import gradio as gr import pandas as pd import requests from bs4 import BeautifulSoup from docx import Document import os from openai import OpenAI import json from youtube_transcript_api import YouTubeTranscriptApi from youtube_transcript_api._errors import NoTranscriptFound from moviepy.editor import VideoFileClip from pytube import YouTube import os from google.cloud import storage from google.oauth2 import service_account from googleapiclient.discovery import build from googleapiclient.http import MediaFileUpload from googleapiclient.http import MediaIoBaseDownload from googleapiclient.http import MediaIoBaseUpload import io import time from urllib.parse import urlparse, parse_qs # 假设您的环境变量或Secret的名称是GOOGLE_APPLICATION_CREDENTIALS_JSON # credentials_json_string = os.getenv("GOOGLE_APPLICATION_CREDENTIALS_JSON") # credentials_dict = json.loads(credentials_json_string) # SCOPES = ['https://www.googleapis.com/auth/drive'] # credentials = service_account.Credentials.from_service_account_info( # credentials_dict, scopes=SCOPES) # service = build('drive', 'v3', credentials=credentials) # # 列出 Google Drive 上的前10個文件 # results = service.files().list(pageSize=10, fields="nextPageToken, files(id, name)").execute() # items = results.get('files', []) # if not items: # print('No files found.') # else: # print("=====Google Drive 上的前10個文件=====") # print('Files:') # for item in items: # print(u'{0} ({1})'.format(item['name'], item['id'])) OUTPUT_PATH = 'videos' TRANSCRIPTS = [] CURRENT_INDEX = 0 VIDEO_ID = "" OPEN_AI_KEY = os.getenv("OPEN_AI_KEY") OPEN_AI_CLIENT = OpenAI(api_key=OPEN_AI_KEY) DRIVE_KEY = os.getenv("GOOGLE_APPLICATION_CREDENTIALS_JSON") GCS_KEY = os.getenv("GOOGLE_APPLICATION_CREDENTIALS_JSON") # ====gcs==== def init_gcs_client(service_account_key_string): """使用服务账号密钥文件创建 GCS 客户端""" credentials_json_string = service_account_key_string credentials_dict = json.loads(credentials_json_string) credentials = service_account.Credentials.from_service_account_info(credentials_dict) gcs_client = storage.Client(credentials=credentials, project=credentials_dict['project_id']) return gcs_client def gcs_create_bucket_folder_if_not_exists(gcs_client, bucket_name, folder_name): """检查是否存在特定名称的文件夹（前缀），如果不存在则创建一个标记文件来模拟文件夹""" bucket = gcs_client.bucket(bucket_name) blob = bucket.blob(folder_name) if not blob.exists(): blob.upload_from_string('', content_type='application/x-www-form-urlencoded;charset=UTF-8') print(f"GCS Folder '{folder_name}' created.") else: print(f"GCS Folder '{folder_name}' already exists.") def gcs_check_folder_exists(gcs_client, bucket_name, folder_name): """检查 GCS 存储桶中是否存在指定的文件夹""" bucket = gcs_client.bucket(bucket_name) blobs = list(bucket.list_blobs(prefix=folder_name)) return len(blobs) > 0 def gcs_check_file_exists(gcs_client, bucket_name, file_name): """ 检查 GCS 存储桶中是否存在指定的文件 file_name 格式：{folder_name}/{file_name} """ bucket = gcs_client.bucket(bucket_name) blob = bucket.blob(file_name) return blob.exists() def upload_file_to_gcs(gcs_client, bucket_name, destination_blob_name, file_path): """上传文件到指定的 GCS 存储桶""" bucket = gcs_client.bucket(bucket_name) blob = bucket.blob(destination_blob_name) blob.upload_from_filename(file_path) print(f"File {file_path} uploaded to {destination_blob_name} in GCS.") def upload_file_to_gcs_with_json_string(gcs_client, bucket_name, destination_blob_name, json_string): """上传字符串到指定的 GCS 存储桶""" bucket = gcs_client.bucket(bucket_name) blob = bucket.blob(destination_blob_name) blob.upload_from_string(json_string) print(f"JSON string uploaded to {destination_blob_name} in GCS.") def download_blob_to_string(gcs_client, bucket_name, source_blob_name): """从 GCS 下载文件内容到字符串""" bucket = gcs_client.bucket(bucket_name) blob = bucket.blob(source_blob_name) return blob.download_as_text() def make_blob_public(gcs_client, bucket_name, blob_name): """将指定的 GCS 对象设置为公共可读""" bucket = gcs_client.bucket(bucket_name) blob = bucket.blob(blob_name) blob.make_public() print(f"Blob {blob_name} is now publicly accessible at {blob.public_url}") def get_blob_public_url(gcs_client, bucket_name, blob_name): """获取指定 GCS 对象的公开 URL""" bucket = gcs_client.bucket(bucket_name) blob = bucket.blob(blob_name) return blob.public_url def upload_img_and_get_public_url(gcs_client, bucket_name, file_name, file_path): """上传图片到 GCS 并获取其公开 URL""" # 上传图片 upload_file_to_gcs(gcs_client, bucket_name, file_name, file_path) # 将上传的图片设置为公开 make_blob_public(gcs_client, bucket_name, file_name) # 获取图片的公开 URL public_url = get_blob_public_url(gcs_client, bucket_name, file_name) print(f"Public URL for the uploaded image: {public_url}") return public_url def copy_all_files_from_drive_to_gcs(drive_service, gcs_client, drive_folder_id, bucket_name, gcs_folder_name): # Get all files from the folder query = f"'{drive_folder_id}' in parents and trashed = false" response = drive_service.files().list(q=query).execute() files = response.get('files', []) for file in files: # Copy each file to GCS file_id = file['id'] file_name = file['name'] gcs_destination_path = f"{gcs_folder_name}/{file_name}" copy_file_from_drive_to_gcs(drive_service, gcs_client, file_id, bucket_name, gcs_destination_path) def