app.py

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 moviepy.editor import VideoFileClip
from pytube import YouTube
import os

OUTPUT_PATH = 'videos'


OPEN_AI_KEY = os.getenv("OPEN_AI_KEY")
client = OpenAI(api_key=OPEN_AI_KEY)

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)
    df_summarise = generate_df_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 "", \
           df_summarise, \
           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 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 = link.split("=")[-1]
    transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=['zh-TW'])
    # 基于逐字稿生成其他所需的输出
    questions = generate_questions(transcript)
    df_summarise = generate_df_summarise(transcript)

    formatted_transcript = []
    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'])
        line = {
            "start_time": start_time,
            "end_time": end_time,
            "text": entry['text'],
            "embed_url": embed_url,
            "time_sec": entry['start']
        }
        formatted_transcript.append(line)
    
    html_content = format_transcript_to_html(formatted_transcript)
    print("=====html_content=====")
    print(html_content)
    print("=====html_content=====")

    # 确保返回与 UI 组件预期匹配的输出
    return questions[0] if len(questions) > 0 else "", \
            questions[1] if len(questions) > 1 else "", \
            questions[2] if len(questions) > 2 else "", \
            df_summarise, \
            html_content

def format_transcript_to_html(formatted_transcript):
    html_content = ""
    for entry in formatted_transcript:
        html_content += f"<h3>{entry['start_time']} - {entry['end_time']}</h3>"
        html_content += f"<p>{entry['text']}</p>"
        html_content += f"<p>{entry['time_sec']} </p>  <br><br>"
    return html_content

def get_embedded_youtube_link(video_id, start_time):
    embed_url = f"https://www.youtube.com/embed/{video_id}?start={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):
    # 由于在 Hugging Face Spaces 中直接下载 YouTube 视频可能不可行，
    # 您可能需要调整这部分代码，例如允许用户上传视频。
    # ... 代码 ...

    # 这里假设视频已经在适当的位置
    video_path = f'{OUTPUT_PATH}/{youtube_id}.mp4'

    # Load the video and take a screenshot
    with VideoFileClip(video_path) as video:
        screenshot_path = f'{OUTPUT_PATH}/screenshots/{youtube_id}_{snapshot_sec}.jpg'
        video.save_frame(screenshot_path, snapshot_sec)
    
    return screenshot_path

def process_video(youtube_id):
    download_youtube_video(youtube_id)
    video_path = f'{OUTPUT_PATH}/{youtube_id}.mp4'
    video = VideoFileClip(video_path)
    duration = int(video.duration)
    output_path = f'{OUTPUT_PATH}/screenshots/{youtube_id}'
    os.makedirs(output_path, exist_ok=True)

    #  fake duration
    duration = 10

    screenshot_paths = []
    for i in range(1, duration):
        screenshot_path = screenshot_youtube_video(youtube_id, i)
        screenshot_paths.append(screenshot_path)

    return screenshot_paths

def get_screenshot_from_video(video_link, start_time):
    # 实现从视频中提取帧的逻辑
    # 由于这需要服务器端处理，你可能需要一种方法来下载视频，
    # 并使用 ffmpeg 或类似工具提取特定时间点的帧
    # 这里只是一个示意性的函数实现
    screenshot_url = f"[逻辑以提取视频 {video_link} 在 {start_time} 秒时的截图]"
    return screenshot_url

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 generate_df_summarise(df_string):
    # 使用 OpenAI 生成基于上传数据的问题
    sys_content = "你是一個資料分析師，服務對象為老師，請精讀資料，使用 zh-TW"
    user_content = f"請根據 {df_string}，大概描述這張表的欄位敘述、資料樣態與資料分析，告訴老師這張表的意義，以及可能的結論與對應方式"
    messages = [
        {"role": "system", "content": sys_content},
        {"role": "user", "content": user_content}
    ]
    print("=====messages=====")
    print(messages)
    print("=====messages=====")

    request_payload = {
        "model": "gpt-4-1106-preview",
        "messages": messages,
        "max_tokens": 4000,
    }

    response = 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 = "你是一個資料分析師，user為老師，請精讀資料，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, q2, q3]"
    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 = 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 send_question(question, df_string_output, chat_history):
    # 当问题按钮被点击时调用此函数
    return respond(question, df_string_output, chat_history)

def respond(user_message, df_string_output, chat_history):
    print("=== 變數：user_message ===")
    print(user_message)
    print("=== 變數：chat_history ===")
    print(chat_history)

    sys_content = f"你是一個資料分析師，請用 {df_string_output} 為資料進行對話，使用 zh-TW"
    messages = [
        {"role": "system", "content": sys_content},
        {"role": "user", "content": user_message}
    ]

    print("=====messages=====")
    print(messages)
    print("=====messages=====")


    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 = client.chat.completions.create(**request_payload)
    print(response)
    
    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

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            file_upload = gr.File(label="Upload your CSV or Word file")
            youtube_link = gr.Textbox(label="Enter YouTube Link")
            web_link = gr.Textbox(label="Enter Web Page Link")
            chatbot = gr.Chatbot()
            msg = gr.Textbox(label="Message")
            send_button = gr.Button("Send")

        with gr.Column():
            with gr.Tab("YouTube Transcript and Video"):
                transcript_html = gr.HTML(label="YouTube Transcript and Video")
            with gr.Tab("資料本文"):
                df_string_output = gr.Textbox()
            with gr.Tab("資料摘要"):
                gr.Markdown("## 這是什麼樣的資料？")
                df_summarise = gr.Textbox(container=True, show_copy_button=True, label="資料本文", lines=40)    
            with gr.Tab("常用問題"):
                gr.Markdown("## 常用問題")
                btn_1 = gr.Button()
                btn_2 = gr.Button()
                btn_3 = gr.Button()

            
    send_button.click(
        respond, 
        inputs=[msg, df_string_output, chatbot], 
        outputs=[msg, chatbot]
    )
    # 连接按钮点击事件
    btn_1.click(respond, inputs=[btn_1, df_string_output, chatbot], outputs=[msg, chatbot])
    btn_2.click(respond, inputs=[btn_2, df_string_output, chatbot], outputs=[msg, chatbot])
    btn_3.click(respond, inputs=[btn_3, df_string_output, chatbot], outputs=[msg, chatbot])


    # 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=[btn_1, btn_2, btn_3, df_summarise, transcript_html])

    # 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()