import gradio as gr import os import json import pandas as pd import random import shutil import time import collections from datasets import load_dataset, Audio from huggingface_hub import HfApi dataset = load_dataset("intersteller2887/Turing-test-dataset", split="train") dataset = dataset.cast_column("audio", Audio(decode=False)) target_audio_dir = "/home/user/app/audio" os.makedirs(target_audio_dir, exist_ok=True) COUNT_JSON_PATH = "/home/user/app/count.json" COUNT_JSON_REPO_PATH = "submissions/count.json" local_audio_paths = [] for item in dataset: src_path = item["audio"]["path"] if src_path and os.path.exists(src_path): filename = os.path.basename(src_path) dst_path = os.path.join(target_audio_dir, filename) if not os.path.exists(dst_path): shutil.copy(src_path, dst_path) local_audio_paths.append(dst_path) all_data_audio_paths = local_audio_paths sample1_audio_path = local_audio_paths[0] # sample1_audio_path = next((p for p in all_data_audio_paths if p.endswith("sample1.wav")), None) print(sample1_audio_path) # ============================================================================== # 数据定义 (Data Definition) # ============================================================================== DIMENSIONS_DATA = [ { "title": "语义和语用特征", "audio": sample1_audio_path, "sub_dims": [ "记忆一致性：人类会选择性记忆并自我修正错误；机器出现前后矛盾时无法自主察觉或修正（如：遗忘关键细节但坚持错误答案）", "逻辑连贯性：人类逻辑自然流畅，允许合理跳跃；机器逻辑转折生硬或自相矛盾（如：突然切换话题无过渡）", "读音正确性：人类大部分情况下发音正确、自然，会结合语境使用、区分多音字；机器存在不自然的发音错误，且对多音字语境的判断能力有限", "多语言混杂：人类多语言混杂流畅，且带有语境色彩；机器多语言混杂生硬，无语言切换逻辑", "语言不精确性：人类说话时会使用带有犹豫语气的表达，且会出现自我修正的行为；机器的回应通常不存在模糊表达，回答准确、肯定", "填充词使用：人类填充词（如‘嗯’‘那个’）随机且带有思考痕迹；机器填充词规律重复或完全缺失", "隐喻与语用用意：人类会使用隐喻、反语、委婉来表达多重含义；机器表达直白，仅能字面理解或生硬使用修辞，缺乏语义多样性" ], "reference_scores": [5, 5, 3, 3, 5, 5, 3] }, { "title": "非生理性副语言特征", "audio": sample1_audio_path, "sub_dims": [ "节奏：人类语速随语义起伏，偶尔卡顿或犹豫；机器节奏均匀，几乎无停顿或停顿机械", "语调：人类在表达疑问、惊讶、强调时，音调会自然上扬或下降；机器语调单一或变化过于规律，不符合语境", "重读：人类会有意识地加强重要词语，从而突出信息焦点；机器的词语强度一致性强，或出现强调部位异常", "辅助性发声：人类会发出符合语境的非语言声音，如笑声、叹气等；机器的辅助性发声语义错误，或完全无辅助性发声" ], "reference_scores": [4, 5, 4, 3] }, { "title": "生理性副语言特征", "audio": sample1_audio_path, "sub_dims": [ "微生理杂音：人类说话存在呼吸声、口水音、气泡音等无意识发声，且自然地穿插在语流节奏当中；机器没有微生理杂音、语音过于干净，或添加不自然杂音", "发音不稳定性：人类存在个体化波动（如偶尔咬字不清、鼻音丰富等）；机器发音过于标准或统一，缺乏个性", "口音：人类存在自然的地区口音或语音特征；机器元音辅音机械拼接，或口音模式统一无差异" ], "reference_scores": [3, 3, 4] }, { "title": "机械人格", "audio": sample1_audio_path, "sub_dims": [ "谄媚现象：人类会根据语境判断是否同意，有时提出不同意见；机器频繁同意、感谢、道歉，过度认同对方观点，缺乏真实互动感", "书面化表达：人类表达灵活，；机器回应句式工整、规范，内容过于书面化、用词泛泛" ], "reference_scores": [5, 5] }, { "title": "情感表达", "audio": sample1_audio_path, "sub_dims": [ "语义层面：人类能对悲伤、开心等语境有符合人类的情感反应；机器回应情绪淡漠，或情感词泛泛、脱离语境", "声学层面：人类音调、音量随情绪动态变化；机器情感语调模式化，或与语境不符" ], "reference_scores": [3, 3] } ] DIMENSION_TITLES = [d["title"] for d in DIMENSIONS_DATA] def load_or_initialize_count_json(audio_paths): if os.path.exists(COUNT_JSON_PATH): with open(COUNT_JSON_PATH, "r", encoding="utf-8") as f: # 使用 object_pairs_hook 保持原始顺序 count_data = json.load(f, object_pairs_hook=collections.OrderedDict) else: count_data = collections.OrderedDict() updated = False # 获取所有样例音频文件名 sample_audio_files = {os.path.basename(d["audio"]) for d in DIMENSIONS_DATA} for path in audio_paths: filename = os.path.basename(path) if filename not in count_data: # 如果是样例音频，直接设置为最大值 if filename in sample_audio_files: count_data[filename] = 999 # 设置为很大的值，确保不会被选中 else: count_data[filename] = 0 updated = True if updated or not os.path.exists(COUNT_JSON_PATH): with open(COUNT_JSON_PATH, "w", encoding="utf-8") as f: # 确保写入时也保持顺序 json.dump(count_data, f, indent=4, ensure_ascii=False) return count_data def append_cache_buster(audio_path): return