copy_file_from_drive_to_gcs(drive_service, gcs_client, file_id, bucket_name, gcs_destination_path): # Download file content from Drive request = drive_service.files().get_media(fileId=file_id) fh = io.BytesIO() downloader = MediaIoBaseDownload(fh, request) done = False while not done: status, done = downloader.next_chunk() fh.seek(0) file_content = fh.getvalue() # Upload file content to GCS bucket = gcs_client.bucket(bucket_name) blob = bucket.blob(gcs_destination_path) blob.upload_from_string(file_content) print(f"File {file_id} copied to GCS at {gcs_destination_path}.") # # ====drive====初始化 def init_drive_service(): credentials_json_string = DRIVE_KEY credentials_dict = json.loads(credentials_json_string) SCOPES = ['https://www.googleapis.com/auth/drive'] credentials = service_account.Credentials.from_service_account_info( credentials_dict, scopes=SCOPES) service = build('drive', 'v3', credentials=credentials) return service def create_folder_if_not_exists(service, folder_name, parent_id): print("检查是否存在特定名称的文件夹，如果不存在则创建") query = f"mimeType='application/vnd.google-apps.folder' and name='{folder_name}' and '{parent_id}' in parents and trashed=false" response = service.files().list(q=query, spaces='drive', fields="files(id, name)").execute() folders = response.get('files', []) if not folders: # 文件夹不存在，创建新文件夹 file_metadata = { 'name': folder_name, 'mimeType': 'application/vnd.google-apps.folder', 'parents': [parent_id] } folder = service.files().create(body=file_metadata, fields='id').execute() return folder.get('id') else: # 文件夹已存在 return folders[0]['id'] # 检查Google Drive上是否存在文件 def check_file_exists(service, folder_name, file_name): query = f"name = '{file_name}' and '{folder_name}' in parents and trashed = false" response = service.files().list(q=query).execute() files = response.get('files', []) return len(files) > 0, files[0]['id'] if files else None def upload_content_directly(service, file_name, folder_id, content): """ 直接将内容上传到Google Drive中的新文件。 """ if not file_name: raise ValueError("文件名不能为空") if not folder_id: raise ValueError("文件夹ID不能为空") if content is None: # 允许空字符串上传，但不允许None raise ValueError("内容不能为空") file_metadata = {'name': file_name, 'parents': [folder_id]} # 使用io.BytesIO为文本内容创建一个内存中的文件对象 try: with io.BytesIO(content.encode('utf-8')) as fh: media = MediaIoBaseUpload(fh, mimetype='text/plain', resumable=True) print("==content==") print(content) print("==content==") print("==media==") print(media) print("==media==") # 执行上传 file = service.files().create(body=file_metadata, media_body=media, fields='id').execute() return file.get('id') except Exception as e: print(f"上传文件时发生错误: {e}") raise # 重新抛出异常，调用者可以根据需要处理或忽略 def upload_file_directly(service, file_name, folder_id, file_path): # 上傳 .json to Google Drive file_metadata = {'name': file_name, 'parents': [folder_id]} media = MediaFileUpload(file_path, mimetype='application/json') file = service.files().create(body=file_metadata, media_body=media, fields='id').execute() # return file.get('id') # 返回文件ID return True def upload_img_directly(service, file_name, folder_id, file_path): file_metadata = {'name': file_name, 'parents': [folder_id]} media = MediaFileUpload(file_path, mimetype='image/jpeg') file = service.files().create(body=file_metadata, media_body=media, fields='id').execute() return file.get('id') # 返回文件ID def download_file_as_string(service, file_id): """ 从Google Drive下载文件并将其作为字符串返回。 """ request = service.files().get_media(fileId=file_id) fh = io.BytesIO() downloader = MediaIoBaseDownload(fh, request) done = False while done is False: status, done = downloader.next_chunk() fh.seek(0) content = fh.read().decode('utf-8') return content def set_public_permission(service, file_id): service.permissions().create( fileId=file_id, body={"type": "anyone", "role": "reader"}, fields='id', ).execute() def update_file_on_drive(service, file_id, file_content): """ 更新Google Drive上的文件内容。 参数: - service: Google Drive API服务实例。 - file_id: 要更新的文件的ID。 - file_content: 新的文件内容，字符串格式。 """ # 将新的文件内容转换为字节流 fh = io.BytesIO(file_content.encode('utf-8')) media = MediaIoBaseUpload(fh, mimetype='application/json', resumable=True) # 更新文件 updated_file = service.files().update( fileId=file_id, media_body=media ).execute() print(f"文件已更新，文件ID: {updated_file['id']}") # ---- Main Functions ---- def process_file(file): # 读取文件 if file.name.endswith('.csv'): df = pd.read_csv(file) text = df_to_text(df) elif file.name.endswith('.xlsx'): df = pd.read_excel(file) text = df_to_text(df) elif file.name.endswith('.docx'): text = docx_to_text(file) else: raise ValueError("Unsupported file type") df_string = df.to_string() # 宜蘭：移除@XX@符号 to | df_string = df_string.replace("@XX@", "|") # 根据上传的文件内容生成问题 questions = generate_questions(df_string) summary = generate_summarise(df_string) # 返回按钮文本和 DataFrame 字符串 return questions[0] if len(questions) > 0 else "", \ questions[1] if len(questions) > 1 else "", \ questions[2] if len(questions) > 2 else "", \ summary, \ df_string def df_to_text(df): # 将 DataFrame 转换为纯文本 return df.to_string() def docx_to_text(file): # 将 Word 文档转换为纯文本 doc = Document(file) return "\n".join([para.text for para in doc.paragraphs]) def format_seconds_to_time(seconds): """将秒数格式化为 