Update app.py

app.py

@@ -1,526 +1,565 @@
-import os
-import json
-import datetime
-import subprocess
-from queue import Queue
-from threading import Thread
-
-import torch
-import yt_dlp
-from faster_whisper import WhisperModel
-from flask import Flask, render_template, request, Response, jsonify
-from openai import OpenAI
-import spacy
-from collections import Counter
-
-import time
-import uuid
-
-import logging
-from logging.handlers import RotatingFileHandler
-from werkzeug.utils import secure_filename
-from collections import deque
-
-# 設置基本日誌配置
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# 創建一個文件處理器，使用 RotatingFileHandler 來限制日誌文件大小
-log_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'app.log')
-file_handler = RotatingFileHandler(log_file_path, maxBytes=10*1024*1024, backupCount=5, encoding='utf-8')
-file_handler.setLevel(logging.DEBUG)
-
-# 創建一個控制台處理器
-console_handler = logging.StreamHandler()
-console_handler.setLevel(logging.INFO)
-
-# 創建一個格式器
-formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-file_handler.setFormatter(formatter)
-console_handler.setFormatter(formatter)
-
-# 將處理器添加到日誌器
-logger.addHandler(file_handler)
-logger.addHandler(console_handler)
-
-# 設置其他模塊的日誌級別
-logging.getLogger("faster_whisper").setLevel(logging.INFO)
-
-os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
-
-app = Flask(__name__, static_folder='static', static_url_path='/static')
-
-# 讀取設定檔
-current_directory = os.path.dirname(os.path.realpath(__file__))
-config_file_path = os.path.join(current_directory, 'config.json')
-try:
-    with open(config_file_path, 'r', encoding='utf-8') as f:
-        config = json.load(f)
-    logger.info("成功加載配置文件")
-except Exception as e:
-    logger.exception("加載配置文件時發生錯誤")
-    raise
-
-# 設置 OpenAI API 金鑰
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-# 初始化 SpaCy
-nlp = spacy.load(config['spacy_model'])
-
-# 初始化 Whisper 模型
-model = WhisperModel(config['whisper_model'], device="auto", compute_type=config['whisper_compute_type'])
-
-# 設置 FFmpeg 路徑
-ffmpeg_path = config['ffmpeg_path']
-if ffmpeg_path not in os.environ["PATH"]:
-    os.environ["PATH"] += os.pathsep + ffmpeg_path
-
-def send_sse_message(q, data):
-    q.put_nowait(data)
-
-def clean_filename(filename):
-    return ''.join(c for c in filename if c.isalnum() or c in (' ', '.', '_')).rstrip()
-
-def download_audio(youtube_url, save_directory, q):
-    send_sse_message(q, {"status": "開始下載 YouTube 音頻..."})
-    unique_id = str(uuid.uuid4())[:8]  # 生成一個唯一的識別碼
-    output_filename = f"audio_{unique_id}"
-    output_path = os.path.join(save_directory, output_filename)
-
-    ydl_opts = {
-        'format': 'bestaudio/best',
-        'outtmpl': output_path + ".%(ext)s",
-        'postprocessors': [{
-            'key': 'FFmpegExtractAudio',
-            'preferredcodec': 'mp3',
-            'preferredquality': '192',
-        }],
-        'ffmpeg_location': ffmpeg_path,
-        'quiet': True
-    }
-
-    try:
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            info = ydl.extract_info(youtube_url, download=True)
-            video_title = clean_filename(info.get('title', 'Untitled'))
-
-        # 等待一小段時間，確保文件已經完全寫入
-        time.sleep(2)
-
-        # 檢查生成的文件
-        for file in os.listdir(save_directory):
-            if file.startswith(output_filename) and file.endswith('.mp3'):
-                converted_output_path = os.path.join(save_directory, file)
-                break
-        else:
-            raise FileNotFoundError("無法找到下載的音頻文件")
-
-        send_sse_message(q, {"status": f"音頻下載完成: {video_title}"})
-        return converted_output_path, video_title
-    except Exception as e:
-        send_sse_message(q, {"status": f"下載音頻時發生錯誤: {str(e)}"})
-        raise
-
-def process_local_video(video_path, save_directory, q):
-    send_sse_message(q, {"status": "正在處理本地視頻..."})
-    video_title = os.path.splitext(os.path.basename(video_path))[0]
-    output_path = os.path.join(save_directory, f"{video_title}_audio.mp3")
-