f"{audio_path}?t={int(time.time() * 1000)}" def sample_audio_paths(audio_paths, count_data, k=5, max_count=1): eligible_paths = [p for p in audio_paths if count_data.get(os.path.basename(p), 0) < max_count] if len(eligible_paths) < k: raise ValueError(f"可用音频数量不足（只剩 {len(eligible_paths)} 条 count<{max_count} 的音频），无法抽取 {k} 条") eligible_paths_copy = eligible_paths.copy() random.seed(int(time.time())) selected = random.sample(eligible_paths_copy, k) for path in selected: filename = os.path.basename(path) count_data[filename] = count_data.get(filename, 0) + 1 with open(COUNT_JSON_PATH, "w", encoding="utf-8") as f: json.dump(count_data, f, indent=4, ensure_ascii=False) return selected, count_data count_data = load_or_initialize_count_json(all_data_audio_paths) selected_audio_paths, updated_count_data = sample_audio_paths(all_data_audio_paths, count_data, k=5) QUESTION_SET = [ {"audio": path, "desc": f"这是音频文件 {os.path.basename(path)} 的描述"} for path in selected_audio_paths ] MAX_SUB_DIMS = max(len(d['sub_dims']) for d in DIMENSIONS_DATA) # ============================================================================== # 功能函数定义 (Function Definitions) # ============================================================================== """def start_challenge(): return gr.update(visible=False), gr.update(visible=True)""" """def start_challenge(user_data_state): global QUESTION_SET, updated_count_data # 每次点击“开始挑战”时重新抽题 count_data = load_or_initialize_count_json(all_data_audio_paths) selected_audio_paths, updated_count_data = sample_audio_paths(all_data_audio_paths, count_data, k=5) QUESTION_SET = [ {"audio": path, "desc": f"这是音频文件 {os.path.basename(path)} 的描述"} for path in selected_audio_paths ] # 重置 user_data 中的状态（也可以留空） user_data_state.clear() return gr.update(visible=False), gr.update(visible=True), user_data_state""" def start_challenge(user_data_state): global QUESTION_SET # Step 1: 读取最新 count.json count_data = load_or_initialize_count_json(all_data_audio_paths) # Step 2: 从 eligible 音频中抽题 selected_audio_paths, _ = sample_audio_paths( audio_paths=all_data_audio_paths, count_data=count_data.copy(), # 不修改原始数据 k=5, max_count=1 ) # Step 3: 保存抽到的音频文件名在用户状态中 user_data_state["selected_filenames"] = [os.path.basename(p) for p in selected_audio_paths] # Step 4: 设置题目列表 QUESTION_SET = [ {"audio": path, "desc": f"这是音频文件 {os.path.basename(path)} 的描述"} for path in selected_audio_paths ] return gr.update(visible=False), gr.update(visible=True), user_data_state def toggle_education_other(choice): is_other = (choice == "其他（请注明）") return gr.update(visible=is_other, interactive=is_other, value="") def check_info_complete(username, age, gender, education, education_other, ai_experience): if username.strip() and age and gender and education and ai_experience: if education == "其他（请注明）" and not education_other.strip(): return gr.update(interactive=False) return gr.update(interactive=True) return gr.update(interactive=False) def show_sample_page_and_init(username, age, gender, education, education_other, ai_experience, user_data): final_edu = education_other if education == "其他（请注明）" else education user_data.update({ "username": username.strip(), "age": age, "gender": gender, "education": final_edu, "ai_experience": ai_experience }) first_dim_title = DIMENSION_TITLES[0] initial_updates = update_sample_view(first_dim_title) return [ gr.update(visible=False), gr.update(visible=True), user_data, first_dim_title ] + initial_updates def update_sample_view(dimension_title): dim_data = next((d for d in DIMENSIONS_DATA if d["title"] == dimension_title), None) if dim_data: audio_up = gr.update(value=dim_data["audio"]) # audio_up = gr.update(value=append_cache_buster(dim_data["audio"])) interactive_view_up = gr.update(visible=True) reference_view_up = gr.update(visible=False) reference_btn_up = gr.update(value="参考") sample_slider_ups = [] ref_slider_ups = [] scores = dim_data.get("reference_scores", []) for i in range(MAX_SUB_DIMS): if i < len(dim_data['sub_dims']): label = dim_data['sub_dims'][i] score = scores[i] if i < len(scores) else 0 sample_slider_ups.append(gr.update(visible=True, label=label, value=3)) ref_slider_ups.append(gr.update(visible=True, label=label, value=score)) else: sample_slider_ups.append(gr.update(visible=False, value=0)) ref_slider_ups.append(gr.update(visible=False, value=0)) return [audio_up, interactive_view_up, reference_view_up, reference_btn_up] + sample_slider_ups + ref_slider_ups empty_updates = [gr.update()] * 4 slider_empty_updates = [gr.update()] * (MAX_SUB_DIMS * 2) return empty_updates + slider_empty_updates def update_test_dimension_view(d_idx, selections): dimension = DIMENSIONS_DATA[d_idx] progress_d = f"维度 {d_idx + 1} / {len(DIMENSIONS_DATA)}: **{dimension['title']}**" existing_scores = selections.get(dimension['title'], {}) slider_updates = [] for i in range(MAX_SUB_DIMS): if i < len(dimension['sub_dims']): sub_dim_label = dimension['sub_dims'][i] value = existing_scores.get(sub_dim_label, 3) slider_updates.append(gr.update(visible=True, label=sub_dim_label, value=value)) else: slider_updates.append(gr.update(visible=False, value=0)) prev_btn_update = gr.update(interactive=(d_idx > 0)) next_btn_update = gr.update( value="进入最终判断" if d_idx == len(DIMENSIONS_DATA) - 1 else "下一维度", interactive=True ) return [gr.update(value=progress_d), prev_btn_update, next_btn_update] + slider_updates def init_test_question(user_data, q_idx): d_idx = 0 question = QUESTION_SET[q_idx] progress_q = f"第 {q_idx + 1} / {len(QUESTION_SET)} 题" initial_updates = update_test_dimension_view(d_idx, {}) dim_title_update, prev_btn_update, next_btn_update = initial_updates[:3] slider_updates = initial_updates[3:] return ( gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), q_idx, d_idx, {}, gr.update(value=progress_q), dim_title_update, gr.update(value=question['audio']), # gr.update(value=append_cache_buster(question['audio'])), prev_btn_update, next_btn_update, gr.update(value=None), # BUG FIX: Changed from "" to None to correctly clear the radio button gr.update(interactive=False), ) + tuple(slider_updates) def navigate_dimensions(direction, q_idx, d_idx, selections, *slider_values): current_dim_data = DIMENSIONS_DATA[d_idx] current_sub_dims = current_dim_data['sub_dims'] scores = {sub_dim: slider_values[i] for i, sub_dim in enumerate(current_sub_dims)} selections[current_dim_data['title']] = scores new_d_idx = d_idx + (1 if direction == "next" else -1) if direction == "next" and d_idx == len(DIMENSIONS_DATA) - 1: return ( gr.update(visible=False), gr.update(visible=True), q_idx, new_d_idx, selections, gr.update(), gr.update(), gr.update(), gr.update(interactive=True), gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False), ) + (gr.update(),) * MAX_SUB_DIMS else: view_updates = update_test_dimension_view(new_d_idx, selections) dim_title_update, prev_btn_update, next_btn_update = view_updates[:3] slider_updates = view_updates[3:] return ( gr.update(), gr.update(), q_idx, new_d_idx, selections, gr.update(), dim_title_update, gr.update(), gr.update(), gr.update(), prev_btn_update, next_btn_update, ) + tuple(slider_updates) def submit_question_and_advance(q_idx, d_idx, selections, final_choice, all_results, user_data): selections["final_choice"] = final_choice final_question_result = { "question_id": q_idx, "audio_file": QUESTION_SET[q_idx]['audio'], "selections": selections } all_results.append(final_question_result) q_idx += 1 if q_idx < len(QUESTION_SET): init_q_updates = init_test_question(user_data, q_idx) return init_q_updates + (all_results, gr.update(value="")) else: result_str = "### 测试全部完成！