import gradio as gr import os import json import pandas as pd import random import shutil import time import collections from functools import wraps from filelock import FileLock from datasets import load_dataset, Audio from huggingface_hub import HfApi, hf_hub_download from multiprocessing import TimeoutError from concurrent.futures import ThreadPoolExecutor, TimeoutError as FutureTimeoutError # Load dataset from HuggingFace dataset = load_dataset("intersteller2887/Turing-test-dataset", split="train") dataset = dataset.cast_column("audio", Audio(decode=False)) # Prevent calling 'torchcodec' from newer version of 'datasets' # Huggingface space working directory: "/home/user/app" 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" # Output directory (Huggingface dataset directory) # Copy recordings to the working directory 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 # Take first file of the datasets as sample sample1_audio_path = local_audio_paths[0] print(sample1_audio_path) # ============================================================================== # Data Definition # ============================================================================== DIMENSIONS_DATA = [ { "title": "语义和语用特征", "audio": sample1_audio_path, "sub_dims": [ "记忆一致性：偏机器：出现上下文记忆不一致且无法察觉或修正（如遗忘掉关键信息坚持错误回答）；偏人类：在短上下文中记忆一致，若出现记忆偏差也会提问确认", "逻辑连贯性：偏机器：逻辑转折生硬或自相矛盾（如：突然切换话题无过渡）；偏人类：逻辑自然流畅", "读音正确性：偏机器：存在不自然的发音错误，常见多音字发音错误；偏人类：用字发音正确、自然，会结合语境正确使用常见多音字", "多语言混杂：偏机器：多语言混杂生硬，无语言切换逻辑；偏人类：说话多语言混杂往往和语境相关（专有名词、习惯用法），语言切换生硬卡顿，不自然", "语言不精确性：偏机器：回应通常不存在模糊表达，回答准确、肯定；偏人类：说话存在含糊表达：如“差不多”、“应该是吧”，且会出现自我打断或修正（“如不对不对”）的行为", "填充词使用：偏机器：很少使用填充词或填充词使用不自然；偏人类：在思考时经常使用填充词（如‘嗯’‘那个’）", "隐喻与语用用意：偏机器：表达直白，缺乏语义多样性，仅能字面理解语义；偏人类：使用隐喻、反语、委婉来表达多重含义" ], "reference_scores": [5, 5, 5, 0, 5, 5, 0] }, { "title": "非生理性副语言特征", "audio": sample1_audio_path, "sub_dims": [ "节奏：偏机器：说话几乎无停顿或停顿机械；偏人类：语速随语义起伏，偶尔卡顿或犹豫", "语调：偏机器：语调单一或变化过于规律，不符合语境；偏人类：在表达如疑问、惊讶、强调时，音调会自然上扬或下降", "重读：偏机器：没有在适当使用重读或出现强调部位异常；偏人类：有意识地重读重要词语，突出重点", "辅助性发声：偏机器：辅助性发声语境错误或机械化；偏人类：发出符合语境的非语言声音，如笑声、叹气等" ], "reference_scores": [5, 5, 5, 5] }, { "title": "生理性副语言特征", "audio": sample1_audio_path, "sub_dims": [ "微生理杂音：偏机器：语音过于干净，或发出不自然杂音（如电音）；偏人类：说话存在呼吸声、口水音、气泡音等无意识发声，且自然地出现在说话中", "发音不稳定性：偏机器：发音过于清晰规则；偏人类：发音存在一定不规则性（诸如连读、颤音、含糊发音、鼻音等）", "口音：偏机器：口音生硬；偏人类：存在自然的地区口音或语音特征" ], "reference_scores": [5, 4, 4] }, { "title": "机械人格", "audio": sample1_audio_path, "sub_dims": [ "谄媚现象：偏机器：频繁同意、感谢、道歉，过度认同对方观点，缺乏真实互动感；偏人类：根据语境判断是否同意对方提出的请求或表达的观点，不总是表示同意或进行附和", "书面化表达：偏机器：回应句式工整、规范，用词过于正式、频繁列举、用词泛泛；偏人类：口语化，表达灵活多变" ], "reference_scores": [5, 5] }, { "title": "情感表达", "audio": sample1_audio_path, "sub_dims": [ "语义层面：偏机器：未能针对对方情绪作出正常的情感反应，或表达情感的词语空泛、脱离语境；偏人类：对悲伤、开心等语境有符合人类的情绪反应", "声学层面：偏机器：情感语调模式化或与语境不符；偏人类：音调、音量、节奏等声学特征随情绪动态变化" ], "reference_scores": [5, 5] } ] DIMENSION_TITLES = [d["title"] for d in DIMENSIONS_DATA] SPECIAL_KEYWORDS = ["多语言混杂", "隐喻与语用用意", "口音"] MAX_SUB_DIMS = max(len(d['sub_dims']) for d in DIMENSIONS_DATA) THE_SUB_DIMS = [d['sub_dims'] for d in DIMENSIONS_DATA] # ============================================================================== # Backend Function Definitions # ============================================================================== # This version did not place file reading into filelock, concurrent read could happen """def load_or_initialize_count_json(audio_paths): try: # Only try downloading if file doesn't exist yet if not os.path.exists(COUNT_JSON_PATH): downloaded_path = hf_hub_download( repo_id="intersteller2887/Turing-test-dataset", repo_type="dataset", filename=COUNT_JSON_REPO_PATH, token=os.getenv("HF_TOKEN") ) # Save it as COUNT_JSON_PATH so that the lock logic remains untouched with open(downloaded_path, "rb") as src, open(COUNT_JSON_PATH, "wb") as dst: dst.write(src.read()) except Exception as e: print(f"Could not download count.json from HuggingFace dataset: {e}") # Add filelock to /workspace/count.json lock_path = COUNT_JSON_PATH + ".lock" # Read of count.json will wait for 10 seconds until another thread involving releases it, and then add a lock to it with FileLock(lock_path, timeout=10): # If