# import gradio as gr # import json, os # import urllib.request # # Replace 'YOUR_API_KEY' with your actual YouTube Data API key # API_KEY = os.getenv('api_key') # def get_youtube_id(youtube_url): # if 'youtube.com' in youtube_url: # video_id = youtube_url.split('v=')[-1] # elif 'youtu.be' in youtube_url: # video_id = youtube_url.split('/')[-1].split('?')[0] # return video_id # def check_cc_license(youtube_url): # # Extract video ID from the URL # video_id = get_youtube_id(youtube_url) # # YouTube Data API URL to get video details # api_url = f'https://www.googleapis.com/youtube/v3/videos?id={video_id}&part=status&key={API_KEY}' # try: # # Fetch video details # response = urllib.request.urlopen(api_url) # data = json.load(response) # # Check the license status # for item in data['items']: # if item['status']['license'] == 'creativeCommon': # return f"Yes." # else: # return f"No." # except Exception as e: # return f"An error occurred: {str(e)}" # # Gradio interface # interface = gr.Interface( # fn=check_cc_license, # inputs=gr.Textbox(label="YouTube Video URL"), # outputs=gr.Textbox(label="Creative Commons license?") # ) # if __name__ == "__main__": # interface.launch() # import gradio as gr # import asyncio # import os # from openai import AsyncOpenAI # # 从您的模块中导入必要的函数 # from utils.generate_distractors import prepare_q_inputs, construct_prompt_textonly, generate_distractors # from utils.api_utils import generate_from_openai_chat_completion # # 修改generate_distractors函数，使其成为异步函数 # # 假设generate_distractors函数定义在您的模块中，我们需要修改它 # # 如果无法修改原始模块，请在此处重新定义 # async def generate_distractors_async(model_name: str, # queries: list, # n: int=1, # max_tokens: int=4096): # assert model_name in ["gpt-4o-mini", "gpt-4-turbo", "gpt-4o", "gpt-4o-2024-08-06"], "Invalid model name" # client = AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY"), base_url="https://yanlp.zeabur.app/v1") # messages = prepare_q_inputs(queries) # # 直接等待协程而不是使用asyncio.run() # responses = await generate_from_openai_chat_completion( # client, # messages=messages, # engine_name=model_name, # n=n, # max_tokens=max_tokens, # requests_per_minute=30, # json_format=True # ) # for query, response in zip(queries, responses): # new_options = response # if new_options and "distractors" in new_options: # query["option_5"] = new_options["distractors"].get("E", "") # query["option_6"] = new_options["distractors"].get("F", "") # query["option_7"] = new_options["distractors"].get("G", "") # query["distractor_analysis"] = new_options["distractors"].get("analysis_of_distractors", "") # else: # query["option_5"] = "" # query["option_6"] = "" # query["option_7"] = "" # query["distractor_analysis"] = "" # return queries # # 定义异步处理函数 # async def generate_distractors_gradio(question, option1, option2, option3, option4, answer, answer_analysis): # query = { # 'question': question, # 'option_1': option1, # 'option_2': option2, # 'option_3': option3, # 'option_4': option4, # 'answer': answer, # 'answer_analysis': answer_analysis # } # queries = [query] # 因为函数期望的是一个列表 # # 调用异步生成干扰项的函数 # results = await generate_distractors_async( # model_name="gpt-4o-mini", # queries=queries, # n=1, # max_tokens=4096 # ) # # 提取结果 # result = results[0] # new_options = { # 'E': result.get('option_5', ''), # 'F': result.get('option_6', ''), # 'G': result.get('option_7', '') # } # distractor_analysis = result.get('distractor_analysis', '') # # 返回新的干扰项和分析 # return new_options, distractor_analysis # # 创建Gradio界面 # with gr.Blocks() as demo: # gr.Markdown("# 多项选择题干扰项生成器") # with gr.Row(): # question_input = gr.Textbox(label="问题", lines=2) # with gr.Row(): # option1_input = gr.Textbox(label="选项A") # option2_input = gr.Textbox(label="选项B") # with gr.Row(): # option3_input = gr.Textbox(label="选项C") # option4_input = gr.Textbox(label="选项D") # with gr.Row(): # answer_input = gr.Textbox(label="正确答案") # with gr.Row(): # answer_analysis_input = gr.Textbox(label="答案解析", lines=3) # with gr.Row(): # generate_button = gr.Button("生成干扰项") # with gr.Row(): # output_options = gr.JSON(label="生成的干扰选项") # with gr.Row(): # output_analysis = gr.Textbox(label="干扰项解析", lines=5) # # 定义按钮点击事件，注意这里不需要修改，Gradio会自动处理异步函数 # generate_button.click( # fn=generate_distractors_gradio, # inputs=[question_input, option1_input, option2_input, option3_input, option4_input, answer_input, answer_analysis_input], # outputs=[output_options, output_analysis] # ) # # 运行Gradio应用 # demo.launch() import gradio as gr import asyncio import os import json import urllib.request from openai import AsyncOpenAI # 第一个功能：检查YouTube视频是否具有Creative Commons许可证 # 请确保在环境变量中设置了您的YouTube Data API密钥 API_KEY = "AIzaSyDyPpkFRUpUuSMQbhxwTFxCBLK5qTHU-ms" os.environ["OPENAI_API_KEY"] = "sk-h8rybT55JORrT40y2397A06c8f2444F8Ad49407fE85eBf76" def get_youtube_id(youtube_url): if 'youtube.com' in youtube_url: video_id = youtube_url.split('v=')[-1] video_id = video_id.split('&')[0] # 移除可能的额外参数 elif 'youtu.be' in youtube_url: video_id = youtube_url.split('/')[-1].split('?')