\n\n你的提交结果概览：\n" for res in all_results: # result_str += f"\n#### 题目: {res['audio_file']}\n" result_str += f"##### 最终判断: **{res['selections'].get('final_choice', '未选择')}**\n" for dim_title, dim_data in res['selections'].items(): if dim_title == 'final_choice': continue result_str += f"- **{dim_title}**:\n" for sub_dim, score in dim_data.items(): result_str += f" - *{sub_dim[:20]}...*: {score}/5\n" # save_all_results_to_file(all_results, user_data) save_all_results_to_file(all_results, user_data, count_data=updated_count_data) return ( gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), q_idx, d_idx, {}, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), ) + (gr.update(),) * MAX_SUB_DIMS + (all_results, result_str) def save_all_results_to_file(all_results, user_data, count_data=None): repo_id = "intersteller2887/Turing-test-dataset" username = user_data.get("username", "user") timestamp = pd.Timestamp.now().strftime('%Y%m%d_%H%M%S') submission_filename = f"submissions_{username}_{timestamp}.json" final_data_package = { "user_info": user_data, "results": all_results } json_string = json.dumps(final_data_package, ensure_ascii=False, indent=4) hf_token = os.getenv("HF_TOKEN") if not hf_token: print("HF_TOKEN not found. Cannot upload to the Hub.") return try: api = HfApi() # 上传 submission 文件 api.upload_file( path_or_fileobj=bytes(json_string, "utf-8"), path_in_repo=f"submissions/{submission_filename}", repo_id=repo_id, repo_type="dataset", token=hf_token, commit_message=f"Add new submission from {username}" ) print(f"上传成功: {submission_filename}") # 上传 count.json（如果提供） if count_data: with open(COUNT_JSON_PATH, "w", encoding="utf-8") as f: json.dump(count_data, f, indent=4, ensure_ascii=False) api.upload_file( path_or_fileobj=COUNT_JSON_PATH, path_in_repo=COUNT_JSON_REPO_PATH, repo_id=repo_id, repo_type="dataset", token=hf_token, commit_message=f"Update count.json after submission by {username}" ) print("count.json 上传成功") except Exception as e: print(f"上传出错: {e}") def toggle_reference_view(current): if current == "参考": return gr.update(visible=False), gr.update(visible=True), gr.update(value="返回") else: return gr.update(visible=True), gr.update(visible=False), gr.update(value="参考") def back_to_welcome(): return ( gr.update(visible=True), # welcome_page gr.update(visible=False), # info_page gr.update(visible=False), # sample_page gr.update(visible=False), # pretest_page gr.update(visible=False), # test_page gr.update(visible=False), # final_judgment_page gr.update(visible=False), # result_page {}, # user_data_state 0, # current_question_index 0, # current_test_dimension_index {}, # current_question_selections [] # test_results ) # ============================================================================== # Gradio 界面定义 (Gradio UI Definition) # ============================================================================== with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 960px !important}") as demo: user_data_state = gr.State({}) current_question_index = gr.State(0) current_test_dimension_index = gr.State(0) current_question_selections = gr.State({}) test_results = gr.State([]) welcome_page = gr.Column(visible=True) info_page = gr.Column(visible=False) sample_page = gr.Column(visible=False) pretest_page = gr.Column(visible=False) test_page = gr.Column(visible=False) final_judgment_page = gr.Column(visible=False) result_page = gr.Column(visible=False) pages = { "welcome": welcome_page, "info": info_page, "sample": sample_page, "pretest": pretest_page, "test": test_page, "final_judgment": final_judgment_page, "result": result_page } with welcome_page: gr.Markdown("# AI 识破者\n你将听到一系列对话，请判断哪个回应者是 AI。") start_btn = gr.Button("开始挑战", variant="primary") with info_page: gr.Markdown("## 请提供一些基本信息") username_input = gr.Textbox(label="用户名", placeholder="请输入你的昵称") age_input = gr.Radio(["18岁以下", "18-25岁", "26-35岁", "36-50岁", "50岁以上"], label="年龄") gender_input = gr.Radio(["男", "女", "其他"], label="性别") education_input = gr.Radio(["高中及以下", "本科", "硕士", "博士", "其他（请注明）"], label="学历") education_other_input = gr.Textbox(label="请填写你的学历", visible=False, interactive=False) ai_experience_input = gr.Radio(["从未使用过", "偶尔接触（如看别人用）", "使用过几次，了解基本功能", "经常使用，有一定操作经验", "非常熟悉，深入使用过多个 AI 工具"], label="对 AI 工具的熟悉程度") submit_info_btn = gr.Button("提交并开始学习样例", variant="primary", interactive=False) with sample_page: gr.Markdown("## 样例分析\n请选择一个维度进行学习和打分练习。所有维度共用同一个样例音频。") sample_dimension_selector = gr.Radio(DIMENSION_TITLES, label="选择学习维度", value=DIMENSION_TITLES[0]) with gr.Row(): with gr.Column(scale=1): sample_audio = gr.Audio(label="样例音频", value=DIMENSIONS_DATA[0]["audio"]) with gr.Column(scale=2): with gr.Column(visible=True) as interactive_view: gr.Markdown("#### 请为以下特征打分 (1-5分。1对应机器，5对应人类)") sample_sliders = [gr.Slider(minimum=1, maximum=5, step=1, label=f"Sub-dim {i+1}", visible=False, interactive=True) for i in range(MAX_SUB_DIMS)] with gr.Column(visible=False) as reference_view: gr.Markdown("### 参考答案解析 (1-5分。1对应机器，5对应人类)") reference_sliders = [gr.Slider(minimum=1, maximum=5, step=1, label=f"Sub-dim {i+1}", visible=False, interactive=False) for i in range(MAX_SUB_DIMS)] with gr.Row(): reference_btn = gr.Button("参考") go_to_pretest_btn = gr.Button("我明白了，开始测试", variant="primary") with pretest_page: gr.Markdown("## 测试说明\n" "- 对于每一道题，你都需要对全部 **5 个维度** 进行评估。\n" "- 在每个维度下，请为出现的每个特征 **从1到5打分。\n" "- **评分解释如下：**\n" " - **1 分：极度符合机器特征**；\n" " - **2 分：较为符合机器特征**；\n" " - **3 分：无明显人类或机器倾向或特征无体现**；\n" " - **4 分：较为符合人类特征**；\n" " - **5 分：极度符合人类特征**。\n" "- 完成所有维度后，请根据整体印象对回应方的身份做出做出“人类”或“机器人”的 **最终判断**。\n" "- 你可以使用“上一维度”和“下一维度”按钮在5个维度间自由切换和修改分数。") go_to_test_btn = gr.Button("开始测试", variant="primary") with test_page: gr.Markdown("## 正式测试") question_progress_text = gr.Markdown() test_dimension_title = gr.Markdown() test_audio = gr.Audio(label="测试音频") gr.Markdown("--- \n ### 请为以下特征打分 (1-5分。1对应机器，5对应人类)") test_sliders = [gr.Slider(minimum=1, maximum=5, step=1, label=f"Sub-dim {i+1}", visible=False, interactive=True) for i in range(MAX_SUB_DIMS)] with gr.Row(): prev_dim_btn = gr.Button("上一维度") next_dim_btn = gr.Button("下一维度", variant="primary") with final_judgment_page: gr.Markdown("## 最终判断") gr.Markdown("您已完成对所有维度的评分。请根据您的综合印象，做出最终判断。") final_human_robot_radio = gr.Radio(["👤 人类", "🤖 机器人"], label="请判断回应者类型 (必填)") submit_final_answer_btn = gr.Button("提交本题答案", variant="primary", interactive=False) with result_page: gr.Markdown("## 测试完成") result_text = gr.Markdown() back_to_welcome_btn = gr.Button("返回主界面", variant="primary") # ============================================================================== # 事件绑定 (Event Binding) & IO 列表定义 # ============================================================================== sample_init_outputs = [ info_page, sample_page, user_data_state, sample_dimension_selector, sample_audio, interactive_view, reference_view, reference_btn ] + sample_sliders + reference_sliders