时:分:秒 的形式""" hours = int(seconds // 3600) minutes = int((seconds % 3600) // 60) seconds = int(seconds % 60) return f"{hours:02}:{minutes:02}:{seconds:02}" def extract_youtube_id(url): parsed_url = urlparse(url) if "youtube.com" in parsed_url.netloc: # 对于标准链接，视频ID在查询参数'v'中 query_params = parse_qs(parsed_url.query) return query_params.get("v")[0] if "v" in query_params else None elif "youtu.be" in parsed_url.netloc: # 对于短链接，视频ID是路径的一部分 return parsed_url.path.lstrip('/') else: return None def get_transcript(video_id): languages = ['zh-TW', 'zh-Hant', 'zh', 'en'] # 優先順序列表 for language in languages: try: transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=[language]) return transcript # 成功獲取字幕，直接返回結果 except NoTranscriptFound: continue # 當前語言的字幕沒有找到，繼續嘗試下一個語言 return None # 所有嘗試都失敗，返回None def process_transcript_and_screenshots(video_id): print("====process_transcript_and_screenshots====") # Drive service = init_drive_service() parent_folder_id = '1GgI4YVs0KckwStVQkLa1NZ8IpaEMurkL' folder_id = create_folder_if_not_exists(service, video_id, parent_folder_id) # 逐字稿文件名 file_name = f'{video_id}_transcript.json' # 检查逐字稿是否存在 exists, file_id = check_file_exists(service, folder_id, file_name) if not exists: # 从YouTube获取逐字稿并上传 transcript = get_transcript(video_id) if transcript: print("成功獲取字幕") else: print("沒有找到字幕") transcript_text = json.dumps(transcript, ensure_ascii=False, indent=2) file_id = upload_content_directly(service, file_name, folder_id, transcript_text) print("逐字稿已上传到Google Drive") else: # 逐字稿已存在，下载逐字稿内容 print("逐字稿已存在于Google Drive中") transcript_text = download_file_as_string(service, file_id) transcript = json.loads(transcript_text) # 处理逐字稿中的每个条目，检查并上传截图 for entry in transcript: if 'img_file_id' not in entry: screenshot_path = screenshot_youtube_video(video_id, entry['start']) img_file_id = upload_img_directly(service, f"{video_id}_{entry['start']}.jpg", folder_id, screenshot_path) set_public_permission(service, img_file_id) entry['img_file_id'] = img_file_id print(f"截图已上传到Google Drive: {img_file_id}") # 更新逐字稿文件 updated_transcript_text = json.dumps(transcript, ensure_ascii=False, indent=2) update_file_on_drive(service, file_id, updated_transcript_text) print("逐字稿已更新，包括截图链接") # init gcs client gcs_client = init_gcs_client(GCS_KEY) bucket_name = 'video_ai_assistant' # 检查 folder 是否存在 is_gcs_exists = gcs_check_folder_exists(gcs_client, bucket_name, video_id) if not is_gcs_exists: gcs_create_bucket_folder_if_not_exists(gcs_client, bucket_name, video_id) copy_all_files_from_drive_to_gcs(service, gcs_client, folder_id, bucket_name, video_id) print("Drive file 已上传到GCS") else: print("GCS folder:{video_id} 已存在") return transcript def process_transcript_and_screenshots_on_gcs(video_id): print("====process_transcript_and_screenshots_on_gcs====") # GCS gcs_client = init_gcs_client(GCS_KEY) bucket_name = 'video_ai_assistant' # 检查 folder 是否存在 # is_gcs_exists = gcs_check_folder_exists(gcs_client, bucket_name, video_id) # if not is_gcs_exists: # gcs_create_bucket_folder_if_not_exists(gcs_client, bucket_name, video_id) # print("GCS folder:{video_id} 已创建") # else: # print("GCS folder:{video_id} 已存在") # 逐字稿文件名 transcript_file_name = f'{video_id}_transcript.json' transcript_blob_name = f"{video_id}/{transcript_file_name}" # 检查逐字稿是否存在 is_transcript_exists = gcs_check_file_exists(gcs_client, bucket_name, transcript_blob_name) if not is_transcript_exists: # 从YouTube获取逐字稿并上传 transcript = get_transcript(video_id) if transcript: print("成功獲取字幕") else: print("沒有找到字幕") transcript_text = json.dumps(transcript, ensure_ascii=False, indent=2) upload_file_to_gcs_with_json_string(gcs_client, bucket_name, transcript_blob_name, transcript_text) else: # 逐字稿已存在，下载逐字稿内容 print("逐字稿已存在于GCS中") transcript_text = download_blob_to_string(gcs_client, bucket_name, transcript_blob_name) transcript = json.loads(transcript_text) source = "gcs" get_questions(video_id, transcript_text, source) get_video_id_summary(video_id, transcript_text, source) get_mind_map(video_id, transcript_text, source) # 處理截圖 # for entry in transcript: # if 'img_file_id' not in entry: # screenshot_path = screenshot_youtube_video(video_id, entry['start']) # screenshot_blob_name = f"{video_id}/{video_id}_{entry['start']}.jpg" # img_file_id = upload_img_and_get_public_url(gcs_client, bucket_name, screenshot_blob_name, screenshot_path) # entry['img_file_id'] = img_file_id # print(f"截图已上传到GCS: {img_file_id}") # 更新逐字稿文件 print("===更新逐字稿文件===") print(transcript) print("===更新逐字稿文件===") updated_transcript_text = json.dumps(transcript, ensure_ascii=False, indent=2) upload_file_to_gcs_with_json_string(gcs_client, bucket_name, transcript_blob_name, updated_transcript_text) print("逐字稿已更新，包括截图链接") updated_transcript_json = json.loads(updated_transcript_text) return updated_transcript_json def process_youtube_link(link): # 使用 YouTube API 获取逐字稿 # 假设您已经获取了 YouTube 视频的逐字稿并存储在变量 `transcript` 中 video_id = extract_youtube_id(link) global VIDEO_ID VIDEO_ID = video_id download_youtube_video(video_id, output_path=OUTPUT_PATH) try: # transcript = process_transcript_and_screenshots(video_id) transcript = process_transcript_and_screenshots_on_gcs(video_id) except Exception