[0] else: video_id = '' return video_id def check_cc_license(youtube_url): # 从URL中提取视频ID video_id = get_youtube_id(youtube_url) if not video_id: return "Invalid YouTube URL." # YouTube Data API URL，用于获取视频详情 api_url = f'https://www.googleapis.com/youtube/v3/videos?id={video_id}&part=status&key={API_KEY}' try: # 获取视频详情 response = urllib.request.urlopen(api_url) data = json.load(response) # 检查许可证状态 if 'items' in data and len(data['items']) > 0: item = data['items'][0] if item['status']['license'] == 'creativeCommon': return "Yes." else: return "No." else: return "Video not found." except Exception as e: return f"An error occurred: {str(e)}" # 第二个功能：为多项选择题生成干扰项 # 从您的模块中导入必要的函数 from utils.generate_distractors import prepare_q_inputs, construct_prompt_textonly, generate_distractors from utils.api_utils import generate_from_openai_chat_completion # 修改generate_distractors函数，使其成为异步函数 # 假设generate_distractors函数定义在您的模块中，我们需要修改它 # 如果无法修改原始模块，请在此处重新定义 async def generate_distractors_async(model_name: str, queries: list, n: int=1, max_tokens: int=4096): assert model_name in ["gpt-4o-mini", "gpt-4-turbo", "gpt-4o", "gpt-4o-2024-08-06"], "Invalid model name" client = AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY"), base_url="https://yanlp.zeabur.app/v1") messages = prepare_q_inputs(queries) # 直接等待协程而不是使用asyncio.run() responses = await generate_from_openai_chat_completion( client, messages=messages, engine_name=model_name, n=n, max_tokens=max_tokens, requests_per_minute=30, json_format=True ) for query, response in zip(queries, responses): new_options = response if new_options and "distractors" in new_options: query["option_5"] = new_options["distractors"].get("E", "") query["option_6"] = new_options["distractors"].get("F", "") query["option_7"] = new_options["distractors"].get("G", "") query["distractor_analysis"] = new_options["distractors"].get("analysis_of_distractors", "") else: query["option_5"] = "" query["option_6"] = "" query["option_7"] = "" query["distractor_analysis"] = "" return queries # 定义异步处理函数 async def generate_distractors_gradio(question, option1, option2, option3, option4, answer, answer_analysis): query = { 'question': question, 'option_1': option1, 'option_2': option2, 'option_3': option3, 'option_4': option4, 'answer': answer, 'answer_analysis': answer_analysis } queries = [query] # 因为函数期望的是一个列表 # 调用异步生成干扰项的函数 results = await generate_distractors_async( model_name="gpt-4o-mini", queries=queries, n=1, max_tokens=4096 ) # 提取结果 result = results[0] new_options = { 'E': result.get('option_5', ''), 'F': result.get('option_6', ''), 'G': result.get('option_7', '') } distractor_analysis = result.get('distractor_analysis', '') # 返回新的干扰项和分析 return new_options, distractor_analysis with gr.Blocks() as demo: gr.Markdown("# CC检查器和干扰项生成器") with gr.Tabs(): with gr.TabItem("YouTube Creative Commons检查器"): gr.Markdown("## 检查YouTube视频是否具有Creative Commons许可证") youtube_url_input = gr.Textbox(label="YouTube视频URL") cc_license_output = gr.Textbox(label="是否为Creative Commons许可证？") check_button = gr.Button("检查许可证") check_button.click( fn=check_cc_license, inputs=youtube_url_input, outputs=cc_license_output ) with gr.TabItem("多项选择题干扰项生成器"): gr.Markdown("## 为多项选择题生成干扰项") with gr.Row(): question_input = gr.Textbox(label="问题", lines=2) with gr.Row(): option1_input = gr.Textbox(label="选项A") option2_input = gr.Textbox(label="选项B") with gr.Row(): option3_input = gr.Textbox(label="选项C") option4_input = gr.Textbox(label="选项D(可以输入也可以不输入)") with gr.Row(): answer_input = gr.Textbox(label="正确答案（A/B/C/D）") with gr.Row(): answer_analysis_input = gr.Textbox(label="答案解析", lines=3) generate_button = gr.Button("生成干扰项") output_options = gr.JSON(label="生成的干扰选项") output_analysis = gr.Textbox(label="干扰项解析", lines=5) generate_button.click( fn=generate_distractors_gradio, inputs=[question_input, option1_input, option2_input, option3_input, option4_input, answer_input, answer_analysis_input], outputs=[output_options, output_analysis] ) # 运行Gradio应用 demo.launch()