test_init_outputs = [ pretest_page, test_page, final_judgment_page, result_page, current_question_index, current_test_dimension_index, current_question_selections, question_progress_text, test_dimension_title, test_audio, prev_dim_btn, next_dim_btn, final_human_robot_radio, submit_final_answer_btn, ] + test_sliders nav_inputs = [current_question_index, current_test_dimension_index, current_question_selections] + test_sliders nav_outputs = [ test_page, final_judgment_page, current_question_index, current_test_dimension_index, current_question_selections, question_progress_text, test_dimension_title, test_audio, final_human_robot_radio, submit_final_answer_btn, prev_dim_btn, next_dim_btn, ] + test_sliders full_outputs_with_results = test_init_outputs + [test_results, result_text] # start_btn.click(fn=start_challenge, outputs=[welcome_page, info_page]) start_btn.click( fn=start_challenge, inputs=[user_data_state], outputs=[welcome_page, info_page, user_data_state] ) for comp in [age_input, gender_input, education_input, education_other_input, ai_experience_input]: comp.change( fn=check_info_complete, inputs=[username_input, age_input, gender_input, education_input, education_other_input, ai_experience_input], outputs=submit_info_btn ) education_input.change(fn=toggle_education_other, inputs=education_input, outputs=education_other_input) submit_info_btn.click( fn=show_sample_page_and_init, inputs=[username_input, age_input, gender_input, education_input, education_other_input, ai_experience_input, user_data_state], outputs=sample_init_outputs ) sample_dimension_selector.change( fn=update_sample_view, inputs=sample_dimension_selector, outputs=[sample_audio, interactive_view, reference_view, reference_btn] + sample_sliders + reference_sliders ) reference_btn.click( fn=toggle_reference_view, inputs=reference_btn, outputs=[interactive_view, reference_view, reference_btn] ) go_to_pretest_btn.click(lambda: (gr.update(visible=False), gr.update(visible=True)), outputs=[sample_page, pretest_page]) go_to_test_btn.click( fn=lambda user: init_test_question(user, 0) + ([], gr.update()), inputs=[user_data_state], outputs=full_outputs_with_results ) prev_dim_btn.click( fn=lambda q,d,s, *sliders: navigate_dimensions("prev", q,d,s, *sliders), inputs=nav_inputs, outputs=nav_outputs ) next_dim_btn.click( fn=lambda q,d,s, *sliders: navigate_dimensions("next", q,d,s, *sliders), inputs=nav_inputs, outputs=nav_outputs ) final_human_robot_radio.change( fn=lambda choice: gr.update(interactive=bool(choice)), inputs=final_human_robot_radio, outputs=submit_final_answer_btn ) submit_final_answer_btn.click( fn=submit_question_and_advance, inputs=[current_question_index, current_test_dimension_index, current_question_selections, final_human_robot_radio, test_results, user_data_state], outputs=full_outputs_with_results ) back_to_welcome_btn.click(fn=back_to_welcome, outputs=list(pages.values()) + [user_data_state, current_question_index, current_test_dimension_index, current_question_selections, test_results]) # ============================================================================== # 程序入口 (Entry Point) # ============================================================================== if __name__ == "__main__": if not os.path.exists("audio"): os.makedirs("audio") if "SPACE_ID" in os.environ: print("Running in a Hugging Face Space, checking for audio files...") all_files = [q["audio"] for q in QUESTION_SET] + [d["audio"] for d in DIMENSIONS_DATA] for audio_file in set(all_files): if not os.path.exists(audio_file): print(f"⚠️ Warning: Audio file not found: {audio_file}") demo.launch(debug=True)