as e: error_msg = f" {video_id} 逐字稿錯誤: {str(e)}" print("===process_youtube_link error===") print(error_msg) raise gr.Error(error_msg) formatted_transcript = [] formatted_simple_transcript =[] screenshot_paths = [] for entry in transcript: start_time = format_seconds_to_time(entry['start']) end_time = format_seconds_to_time(entry['start'] + entry['duration']) embed_url = get_embedded_youtube_link(video_id, entry['start']) # img_file_id = entry['img_file_id'] img_file_id ="" # 先取消 Google Drive 的图片 # screenshot_path = f"https://lh3.googleusercontent.com/d/{img_file_id}=s4000" screenshot_path = img_file_id line = { "start_time": start_time, "end_time": end_time, "text": entry['text'], "embed_url": embed_url, "screenshot_path": screenshot_path } formatted_transcript.append(line) # formatted_simple_transcript 只要 start_time, end_time, text simple_line = { "start_time": start_time, "end_time": end_time, "text": entry['text'] } formatted_simple_transcript.append(simple_line) screenshot_paths.append(screenshot_path) global TRANSCRIPTS TRANSCRIPTS = formatted_transcript # 基于逐字稿生成其他所需的输出 source = "gcs" questions = get_questions(video_id, formatted_simple_transcript, source) formatted_transcript_json = json.dumps(formatted_transcript, ensure_ascii=False, indent=2) summary_json = get_video_id_summary(video_id, formatted_simple_transcript, source) summary = summary_json["summary"] html_content = format_transcript_to_html(formatted_transcript) simple_html_content = format_simple_transcript_to_html(formatted_simple_transcript) first_image = formatted_transcript[0]['screenshot_path'] first_text = formatted_transcript[0]['text'] mind_map_json = get_mind_map(video_id, formatted_simple_transcript, source) mind_map = mind_map_json["mind_map"] mind_map_html = get_mind_map_html(mind_map) # 确保返回与 UI 组件预期匹配的输出 return video_id, \ questions[0] if len(questions) > 0 else "", \ questions[1] if len(questions) > 1 else "", \ questions[2] if len(questions) > 2 else "", \ formatted_transcript_json, \ summary, \ mind_map, \ mind_map_html, \ html_content, \ simple_html_content, \ first_image, \ first_text, def format_transcript_to_html(formatted_transcript): html_content = "" for entry in formatted_transcript: html_content += f"

{entry['start_time']} - {entry['end_time']}

" html_content += f"

{entry['text']}

" html_content += f"" return html_content def format_simple_transcript_to_html(formatted_transcript): html_content = "" for entry in formatted_transcript: html_content += f"

{entry['start_time']} - {entry['end_time']}

" html_content += f"

{entry['text']}

" return html_content def get_embedded_youtube_link(video_id, start_time): int_start_time = int(start_time) embed_url = f"https://www.youtube.com/embed/{video_id}?start={int_start_time}&autoplay=1" return embed_url def download_youtube_video(youtube_id, output_path=OUTPUT_PATH): # Construct the full YouTube URL youtube_url = f'https://www.youtube.com/watch?v={youtube_id}' # Create the output directory if it doesn't exist if not os.path.exists(output_path): os.makedirs(output_path) # Download the video yt = YouTube(youtube_url) video_stream = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first() video_stream.download(output_path=output_path, filename=youtube_id+".mp4") print(f"Video downloaded successfully: {output_path}/{youtube_id}.mp4") def screenshot_youtube_video(youtube_id, snapshot_sec): video_path = f'{OUTPUT_PATH}/{youtube_id}.mp4' file_name = f"{youtube_id}_{snapshot_sec}.jpg" with VideoFileClip(video_path) as video: screenshot_path = f'{OUTPUT_PATH}/{file_name}' video.save_frame(screenshot_path, snapshot_sec) return screenshot_path def process_web_link(link): # 抓取和解析网页内容 response = requests.get(link) soup = BeautifulSoup(response.content, 'html.parser') return soup.get_text() def get_mind_map(video_id, df_string, source): if source == "gcs": print("===get_mind_map on gcs===") gcs_client = init_gcs_client(GCS_KEY) bucket_name = 'video_ai_assistant' file_name = f'{video_id}_mind_map.json' blob_name = f"{video_id}/{file_name}" # 检查檔案是否存在 is_file_exists = gcs_check_file_exists(gcs_client, bucket_name, blob_name) if not is_file_exists: mind_map = generate_mind_map(df_string) mind_map_json = {"mind_map": str(mind_map)} mind_map_text = json.dumps(mind_map_json, ensure_ascii=False, indent=2) upload_file_to_gcs_with_json_string(gcs_client, bucket_name, blob_name, mind_map_text) print("mind_map已上傳到GCS") else: # mindmap已存在，下载内容 print("mind_map已存在于GCS中") mind_map_text = download_blob_to_string(gcs_client, bucket_name, blob_name) mind_map_json = json.loads(mind_map_text) elif source == "drive": print("===get_mind_map on drive===") service = init_drive_service() parent_folder_id = '1GgI4YVs0KckwStVQkLa1NZ8IpaEMurkL' folder_id = create_folder_if_not_exists(service, video_id, parent_folder_id) file_name = f'{video_id}_mind_map.json' # 检查檔案是否存在 exists, file_id = check_file_exists(service, folder_id, file_name) if not exists: mind_map = generate_mind_map(df_string) mind_map_json = {"mind_map": str(mind_map)} mind_map_text = json.dumps(mind_map_json, ensure_ascii=False, indent=2) upload_content_directly(service, file_name, folder_id, mind_map_text) print("mind_map已上傳到Google