rank_val-1]), variant='secondary', elem_classes="rank-btn") rank_buttons.append(btn) prompt_display = gr.Markdown("## \"Prompt Text Here\"", elem_classes="prompt-display") gr.Markdown( "

You may use AI or web search for the meaning of the text. However, please do not ask an AI about the emotion of the image itself.
意味についてはAIに聞いたりネット検索しても構いません。ただし、画像そのものの感情をAIに尋ねるのを止めてください。

") guidance_display = gr.Markdown("### Guidance", elem_classes="rank-guidance") error_display = gr.Markdown(visible=False) gr.Markdown( """ ランキングの付け方 / How to Rank: """, elem_classes="rank-instruction" ) # ▼▼▼ 修正: 絶対評価（Best）のUI ▼▼▼ with gr.Group(visible=False, elem_classes="absolute-eval-group") as absolute_eval_group_best: gr.Markdown("---") gr.Markdown( "#### 絶対評価 (Best) / Absolute Score (Best)\n最もテキストと一致している画像について、どのていど一致しているかを評価してください。\n(Please evaluate the degree of alignment for the image that **best** matches the text.)") absolute_score_buttons = [] with gr.Row(): with gr.Column(scale=1): gr.Markdown( "

1 (全く一致してない / Not at all)

") with gr.Column(scale=3): with gr.Row(elem_classes="rank-btn-row"): for i in range(1, 8): btn = gr.Button(str(i), variant='secondary', elem_classes="rank-btn") absolute_score_buttons.append(btn) with gr.Column(scale=1): gr.Markdown("

7 (完全に一致 / Absolutely)

") # ▼▼▼ 追加: 絶対評価（Worst）のUI ▼▼▼ with gr.Group(visible=False, elem_classes="absolute-eval-group") as absolute_eval_group_worst: gr.Markdown( "#### 絶対評価 (Worst) / Absolute Score (Worst)\n最もテキストと一致していない画像について、どのていど一致していないかを評価してください。\n(Please evaluate the degree of alignment for the image that **least** matches the text.)") absolute_score_worst_buttons = [] with gr.Row(): with gr.Column(scale=1): gr.Markdown( "

1 (全く一致してない / Not at all)

") with gr.Column(scale=3): with gr.Row(elem_classes="rank-btn-row"): for i in range(1, 8): btn = gr.Button(str(i), variant='secondary', elem_classes="rank-btn") absolute_score_worst_buttons.append(btn) with gr.Column(scale=1): gr.Markdown("

7 (完全に一致 / Absolutely)

") with gr.Row(): prev_btn = gr.Button("← Previous / 前へ", interactive=False) next_btn = gr.Button("Save & Next / 保存して次へ →", variant="primary") with gr.TabItem("3. Export / エクスポート", interactive=False) as tab_export: gr.Markdown("## (C) Final Comments & Export / 最終コメントとエクスポート") gr.Markdown( "Thank you for completing the evaluation. Please provide the reasoning for your judgments for each criterion below. / 評価お疲れ様でした。以下の各評価基準について、判断の理由をご記入ください。") with gr.Group(): gr.Markdown("#### Reasoning for Judgments (Required) / 判断理由（必須）") alignment_reason_box = gr.Textbox(label="Alignment / 一致度", lines=3, placeholder="Why did you rank them this way for alignment? / なぜ一致度について、このような順位付けをしましたか？") naturalness_reason_box = gr.Textbox(label="Naturalness / 自然さ", lines=3, placeholder="Why did you rank them this way for naturalness? / なぜ自然さについて、このような順位付けをしましたか？") attractiveness_reason_box = gr.Textbox(label="Attractiveness / 魅力度", lines=3, placeholder="Why did you rank them this way for attractiveness? / なぜ魅力度について、このような順位付けをしましたか？") with gr.Group(): gr.Markdown("#### Overall Comments (Optional) / 全体的な感想（任意）") optional_comment_box = gr.Textbox(label="Any other comments? / その他、実験全体に関するご意見・ご感想", lines=4, placeholder="e.g., 'Image B often looked the most natural.' / 例：「Bの画像が最も自然に見えることが多かったです。」") gr.Markdown("---") gr.Markdown( "Finally, click the button below to export your results. / 最後に、下のボタンを押して結果をエクスポートしてください。") export_btn = gr.Button("Export Results / 結果をエクスポート", variant="primary") download_file = gr.File(label="Download JSON", visible=False) export_status = gr.Markdown() # --- Event Handlers --- def check_and_confirm_id(pid): pid = pid.strip() if re.fullmatch(r"P\d{2}", pid): GLOBAL_STATE["participant_id"] = pid return gr.update(visible=False), gr.update(visible=True) else: error_msg = "

Invalid ID. Must be 'P' followed by two digits (e.g., P01). / 無効なIDです。「P」と数字2桁の形式（例: P01）で入力してください。

" return gr.update(value=error_msg, visible=True), gr.update(visible=False) confirm_id_btn.click(check_and_confirm_id, [participant_id_input], [setup_warning, setup_main_group]) load_data_btn.click(load_evaluation_data, [participant_id_input], [setup_status, load_data_btn, tab_evaluation]) # ▼▼▼ 修正: all_eval_outputs に新しいUIコンポーネントを追加 ▼▼▼ all_eval_outputs = [ progress_text, prompt_display, guidance_display, error_display, *image_components, *rank_buttons, absolute_eval_group_best, *absolute_score_buttons, absolute_eval_group_worst, *absolute_score_worst_buttons, prev_btn, next_btn ] btn_idx = 0 for label in IMAGE_LABELS: for rank_val in range(1, 6): btn = rank_buttons[btn_idx] btn.click( partial(handle_rank_button_click, label, rank_val), [], rank_buttons ) btn_idx += 1 for i, btn in enumerate(absolute_score_buttons): btn.click( partial(handle_absolute_score_click, i + 1), [], absolute_score_buttons ) # ▼▼▼ 追加: 新しいボタンのイベントハンドラを接続 ▼▼▼ for i, btn in enumerate(absolute_score_worst_buttons): btn.click( partial(handle_absolute_score_worst_click, i + 1), [], absolute_score_worst_buttons ) tab_evaluation.select(display_current_prompt_and_criterion, [], all_eval_outputs) # ▼▼▼ 修正: next_btn の出力に tab_export を追加 ▼▼▼ next_btn.click(validate_and_navigate, [], [tab_export, *all_eval_outputs]) prev_btn.click(navigate_previous, [], all_eval_outputs) export_tab_interactive_components = [alignment_reason_box, naturalness_reason_box, attractiveness_reason_box, optional_comment_box, export_btn] def on_select_export_tab(): # end_time is set only when all evaluations are complete if GLOBAL_STATE.get("end_time"): return [gr.update(interactive=True)] * 5 # This logic is now handled by next_btn click, but kept as a fallback. return [gr.update(interactive=False)] * 5 tab_export.select(on_select_export_tab, [], export_tab_interactive_components) export_btn.click( export_results, [participant_id_input, alignment_reason_box, naturalness_reason_box, attractiveness_reason_box, optional_comment_box], [download_file, export_status] ) return app if __name__ == "__main__": os.makedirs(LOG_DIR, exist_ok=True) log_file_path = os.path.join(LOG_DIR, "evaluation_ui_log_{time}.log") random.seed(datetime.now().timestamp()) logger.remove() logger.add(sys.stderr, level="INFO") logger.add(log_file_path, rotation="10 MB") app = create_gradio_interface() app.launch(share=True, debug=True)