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# 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("# 多功能Gradio应用")
    
    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")
            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)
            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()