Drive") else: # mindmap已存在，下载内容 print("mind_map已存在于Google Drive中") mind_map_text = download_file_as_string(service, file_id) mind_map_json = json.loads(mind_map_text) return mind_map_json def generate_mind_map(df_string): # 使用 OpenAI 生成基于上传数据的问题 sys_content = "你是一個擅長資料分析跟影片教學的老師，user 為學生，請精讀資料文本，自行判斷資料的種類，使用 zh-TW" user_content = f""" 請根據 {df_string} 文本建立 markdown 心智圖 注意：不需要前後文敘述，直接給出 markdown 文本即可 這對我很重要 """ messages = [ {"role": "system", "content": sys_content}, {"role": "user", "content": user_content} ] request_payload = { "model": "gpt-4-1106-preview", "messages": messages, "max_tokens": 4000, } response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) mind_map = response.choices[0].message.content.strip() print("=====mind_map=====") print(mind_map) print("=====mind_map=====") return mind_map def get_mind_map_html(mind_map): mind_map_markdown = mind_map.replace("```markdown", "").replace("```", "") mind_map_html = f"""
""" return mind_map_html def processed_video_summary_to_json(summary): """ 整體格式為： 1. 內容類型 2. 整體摘要 3. 條列式重點 4. 關鍵時刻（段落摘要） 5. 結論反思（為什麼我們要學這個？） 6. 延伸小問題 使用 regex 拆解 summary 抓取各個部分 example: 1. 內容類型：影片類型\n\n2. 整體摘要\n本段影片透過一組劇情式的場景講述，描述了一群人物進行VR教育體驗的故事，涵蓋了冒險、探索、學習和救援等元素。影片同時充分融合了互動問答和地理科學知識，並對南極和北極的地理環境、生態系統以及國際政治局勢進行了介紹。\n\n3. 條列式重點\n- VR教育體驗的場景設置。\n- 冒險遊戲中融入地理科學知識。\n- 南北極的環境差異和重要性。\n- 介紹了南極條約的內容。\n- 探討全球暖化對極地生物的影響。\n\n4. 關鍵時刻（段落摘要）\n【00:00:05 - 00:00:21】: 一群人物於周末下午前往VR教育體驗館，選擇了\"極地探險\"遊戲，透過體感裝置體驗寒冷和震動。\n【00:00:34 - 00:02:00】: 故事背景設置，玩家需解開科學家R的神秘失蹤之謎，在極地嚴酷環境中尋找線索。期間介紹了南極的地理氣候和與英國的關係。\n【00:03:08 - 00:03:23】: 透過與店員互動回答來隱喻人工智慧語言模型的限制，並提示玩家搜集線索進行冒險。\n【00:04:41 - 00:05:03】: 發現北極熊的照片，玩家決定行動至北極，並提供了地形知識問答。\n【00:06:01 - 00:08:11】: 揭露科學家R被綁架的原因，並討論了北極的政治和經濟重要性。最後強調保護環境的重要性。\n\n5. 結論反思（為什麼我們要學這個？）\n通過這個故事情節，學習者不僅能夠體驗虛擬實境的樂趣，也能夠學習到關於地理、生態、以及環境保護的知識，提高對全球環境議題的認知和理解。它教導我們通過娛樂來學習如何關懷地球的未來，同時也啟發了對於科學研究與國際政治的基本認知。\n\n6. 延伸小問題\n- 你認為VR遊戲在教育上有哪些潛力？\n- 與南極相比，為什麼北極會成為各國政治和經濟角力的場所？\n- 全球暖化對極地動物的生存造成了哪些影響？我們能做些什麼來幫助改善這種情況？ """ # 1. 內容類型 -> 針對 1. 內容類型：？ 進行處理 content_type = summary.split("1. 內容類型：")[1].split("\n")[0].strip() # 2. 整體摘要 overall_summary = summary.split("2. 整體摘要")[1].split("\n\n")[1].strip() # 3. 條列式重點 key_points = summary.split("3. 條列式重點")[1].split("\n\n")[1].strip() # 4. 關鍵時刻（段落摘要） key_moments = summary.split("4. 關鍵時刻（段落摘要）")[1].split("\n\n")[1].strip() # 5. 結論反思（為什麼我們要學這個？） conclusion_reflection = summary.split("5. 結論反思（為什麼我們要學這個？）")[1].split("\n\n")[1].strip() # 6. 延伸小問題 extension_questions = summary.split("6. 延伸小問題")[1].split("\n\n")[1].strip() summary_json = { "content_type": content_type, "overall_summary": overall_summary, "key_points": key_points, "key_moments": key_moments, "conclusion_reflection": conclusion_reflection, "extension_questions": extension_questions } print("===processed_video_summary_to_json===") print(summary_json) print("===processed_video_summary_to_json===") return summary_json # get video_id_summary.json content def get_video_id_summary(video_id, df_string, source): if source == "gcs": print("===get_video_id_summary on gcs===") gcs_client = init_gcs_client(GCS_KEY) bucket_name = 'video_ai_assistant' file_name = f'{video_id}_summary.json' summary_file_blob_name = f"{video_id}/{file_name}" # 检查 summary_file 是否存在 is_summary_file_exists = gcs_check_file_exists(gcs_client, bucket_name, summary_file_blob_name) if not is_summary_file_exists: summary = generate_summarise(df_string) summary_json = {"summary": str(summary)} summary_text = json.dumps(summary_json, ensure_ascii=False, indent=2) upload_file_to_gcs_with_json_string(gcs_client, bucket_name, summary_file_blob_name, summary_text) print("summary已上传到GCS") else: # summary已存在，下载内容 print("summary已存在于GCS中") summary_text = download_blob_to_string(gcs_client, bucket_name, summary_file_blob_name) summary_json = json.loads(summary_text) elif source == "drive": print("===get_video_id_summary===") service = init_drive_service() parent_folder_id = '1GgI4YVs0KckwStVQkLa1NZ8IpaEMurkL' folder_id = create_folder_if_not_exists(service, video_id, parent_folder_id) file_name = f'{video_id}_summary.json' # 检查逐字稿是否存在 exists, file_id = check_file_exists(service, folder_id, file_name) if not exists: summary = generate_summarise(df_string) summary_json = {"summary": str(summary)} summary_text = json.dumps(summary_json, ensure_ascii=False, indent=2) try: upload_content_directly(service, file_name, folder_id, summary_text) print("summary已上傳到Google Drive") except Exception as e: error_msg = f" {video_id} 摘要錯誤: {str(e)}" print("===get_video_id_summary error===") print(error_msg) print("===get_video_id_summary error===") else: # 逐字稿已存在，下载逐字稿内容 print("summary已存在Google Drive中") summary_text = download_file_as_string(service, file_id) summary_json = json.loads(summary_text) return summary_json def generate_summarise(df_string): # 使用 OpenAI 生成基于上传数据的问题 sys_content = "你是一個擅長資料分析跟影片教學的老師，user 為學生，請精讀資料文本，自行判斷資料的種類，使用 zh-TW" user_content = f""" 請根據 {df_string}，判斷這份文本 如果是資料類型，請提估欄位敘述、資料樣態與資料分析，告訴學生這張表的意義，以及可能的結論與對應方式 如果是影片類型，請提估影片內容，告訴學生這部影片的意義， 小範圍切出不同段落的相對應時間軸的重點摘要，最多不超過五段 注意不要遺漏任何一段時間軸的內容 格式為 【start - end】: 摘要 以及可能的結論與結尾延伸小問題提供學生作反思 整體格式為： 🗂️ 1. 內容類型：？ 📚 2. 整體摘要 🔖 3. 條列式重點 🔑 4. 關鍵時刻（段落摘要） 💡 5. 結論反思（為什麼我們要學這個？） ❓ 6. 