count.json exists: load into count_data # Else initialize count_data with orderedDict if os.path.exists(COUNT_JSON_PATH): with open(COUNT_JSON_PATH, "r", encoding="utf-8") as f: count_data = json.load(f, object_pairs_hook=collections.OrderedDict) else: count_data = collections.OrderedDict() updated = False sample_audio_files = {os.path.basename(d["audio"]) for d in DIMENSIONS_DATA} # Guarantee that the sample recording won't be take into the pool # Update newly updated recordings into count.json for path in audio_paths: filename = os.path.basename(path) if filename not in count_data: if filename in sample_audio_files: count_data[filename] = 999 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: json.dump(count_data, f, indent=4, ensure_ascii=False) return count_data""" # Function that load or initialize count.json # Function is called when user start a challenge, and this will load or initialize count.json to working directory # Initialize happens when count.json does not exist in the working directory as well as HuggingFace dataset # Load happens when count.json exists in HuggingFace dataset, and it's not loaded to the working directory yet # After load/initialize, all newly added audio files will be added to count.json with initial value of 0 # Load/Initialize will generate count.json in the working directory for all users under this space # This version also places file reading into filelock, and modified def load_or_initialize_count_json(audio_paths): # Add filelock to /workspace/count.json lock_path = COUNT_JSON_PATH + ".lock" with FileLock(lock_path, timeout=10): # If count.json does not exist in the working directory, try to download it from HuggingFace dataset if not os.path.exists(COUNT_JSON_PATH): try: # Save latest count.json to working directory downloaded_path = hf_hub_download( repo_id="intersteller2887/Turing-test-dataset", repo_type="dataset", filename=COUNT_JSON_REPO_PATH, token=os.getenv("HF_TOKEN") ) with open(downloaded_path, "rb") as src, open(COUNT_JSON_PATH, "wb") as dst: dst.write(src.read()) except Exception: pass # If count.json exists in the working directory: load into count_data for potential update if os.path.exists(COUNT_JSON_PATH): with open(COUNT_JSON_PATH, "r", encoding="utf-8") as f: count_data = json.load(f, object_pairs_hook=collections.OrderedDict) # Else initialize count_data with orderedDict # This happens when there is no count.json (both working directory and HuggingFace dataset) else: count_data = collections.OrderedDict() updated = False sample_audio_files = {os.path.basename(d["audio"]) for d in DIMENSIONS_DATA} # Guarantee that the sample recording won't be take into the pool # Update newly updated recordings into count.json for path in audio_paths: filename = os.path.basename(path) if filename not in count_data: if filename in sample_audio_files: count_data[filename] = 999 else: count_data[filename] = 0 updated = True # Write updated count_data to /home/user/app/count.json if updated or not os.path.exists(COUNT_JSON_PATH): with open(COUNT_JSON_PATH, "w", encoding="utf-8") as f: json.dump(count_data, f, indent=4, ensure_ascii=False) return # Shorten the time of playing previous audio when reached next question def append_cache_buster(audio_path): return f"{audio_path}?t={int(time.time() * 1000)}" # Function that samples questions from avaliable question set # This version utilizes a given count_data to sample audio paths """def sample_audio_paths(audio_paths, count_data, k=5, max_count=1): # k for questions per test; max_count for question limit in total 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") # Shuffule to avoid fixed selections resulted from directory structure selected = random.sample(eligible_paths, k) # Once sampled a test, update these questions immediately for path in selected: filename = os.path.basename(path) count_data[filename] = count_data.get(filename, 0) + 1 # Add filelock to /workspace/count.json lock_path = COUNT_JSON_PATH + ".lock" with FileLock(lock_path, timeout=10): 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""" # This version