-    ffmpeg_command = [
-        os.path.join(ffmpeg_path, 'ffmpeg'),  # 使用完整路徑
-        '-i', video_path,
-        '-vn',  # 禁用視頻
-        '-acodec', 'libmp3lame',  # 使用 MP3 編碼器
-        '-q:a', '2',  # 音頻質量，2 是很好的質量
-        output_path
-    ]
-
-    logger.info(f"FFmpeg 命令: {' '.join(ffmpeg_command)}")
-    logger.info(f"輸入視頻路徑: {video_path}")
-    logger.info(f"輸出音頻路徑: {output_path}")
-
-    try:
-        # 檢查輸入文件是否存在
-        if not os.path.exists(video_path):
-            raise FileNotFoundError(f"輸入視頻文件不存在: {video_path}")
-
-        # 檢查輸出目錄是否可寫
-        if not os.access(os.path.dirname(output_path), os.W_OK):
-            raise PermissionError(f"沒有寫入權限: {os.path.dirname(output_path)}")
-
-        result = subprocess.run(ffmpeg_command, check=True, capture_output=True, text=True)
-        logger.info(f"FFmpeg 輸出: {result.stdout}")
-        send_sse_message(q, {"status": f"本地視頻處理完成: {video_title}"})
-        return output_path, video_title
-    except subprocess.CalledProcessError as e:
-        error_message = f"處理本地視頻時出錯: {e}\n\nFFmpeg 輸出:\n{e.stdout}\n\nFFmpeg 錯誤:\n{e.stderr}"
-        logger.error(error_message)
-        send_sse_message(q, {"status": "錯誤", "error": error_message})
-        raise
-    except Exception as e:
-        error_message = f"處理本地視頻時出現意外錯誤: {str(e)}"
-        logger.error(error_message)
-        send_sse_message(q, {"status": "錯誤", "error": error_message})
-        raise
-
-def generate_transcript(audio_path, video_title, q):
-    send_sse_message(q, {"status": "開始音頻轉錄..."})
-    segments, info = model.transcribe(
-        audio_path, 
-        beam_size=config['whisper_beam_size'],
-        language=config['whisper_language'],
-        temperature=config['whisper_temperature'],
-        initial_prompt=video_title,
-        repetition_penalty=2,
-        condition_on_previous_text=False
-    )
-    transcript = "\n".join([segment.text for segment in segments])
-    send_sse_message(q, {"status": f"音頻轉錄完成，檢測到的語言: {info.language}", "transcript": transcript})
-    return transcript
-
-def smart_split_transcript(transcript, q):
-    send_sse_message(q, {"status": "開始智能分割轉錄文本..."})
-    doc = nlp(transcript)
-    segments = []
-    current_segment = ""
-    max_length = 1024
-
-    for sent in doc.sents:
-        if len(current_segment) + len(sent.text) <= max_length:
-            current_segment += " " + sent.text
-        else:
-            if current_segment:
-                segments.append(current_segment.strip())
-            current_segment = sent.text
-
-    if current_segment:
-        segments.append(current_segment.strip())
-
-    send_sse_message(q, {"status": f"轉錄文本分割完成，共 {len(segments)} 個段落"})
-    return segments
-
-def extract_keywords_and_entities(text):
-    doc = nlp(text)
-    keywords = [token.lemma_ for token in doc if not token.is_stop and not token.is_punct]
-    keyword_freq = Counter(keywords).most_common(5)
-    entities = [(ent.text, ent.label_) for ent in doc.ents]
-    return [keyword for keyword, _ in keyword_freq], entities
-
-def process_youtube_description(description):
-    prompt = f"""請處理以下 YouTube 影片描述，移除所有渠道宣傳內容後，保留原文。
-
-描述內容：
-{description}"""
-
-    response = client.chat.completions.create(
-        model=config['openai_model'],
-        messages=[{"role": "system", "content": prompt}],
-        temperature=0.1,
-        max_tokens=500
-    )
-    
-    processed_description = response.choices[0].message.content.strip()
-    
-    # 在終端機打印處理後的描述
-    print("處理後的 YouTube 描述：")
-    print(processed_description)
-    print("------------------------")
-    
-    return processed_description
-
-def get_openai_summary(segment, video_title, is_final_summary, keywords, entities, processed_description, q):
-    if is_final_summary:
-        prompt = f"""以下是YouTube視頻'{video_title}'的多個段落摘要。請生成一個深入且全面的最終摘要，盡力保留主要內容、資訊細節、關鍵點和結論。摘要應該是連貫的、有條理的、詳細的，並且避免重複信息。在內容結尾，加入能夠方便搜尋器和 SEO 找到的 3 個 Hash Tag。請用繁體中文(香港)回應。
-
-影片描述提供的可靠資訊 (請特別使用來補充和糾正摘要中的信息，尤其是戈人名或專有名詞)：
-{processed_description}
-
-