延伸小問題 """ # 🗂️ 1. 內容類型：？ # 📚 2. 整體摘要 # 🔖 3. 條列式重點 # 🔑 4. 關鍵時刻（段落摘要） # 💡 5. 結論反思（為什麼我們要學這個？） # ❓ 6. 延伸小問題 messages = [ {"role": "system", "content": sys_content}, {"role": "user", "content": user_content} ] request_payload = { "model": "gpt-4-turbo-preview", "messages": messages, "max_tokens": 4000, } response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) df_summarise = response.choices[0].message.content.strip() print("=====df_summarise=====") print(df_summarise) print("=====df_summarise=====") return df_summarise def generate_questions(df_string): # 使用 OpenAI 生成基于上传数据的问题 sys_content = "你是一個擅長資料分析跟影片教學的老師，user 為學生，請精讀資料文本，自行判斷資料的種類，並用既有資料為本質猜測用戶可能會問的問題，使用 zh-TW" user_content = f"請根據 {df_string} 生成三個問題，並用 JSON 格式返回 questions:[q1的敘述text, q2的敘述text, q3的敘述text]" messages = [ {"role": "system", "content": sys_content}, {"role": "user", "content": user_content} ] response_format = { "type": "json_object" } print("=====messages=====") print(messages) print("=====messages=====") request_payload = { "model": "gpt-4-1106-preview", "messages": messages, "max_tokens": 4000, "response_format": response_format } response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) questions = json.loads(response.choices[0].message.content)["questions"] print("=====json_response=====") print(questions) print("=====json_response=====") return questions def get_questions(video_id, df_string, source="gcs"): if source == "gcs": # 去 gcs 確認是有有 video_id_questions.json print("===get_questions on gcs===") gcs_client = init_gcs_client(GCS_KEY) bucket_name = 'video_ai_assistant' file_name = f'{video_id}_questions.json' blob_name = f"{video_id}/{file_name}" # 检查檔案是否存在 is_questions_exists = gcs_check_file_exists(gcs_client, bucket_name, blob_name) if not is_questions_exists: questions = generate_questions(df_string) questions_text = json.dumps(questions, ensure_ascii=False, indent=2) upload_file_to_gcs_with_json_string(gcs_client, bucket_name, blob_name, questions_text) print("questions已上傳到GCS") else: # 逐字稿已存在，下载逐字稿内容 print("questions已存在于GCS中") questions_text = download_blob_to_string(gcs_client, bucket_name, blob_name) questions = json.loads(questions_text) elif source == "drive": # 去 g drive 確認是有有 video_id_questions.json print("===get_questions===") service = init_drive_service() parent_folder_id = '1GgI4YVs0KckwStVQkLa1NZ8IpaEMurkL' folder_id = create_folder_if_not_exists(service, video_id, parent_folder_id) file_name = f'{video_id}_questions.json' # 检查檔案是否存在 exists, file_id = check_file_exists(service, folder_id, file_name) if not exists: questions = generate_questions(df_string) questions_text = json.dumps(questions, ensure_ascii=False, indent=2) upload_content_directly(service, file_name, folder_id, questions_text) print("questions已上傳到Google Drive") else: # 逐字稿已存在，下载逐字稿内容 print("questions已存在于Google Drive中") questions_text = download_file_as_string(service, file_id) questions = json.loads(questions_text) q1 = questions[0] if len(questions) > 0 else "" q2 = questions[1] if len(questions) > 1 else "" q3 = questions[2] if len(questions) > 2 else "" print("=====get_questions=====") print(f"q1: {q1}") print(f"q2: {q2}") print(f"q3: {q3}") print("=====get_questions=====") return q1, q2, q3 def change_questions(df_string): questions = generate_questions(df_string) q1 = questions[0] if len(questions) > 0 else "" q2 = questions[1] if len(questions) > 1 else "" q3 = questions[2] if len(questions) > 2 else "" print("=====get_questions=====") print(f"q1: {q1}") print(f"q2: {q2}") print(f"q3: {q3}") print("=====get_questions=====") return q1, q2, q3 def respond(user_message, data, chat_history, socratic_mode=False): print("=== 變數：user_message ===") print(user_message) print("=== 變數：chat_history ===") print(chat_history) data_json = json.loads(data) for entry in data_json: entry.pop('embed_url', None) # Remove 'embed_url' if it exists entry.pop('screenshot_path', None) if socratic_mode: sys_content = f""" 你是一個擅長資料分析跟影片教學的老師，user 為學生 請用 {data} 為資料文本，自行判斷資料的種類， 並進行對話，使用 台灣人的口與表達，及繁體中文zh-TW 如果是影片類型，不用解釋逐字稿格式，直接回答學生問題 請你用蘇格拉底式的提問方式，引導學生思考，並且給予學生一些提示 不要直接給予答案，讓學生自己思考 但可以給予一些提示跟引導，例如給予影片的時間軸，讓學生自己去找答案 如果學生問了一些問題你無法判斷，請告訴學生你無法判斷，並建議學生可以問其他問題 或者你可以問學生一些問題，幫助學生更好的理解資料 如果學生的問題與資料文本無關，請告訴學生你無法回答超出範圍的問題 最後，在你回答的開頭標註【蘇格拉底助教】 """ else: sys_content = f""" 你是一個擅長資料分析跟影片教學的老師，user 為學生 請用 {data} 為資料文本，自行判斷資料的種類， 並進行對話，使用 zh-TW 如果是影片類型，不用解釋逐字稿格式，直接回答學生問題 但可以給予一些提示跟引導，例如給予影片的時間軸，讓學生可以找到相對應的時間點 如果學生問了一些問題你無法判斷，請告訴學生你無法判斷，並建議學生可以問其他問題 或者你可以問學生一些問題，幫助學生更好的理解資料 如果學生的問題與資料文本無關，請告訴學生你無法回答超出範圍的問題 """ messages = [ {"role": "system", "content": sys_content} ] # if chat_history is not none, append role, content to messages # chat_history = [(user, assistant), (user, assistant), ...] # In the list, first one is user, then assistant if chat_history is not None: # 如果超過10則訊息，只保留最後10則訊息 if len(chat_history) > 10: chat_history = chat_history[-10:] for chat in chat_history: old_messages = [ {"role": "user", "content": chat[0]}, {"role": "assistant", "content": chat[1]} ] messages += old_messages else: pass messages.append({"role": "user", "content": user_message}) request_payload = { "model": "gpt-4-1106-preview", "messages": messages, "max_tokens": 