places file reading into filelock to guarantee correct update of count.json def sample_audio_paths(audio_paths, k=5, max_count=1): # Add filelock to /workspace/count.json lock_path = COUNT_JSON_PATH + ".lock" # Load newest count.json with FileLock(lock_path, timeout=10): with open(COUNT_JSON_PATH, "r", encoding="utf-8") as f: count_data = json.load(f) 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") selected = random.sample(eligible_paths, k) # Update count_data for path in selected: filename = os.path.basename(path) count_data[filename] = count_data.get(filename, 0) + 1 # Update count.json 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 # Keep count_data atomic return selected # ============================================================================== # Frontend Function Definitions # ============================================================================== # Save question_set in each user_data_state, preventing global sharing def start_challenge(user_data_state): load_or_initialize_count_json(all_data_audio_paths) # selected_audio_paths, updated_count_data = sample_audio_paths(all_data_audio_paths, k=5) # Keep count_data atomic selected_audio_paths = sample_audio_paths(all_data_audio_paths, 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 ] user_data_state["question_set"] = question_set # count_data is not needed in the user data # user_data_state["updated_count_data"] = updated_count_data return gr.update(visible=False), gr.update(visible=True), user_data_state # This function toggles the visibility of the "其他（请注明）" input field based on the selected education choice def toggle_education_other(choice): is_other = (choice == "其他（请注明）") return gr.update(visible=is_other, interactive=is_other, value="") # This function checks if the user information is complete 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) # This function updates user_data and initializes the sample page (called when user submits their info) 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=0)) 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] slider_updates = [] dim_data = DIMENSIONS_DATA[d_idx] sub_dims = dim_data["sub_dims"] dim_title = dim_data["title"] existing_scores = selections.get(dim_data['title'], {}) progress_d = f"维度 {d_idx + 1} / {len(DIMENSIONS_DATA)}: **{dim_data['title']}**" for i in range(MAX_SUB_DIMS): if i < len(sub_dims): desc = sub_dims[i] # print(f"{desc} -> default value: {existing_scores.get(desc, 0)}") name = desc.split("：")[0].strip() default_value = 0 if name in SPECIAL_KEYWORDS else 1 value = existing_scores.get(desc, default_value) slider_updates.append(gr.update( visible=True, label=desc, minimum=default_value, maximum=5, step=1, value=value, interactive=True, )) # slider_updates.append(gr.update( # visible=True, # label=desc, # minimum=0 if name in SPECIAL_KEYWORDS else 1, # maximum=5, # value = existing_scores.get(desc, 0), # interactive=True, # )) else: slider_updates.append(gr.update(visible=False)) print(f"{desc} -> default value: {existing_scores.get(desc, 0)}") # 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, 0) # 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 = user_data["question_set"][q_idx] progress_q = f"第 {q_idx + 1} / {len(user_data['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 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 ) # ============================================================================== # Retry Function Definitions # ============================================================================== # Decorator function that allows to use ThreadPoolExecutor to retry a function with timeout def retry_with_timeout(max_retries=3, timeout=10, backoff=1): def decorator(func): @wraps(func) def wrapper(*args, **kwargs): last_exception = None for attempt in range(max_retries): try: with ThreadPoolExecutor(max_workers=1) as executor: future = executor.submit(func, *args, **kwargs) try: result = future.result(timeout=timeout) return result except FutureTimeoutError: future.cancel() raise TimeoutError(f"Operation timed out after {timeout} seconds") except Exception as e: last_exception = e print(f"Attempt {attempt + 1} failed: {str(e)}") if attempt < max_retries - 1: time.sleep(backoff * (attempt + 1)) print(f"All {max_retries} attempts failed") if last_exception: raise last_exception raise Exception("Unknown error occurred") return wrapper return decorator def save_with_retry(all_results, user_data): # 尝试上传到Hugging Face Hub try: # 使用线程安全的保存方式 with ThreadPoolExecutor(max_workers=1) as executor: future = executor.submit(save_all_results_to_file, all_results, user_data) try: future.result(timeout=30) # 设置30秒超时 return True except FutureTimeoutError: future.cancel() print("上传超时") return False except Exception as e: print(f"上传到Hub失败: {e}") return False def save_locally_with_retry(data, filename, max_retries=3): for attempt in range(max_retries): try: with open(filename, 'w', encoding='utf-8') as f: json.dump(data, f, indent=4, ensure_ascii=False) return True except Exception as e: print(f"本地保存尝试 {attempt + 1} 失败: {e}") if attempt < max_retries - 1: time.sleep(1) return False def update_count_with_retry(count_data, question_set, max_retries=3): for attempt in range(max_retries): try: lock_path = COUNT_JSON_PATH + ".lock" with FileLock(lock_path, timeout=10): # Remove unfinished question(s) from count.json for question in question_set: filename = os.path.basename(question['audio']) if filename in count_data and count_data[filename] < 1: count_data[filename] = 0 # Mark unfinished data as 0 with open(COUNT_JSON_PATH, 'w', encoding='utf-8') as f: json.dump(count_data, f, indent=4, ensure_ascii=False) return True except Exception as e: print(f"Fail to update count.json {e} for {attempt + 1} time") if attempt < max_retries - 1: time.sleep(1) return False # ============================================================================== # Previous version of submit_question_and_advance """def submit_question_and_advance(q_idx, d_idx, selections, final_choice, all_results, user_data): # selections["final_choice"] = final_choice cleaned_selections = {} for dim_title, sub_scores in selections.items(): # if dim_title == "final_choice": # 去掉if判断 cleaned_selections["final_choice"] = final_choice # continue cleaned_sub_scores = {} for sub_dim, score in sub_scores.items(): cleaned_sub_scores[sub_dim] = None if score == 0 else score cleaned_selections[dim_title] = cleaned_sub_scores final_question_result = { "question_id": q_idx, "audio_file": user_data["question_set"][q_idx]['audio'], "selections": cleaned_selections } all_results.append(final_question_result) q_idx += 1 # If q_idx hasn't reached the last one if q_idx < len(user_data["question_set"]): init_q_updates = init_test_question(user_data, q_idx) # Case 1: jam happens when initialize next question return init_q_updates + (all_results, gr.update(value="")) # If q_idx has reached the last one 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) save_all_results_to_file(all_results, user_data, count_data=user_data.get("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)""" # user_data now no further contain "updated_count_data", which should be read/write with filelock and be directly accessed from working directory def submit_question_and_advance(q_idx, d_idx, selections, final_choice, all_results, user_data): try: # 准备数据 cleaned_selections = {} for dim_title, sub_scores in selections.items(): cleaned_selections["final_choice"] = final_choice cleaned_sub_scores = {} for sub_dim, score in sub_scores.items(): cleaned_sub_scores[sub_dim] = None if score == 0 else score cleaned_selections[dim_title] = cleaned_sub_scores final_question_result = { "question_id": q_idx, "audio_file": user_data["question_set"][q_idx]['audio'], "selections": cleaned_selections } all_results.append(final_question_result) q_idx += 1 if q_idx < len(user_data["question_set"]): init_q_updates = init_test_question(user_data, q_idx) return init_q_updates + (all_results, gr.update(value="")) else: # 准备完整结果数据 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"##### 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" # 尝试上传（带重试） try: # success = save_with_retry(all_results, user_data, user_data.get("updated_count_data")) success = save_with_retry(all_results, user_data) except Exception as e: print(f"上传过程中发生错误: {e}") success = False if not success: # 上传失败，保存到本地 username = user_data.get("username", "anonymous") timestamp = pd.Timestamp.now().strftime('%Y%m%d_%H%M%S') local_filename = f"submission_{username}_{timestamp}.json" # 准备数据包 user_info_clean = { k: v for k, v in user_data.items() if k not in ["question_set"] } final_data_package = { "user_info": user_info_clean, "results": all_results } # 尝试保存到本地 local_success = save_locally_with_retry(final_data_package, local_filename) if local_success: result_str += f"\n\n⚠️ 上传失败，结果已保存到本地文件: {local_filename}" else: result_str += "\n\n❌ 上传失败且无法保存到本地文件，请联系管理员" # 更新count.json（剔除未完成的题目） try: with FileLock(COUNT_JSON_PATH + ".lock", timeout=5): with open(COUNT_JSON_PATH, "r", encoding="utf-8") as f: count_data = json.load(f, object_pairs_hook=collections.OrderedDict) count_update_success = update_count_with_retry(count_data, user_data["question_set"]) except Exception as e: print(f"更新count.json失败: {e}") count_update_success = False if not count_update_success: result_str += "\n\n⚠️ 无法更新题目计数，请联系管理员" 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) except Exception as e: print(f"提交过程中发生错误: {e}") # 返回错误信息 error_msg = f"提交过程中发生错误: {str(e)}" return ( gr.update(), gr.update(), gr.update(), gr.update(), q_idx, d_idx, selections, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), ) + (gr.update(),) * MAX_SUB_DIMS + (all_results, error_msg) """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" user_info_clean = { k: v for k, v in user_data.items() if k not in ["question_set", "updated_count_data"] } final_data_package = { "user_info": user_info_clean, "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() # Upload submission file 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}") if count_data: with FileLock(COUNT_JSON_PATH + ".lock", timeout=10): 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}" ) except Exception as e: print(f"上传出错: {e}")""" def save_all_results_to_file(all_results, user_data): 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" user_info_clean = { k: v for k, v in user_data.items() if k not in ["question_set"] } final_data_package = { "user_info": user_info_clean, "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: raise Exception("HF_TOKEN not found. Cannot upload to the Hub.") api = HfApi() # 上传提交文件（不再使用装饰器，直接调用） 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}" ) try: with FileLock(COUNT_JSON_PATH + ".lock", timeout=5): with open(COUNT_JSON_PATH, "r", encoding="utf-8") as f: count_data_str = f.read() api.upload_file( path_or_fileobj=bytes(count_data_str, "utf-8"), 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}" ) except Exception as e: print(f"上传 count.json 失败: {e}") # ============================================================================== # 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"], 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 (0-5分。0-特征无体现；1-机器；3-特征无偏向；5-人类)") sample_sliders = [gr.Slider(minimum=0, 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") reference_sliders = [gr.Slider(minimum=0, 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0到5打分**。\n" "- **评分解释如下：**\n" " - **0 分：特征未体现** (有些特征一定会体现，所以按1到5打分）；\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**；\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。\n" "## 特别注意\n" "- 我们希望您能判断每个维度上**回应者**的表现是**偏向人还是机器**，分数的大小反映回应者的语音类人的程度，而**不是**这个维度体现的程度多少\n（如读音正确也不代表是人类，读音错误也不代表是机器，您应当判断的是“听到的发音更偏向机器还是人类”)\n" "- 即使您一开始就已经很肯定回应方的身份，同样应当**独立地**对每个维度上回应方的表现进行细致的评判。比如您很肯定回应方是机器，也需要独立地对每个维度判断，而非简单地将每个维度归为偏机器。") 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 ### 请为对话中的回应者（非发起者）针对以下特征打分 (0-5分。0-特征无体现；1-机器；3-特征无偏向；5-人类)") test_sliders = [gr.Slider(minimum=0, maximum=5, step=1, label=f"Sub-dim {i+1}", visible=False, interactive=True, show_label = 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] all_files = [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)