4000 # 設定一個較大的值，可根據需要調整 } response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) response_text = response.choices[0].message.content.strip() # 更新聊天历史 new_chat_history = (user_message, response_text) if chat_history is None: chat_history = [new_chat_history] else: chat_history.append(new_chat_history) # 返回聊天历史和空字符串清空输入框 return "", chat_history def chat_with_youtube_transcript(youtube_id, thread_id, trascript, user_message, chat_history, socratic_mode=False): # 先計算 user_message 是否超過 500 個字 if len(user_message) > 1500: error_msg = "你的訊息太長了，請縮短訊息長度至五百字以內" raise gr.Error(error_msg) assistant_id = "asst_kmvZLNkDUYaNkMNtZEAYxyPq" client = OPEN_AI_CLIENT # 從 file 拿逐字稿資料 # instructions = f""" # 你是一個擅長資料分析跟影片教學的老師，user 為學生 # 請根據 assistant beta 的上傳資料 # 如果 file 內有找到 file.content["{youtube_id}"] 為資料文本，自行判斷資料的種類， # 如果沒有資料，請告訴用戶沒有逐字稿資料，但仍然可以進行對話，使用台灣人的口與表達，及繁體中文 zh-TW # 請嚴格執行，只根據 file.content["{youtube_id}"] 為資料文本，沒有就是沒有資料，不要引用其他資料 # 如果是影片類型，不用解釋逐字稿格式，直接回答學生問題 # socratic_mode = {socratic_mode} # 如果 socratic_mode = True， # - 請用蘇格拉底式的提問方式，引導學生思考，並且給予學生一些提示 # - 不要直接給予答案，讓學生自己思考 # - 但可以給予一些提示跟引導，例如給予影片的時間軸，讓學生自己去找答案 # - 在你回答的開頭標註【蘇格拉底助教：{youtube_id} 】 # 如果 socratic_mode = False， # - 直接回答學生問題 # - 在你回答的開頭標註【一般學習精靈：{youtube_id} 】 # 如果學生問了一些問題你無法判斷，請告訴學生你無法判斷，並建議學生可以問其他問題 # 或者你可以反問學生一些問題，幫助學生更好的理解資料 # 如果學生的問題與資料文本無關，請告訴學生你無法回答超出範圍的問題 # 最後只要是參考逐字稿資料，請在回答的最後標註【參考資料：（分）：（秒）】 # """ # 直接安排逐字稿資料 in instructions trascript_json = json.loads(trascript) # 移除 embed_url, screenshot_path for entry in trascript_json: entry.pop('embed_url', None) entry.pop('screenshot_path', None) trascript_text = json.dumps(trascript_json, ensure_ascii=False, indent=2) instructions = f""" 逐字稿資料：{trascript_text} ------------------------------------- 你是一個擅長資料分析跟影片教學的老師，user 為學生 如果是影片類型，不用解釋逐字稿格式，直接回答學生問題 socratic_mode = {socratic_mode} 如果 socratic_mode = True， - 請用蘇格拉底式的提問方式，引導學生思考，並且給予學生一些提示 - 不要直接給予答案，讓學生自己思考 - 但可以給予一些提示跟引導，例如給予影片的時間軸，讓學生自己去找答案 - 在你回答的開頭標註【蘇格拉底助教：{youtube_id} 】 如果 socratic_mode = False， - 直接回答學生問題 - 在你回答的開頭標註【一般學習精靈：{youtube_id} 】 如果學生問了一些問題你無法判斷，請告訴學生你無法判斷，並建議學生可以問其他問題 或者你可以反問學生一些問題，幫助學生更好的理解資料 如果學生的問題與資料文本無關，請告訴學生你無法回答超出範圍的問題 最後只要是參考逐字稿資料，請在回答的最後標註【參考資料：（分）：（秒）】 """ # 创建线程 if not thread_id: thread = client.beta.threads.create() thread_id = thread.id else: thread = client.beta.threads.retrieve(thread_id) # 向线程添加用户的消息 client.beta.threads.messages.create( thread_id=thread.id, role="user", content=user_message ) # 运行助手，生成响应 run = client.beta.threads.runs.create( thread_id=thread.id, assistant_id=assistant_id, instructions=instructions, ) # 等待助手响应，设定最大等待时间为 30 秒 run_status = poll_run_status(run.id, thread.id, timeout=30) # 获取助手的响应消息 if run_status == "completed": messages = client.beta.threads.messages.list(thread_id=thread.id) # [MessageContentText(text=Text(annotations=[], value='您好！有什麼我可以幫助您的嗎？如果有任何問題或需要指導，請隨時告訴我！'), type='text')] response_text = messages.data[0].content[0].text.value else: response_text = "學習精靈有點累，請稍後再試！" # 更新聊天历史 new_chat_history = (user_message, response_text) if chat_history is None: chat_history = [new_chat_history] else: chat_history.append(new_chat_history) # 返回聊天历史和空字符串清空输入框 return "", chat_history, thread.id def poll_run_status(run_id, thread_id, timeout=600, poll_interval=5): """ Polls the status of a Run and handles different statuses appropriately. :param run_id: The ID of the Run to poll. :param thread_id: The ID of the Thread associated with the Run. :param timeout: Maximum time to wait for the Run to complete, in seconds. :param poll_interval: Time to wait between each poll, in seconds. """ client = OPEN_AI_CLIENT start_time = time.time() while time.time() - start_time < timeout: run = client.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run_id) if run.status in ["completed", "cancelled", "failed"]: print(f"Run completed with status: {run.status}") break elif run.status == "requires_action": print("Run requires action. Performing required action...") # Here, you would perform the required action, e.g., running functions # and then submitting the outputs. This is simplified for this example. # After performing the required action, you'd complete the action: # OPEN_AI_CLIENT.beta.threads.runs.complete_required_action(...) elif run.status == "expired": print("Run expired. Exiting...") break else: print(f"Run status is {run.status}. Waiting for updates...") time.sleep(poll_interval) else: print("Timeout reached. Run did not complete in the expected time.") # Once the Run is completed, handle the result accordingly if run.status == "completed": # Retrieve and handle messages or run steps as needed messages = client.beta.threads.messages.list(thread_id=thread_id) for message in messages.data: if message.role == "assistant": print(f"Assistant response: {message.content}") elif run.status in ["cancelled", "failed"]: # Handle cancellation or failure print(f"Run ended with status: {run.status}") elif run.status == "expired": # Handle expired run print("Run expired without completion.") return run.status def update_slide(direction): global TRANSCRIPTS global CURRENT_INDEX print("=== 更新投影片 ===") print(f"CURRENT_INDEX: {CURRENT_INDEX}") # print(f"TRANSCRIPTS: {TRANSCRIPTS}") CURRENT_INDEX += direction if CURRENT_INDEX < 0: CURRENT_INDEX = 0 # 防止索引小于0 elif CURRENT_INDEX >= len(TRANSCRIPTS): CURRENT_INDEX = len(TRANSCRIPTS) - 1 # 防止索引超出范围 # 获取当前条目的文本和截图 URL current_transcript = TRANSCRIPTS[CURRENT_INDEX] slide_image = current_transcript["screenshot_path"] slide_text = current_transcript["text"] return slide_image, slide_text def prev_slide(): return update_slide(-1) def next_slide(): return update_slide(1) HEAD = """ """ with gr.Blocks() as demo: with gr.Row(): with gr.Column(scale=2): file_upload = gr.File(label="Upload your CSV or Word file", visible=False) youtube_link = gr.Textbox(label="Enter YouTube Link") video_id = gr.Textbox(label="video_id", visible=False) youtube_link_btn = gr.Button("Submit_YouTube_Link") web_link = gr.Textbox(label="Enter Web Page Link", visible=False) chatbot = gr.Chatbot() thread_id = gr.Textbox(label="thread_id", visible=False) socratic_mode_btn = gr.Checkbox(label="蘇格拉底家教助理模式", value=False) msg = gr.Textbox(label="Message") send_button = gr.Button("Send") with gr.Column(scale=3): with gr.Tab("圖文"): transcript_html = gr.HTML(label="YouTube Transcript and Video") with gr.Tab("投影片"): slide_image = gr.Image() slide_text = gr.Textbox() with gr.Row(): prev_button = gr.Button("Previous") next_button = gr.Button("Next") prev_button.click(fn=prev_slide, inputs=[], outputs=[slide_image, slide_text]) next_button.click(fn=next_slide, inputs=[], outputs=[slide_image, slide_text]) with gr.Tab("逐字稿"): simple_html_content = gr.HTML(label="Simple Transcript") with gr.Tab("本文"): df_string_output = gr.Textbox(lines=40, label="Data Text") with gr.Tab("重點"): df_summarise = gr.Textbox(container=True, show_copy_button=True, lines=40) with gr.Tab("問題"): gr.Markdown("## 常用問題") btn_1 = gr.Button() btn_2 = gr.Button() btn_3 = gr.Button() gr.Markdown("## 重新生成問題") btn_create_question = gr.Button("Create Questions") with gr.Tab("markdown"): gr.Markdown("## 請複製以下 markdown 並貼到你的心智圖工具中，建議使用：https://markmap.js.org/repl") mind_map = gr.Textbox(container=True, show_copy_button=True, lines=40, elem_id="mind_map_markdown") with gr.Tab("心智圖",elem_id="mind_map_tab"): mind_map_html = gr.HTML() with gr.Row(): gr.Markdown("## 教育評量饗宴") with gr.Row(): with gr.Column(scale=2): with gr.Tab("認知階層評量題目"): cognitive_level_content = gr.Textbox(label="輸入學習目標與內容") cognitive_level_content_btn = gr.Button("生成評量題目") with gr.Tab("素養導向閱讀題組"): literacy_oriented_reading_content = gr.Textbox(label="輸入閱讀材料") literacy_oriented_reading_content_btn = gr.Button("生成閱讀理解題") with gr.Tab("學習單"): worksheet_content = gr.Textbox(label="輸入學習單內容") worksheet_content_btn = gr.Button("生成學習單") with gr.Tab("自我評估"): self_assessment_content = gr.Textbox(label="輸入自評問卷或檢查表") self_assessment_content_btn = gr.Button("生成自評問卷") with gr.Tab("自我反思評量"): self_reflection_content = gr.Textbox(label="輸入自我反思活動") self_reflection_content_btn = gr.Button("生成自我反思活動") with gr.Tab("後設認知"): metacognition_content = gr.Textbox(label="輸入後設認知相關問題") metacognition_content_btn = gr.Button("生成後設認知問題") with gr.Column(scale=3): # 生成對應不同模式的結果 exam_result = gr.Textbox("生成結果") # 傳統模式 # send_button.click( # respond, # inputs=[msg, df_string_output, chatbot, socratic_mode_btn], # outputs=[msg, chatbot] # ) # # 连接按钮点击事件 # btn_1.click(respond, inputs=[btn_1, df_string_output, chatbot, socratic_mode_btn], outputs=[msg, chatbot]) # btn_2.click(respond, inputs=[btn_2, df_string_output, chatbot, socratic_mode_btn], outputs=[msg, chatbot]) # btn_3.click(respond, inputs=[btn_3, df_string_output, chatbot, socratic_mode_btn], outputs=[msg, chatbot]) # chat_with_youtube_transcript send_button.click( chat_with_youtube_transcript, inputs=[video_id, thread_id, df_string_output, msg, chatbot, socratic_mode_btn], outputs=[msg, chatbot, thread_id] ) # 连接按钮点击事件 btn_1.click( chat_with_youtube_transcript, inputs=[video_id, thread_id, df_string_output, btn_1, chatbot, socratic_mode_btn], outputs=[msg, chatbot, thread_id] ) btn_2.click( chat_with_youtube_transcript, inputs=[video_id, thread_id, df_string_output, btn_2, chatbot, socratic_mode_btn], outputs=[msg, chatbot, thread_id] ) btn_3.click( chat_with_youtube_transcript, inputs=[video_id, thread_id, df_string_output, btn_3, chatbot, socratic_mode_btn], outputs=[msg, chatbot, thread_id] ) btn_create_question.click(change_questions, inputs = [df_string_output], outputs = [btn_1, btn_2, btn_3]) # file_upload.change(process_file, inputs=file_upload, outputs=df_string_output) file_upload.change(process_file, inputs=file_upload, outputs=[btn_1, btn_2, btn_3, df_summarise, df_string_output]) # 当输入 YouTube 链接时触发 youtube_link.change( process_youtube_link, inputs=youtube_link, outputs=[ video_id, btn_1, btn_2, btn_3, df_string_output, df_summarise, mind_map, mind_map_html, transcript_html, simple_html_content, slide_image, slide_text ] ) youtube_link_btn.click( process_youtube_link, inputs=youtube_link, outputs=[ video_id, btn_1, btn_2, btn_3, df_string_output, df_summarise, mind_map, mind_map_html, transcript_html, simple_html_content, slide_image, slide_text ] ) # 当输入网页链接时触发 # web_link.change(process_web_link, inputs=web_link, outputs=[btn_1, btn_2, btn_3, df_summarise, df_string_output]) demo.launch(allowed_paths=["videos"])