-
-以下是待處理的摘要內容：
-{segment}"""
-    else:
-        keywords_str = ", ".join(keywords)
-        entities_str = ", ".join([f"{text}({label})" for text, label in entities])
-        prompt = f"""以下內容是YouTube視頻的部份字幕文本，每行以短句顯示，閱讀時需要將多行組合一起才是一句完整的句子，偶爾會出現音譯的錯別字，請修正。內容主題是關於：'{video_title}'，其中包含的關鍵詞有：{keywords_str}，和以下的NER實體：{entities_str}。
-
-影片描述提供的可靠資訊 (請特別使用來補充和糾正摘要中的信息，尤其是戈人名或專有名詞)：
-{processed_description}
-
-請根據每個NER實體的意思，以及上述描述資訊，以不少於 200 字的繁體中文(香港) 重組文章段落。目標是盡量抽取與主題有關的所有觀點、事件、案例、學問、步驟、方法、時間、人物、數據、名詞的基礎資料，建構成一篇連貫的、全面的、詳細的紀錄。請特別注意使用描述資訊來糾正可能的錯誤，尤其是人名和地名。忽略重複的、單純抒發個人情緒的訊息、與 Youtuber 個人宣傳的訊息。
-
-你要處理的內容如下：
-{segment}"""
-    
-    response = client.chat.completions.create(
-        model=config['openai_model'],
-        messages=[{"role": "system", "content": prompt}],
-        temperature=0.6,
-        max_tokens=1000
-    )
-    
-    summary = response.choices[0].message.content.strip()
-    return summary
-
-def save_summary(text, video_title, url_or_path, save_directory):
-    current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    cleaned_title = clean_filename(video_title)[:20]
-    summary_file_name = f"GPT_Summary_{cleaned_title}_{current_time}.txt"
-    summary_file_path = os.path.join(save_directory, summary_file_name)
-
-    # 移除文本開頭可能存在的影片名稱和 URL/路徑信息
-    lines = text.split('\n')
-    if lines[0].startswith("影片名稱：") and lines[1].startswith("網址或路徑："):
-        text = '\n'.join(lines[2:])
-
-    summary_text = f"影片名稱：\"{video_title}\"\n網址或路徑：\"{url_or_path}\"\n\n{text}"
-
-    with open(summary_file_path, "w", encoding="utf-8") as file:
-        file.write(summary_text)
-
-def save_transcript(transcript, video_title, url_or_path, save_directory):
-    current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    cleaned_title = clean_filename(video_title)[:20]
-    transcript_file_name = f"Transcript_{cleaned_title}_{current_time}.txt"
-    transcript_file_path = os.path.join(save_directory, transcript_file_name)
-
-    with open(transcript_file_path, "w", encoding="utf-8") as file:
-        file.write(f"影片名稱：\"{video_title}\"\n網址或路徑：\"{url_or_path}\"\n\n{transcript}")
-
-    logger.info(f"轉錄文本已保存至 {transcript_file_path}")
-
-def save_segment_summary(summary_text, segment_index, video_title, save_directory):
-    current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    cleaned_title = clean_filename(video_title)[:20]
-    summary_file_name = f"Segment_Summary_{cleaned_title}_{segment_index}_{current_time}.txt"
-    summary_file_path = os.path.join(save_directory, summary_file_name)
-    
-    with open(summary_file_path, "w", encoding="utf-8") as file:
-        file.write(summary_text)
-    
-    logger.info(f"段落摘要已保存至 {summary_file_path}")
-
-def process_video(url_or_path, q, local_video_description=''):
-    try:
-        logger.info(f"開始處理視頻: {url_or_path}")
-        save_directory = config['save_directory']
-
-        processed_description = ""
-        if url_or_path.startswith('http'):
-            # YouTube URL 處理邏輯保持不變
-            logger.info("檢測到 YouTube URL，開始獲取視頻信息")
-            ydl_opts = {'quiet': True}
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                video_info = ydl.extract_info(url_or_path, download=False)
-            
-            video_data = {
-                'title': video_info['title'],
-                'duration': str(datetime.timedelta(seconds=video_info['duration'])),
-                'view_count': video_info['view_count'],
-                'like_count': video_info.get('like_count', 'N/A'),
-                'description': video_info['description']
-            }
-            send_sse_message(q, {"status": "獲取到視頻信息", "video_info": video_data})
-
-            # 處理 YouTube 描述
-            raw_description = video_info['description']
-            processed_description = process_youtube_description(raw_description)
-
-            logger.info("開始下載 YouTube 音頻")
-            audio_path, video_title = download_audio(url_or_path, save_directory, q)
-        else:
-            logger.info("檢測到本地文件路徑，開始處理本地視頻")
-            audio_path, video_title = process_local_video(url_or_path, save_directory, q)
-            processed_description = local_video_description if local_video_description else "這是一個本地視頻文件，用戶沒有提供視頻描述。"
-
-        if not audio_path or not os.path.exists(audio_path):
-            raise FileNotFoundError(f"音頻文件不存在: {audio_path}")
-
-        logger.info("開始生成轉錄文本")
-        transcript = generate_transcript(audio_path, video_title, q)
-        
-        # 保存轉錄文本
-        save_transcript(transcript, video_title, url_or_path, save_directory)
-        
-        logger.info("開始分割轉錄文本")
-        segments = smart_split_transcript(transcript, q)
-
-        all_summaries = []
-        for i, segment in enumerate(segments, start=1):
-            logger.info(f"開始為文本段 {i}/{len(segments)} 生��摘要")
-            send_sse_message(q, {"status": f"正在為文本段 {i}/{len(segments)} 生成摘要..."})
-            keywords, entities = extract_keywords_and_entities(segment)
-            segment_summary = get_openai_summary(segment, video_title, False, keywords, entities, processed_description, q)
-            if segment_summary:
-                all_summaries.append(segment_summary)
-                save_segment_summary(segment_summary, i, video_title, save_directory)
-                send_sse_message(q, {"status": f"段落 {i} 摘要完成", "summary": segment_summary})
-
-        logger.info("開始生成最終摘要")
-        send_sse_message(q, {"status": "正在生成最終摘要..."})
-        all_summaries_text = "\n\n".join(all_summaries)
-        final_summary = get_openai_summary(all_summaries_text, video_title, True, [], [], processed_description, q)
-        # 將最終摘要添加到 summary_versions
-        summary_versions.append(final_summary)
-        # 修改這裡：發送包含版本信息的最終摘要
-        send_sse_message(q, {
-            "status": "處理完成", 
-            "final_summary": final_summary,
-            "version": 0,
-            "total_versions": len(summary_versions)
-        })
-        
-        # 添加影片名稱和 URL/路徑到最終摘要
-        final_summary_with_info = f'影片名稱："{video_title}"\n網址或路徑："{url_or_path}"\n\n{final_summary}'
-        
-        send_sse_message(q, {"status": "處理完成", "final_summary": final_summary_with_info})
-
-        # 保存最終摘要
-        logger.info("保存最終摘要")
-        save_summary(final_summary_with_info, video_title, url_or_path, save_directory)
-
-        # 刪除臨時音頻文件
-        if os.path.exists(audio_path):
-            try:
-                os.remove(audio_path)
-                logger.info("臨時音頻文件已刪除")
-                send_sse_message(q, {"status": "臨時音頻文件已刪除"})
-            except Exception as e:
-                logger.error(f"無法刪除臨時音頻文件: {str(e)}")
-                send_sse_message(q, {"status": f"無法刪除臨時音頻文件: {str(e)}"})
-
-        # 如果是本地上傳的 .mp4 文件，刪除臨時文件
-        if not url_or_path.startswith('http') and url_or_path.lower().endswith('.mp4'):
-            try:
-                os.remove(url_or_path)
-                logger.info("臨時上傳的 .mp4 文件已刪除")
-                send_sse_message(q, {"status": "臨時上傳的 .mp4 文件已刪除"})
-            except Exception as e:
-                logger.error(f"無法刪除臨時上傳的 .mp4 文件: {str(e)}")
-                send_sse_message(q, {"status": f"無法刪除臨時上傳的 .mp4 文件: {str(e)}"})
-
-        logger.info("視頻處理完成")
-
-    except Exception as e:
-        logger.exception("處理視頻時發生錯誤")
-        send_sse_message(q, {"status": f"錯誤: {str(e)}"})
-
-
-# 在全局變量部分添加：
-refinement_count = 0
-max_refinement_count = config.get('max_refinement_count', 5)  # 使用 get 方法，如果 config.json 配置中沒有，則使用默認值 5
-summary_versions = deque(maxlen=max_refinement_count + 1)
-
-# 添加新的函數：
-def refine_final_summary(original_summary, user_feedback, video_title, processed_description):
-    prompt = f"""你是一個專業的廣東話視頻內容摘要編輯。請根據用戶的反饋，改進以下內容摘要。標題是"{video_title}"。
-
-原始摘要：
-{original_summary}
-
-用戶反饋：
-{user_feedback}
-
-
-請遵循以下指引：
-1. 仔細閱讀原始摘要和用戶反饋，以用戶反饋的指示作為優先原則。
-2. 根據用戶反饋，補充、修正在原始摘要內，任何錯誤或不準確的資訊，確保摘要全面涵蓋主題內容。
-3. 保留原始摘要中準確和重要的部分。
-4. 確保摘要邏輯清晰，結構完整，易於閱讀理解。
-5. 如有必要，重新組織摘要結構以提高清晰度和連貫性。
-6. 保留原有的 Hash Tag（如果有的話），或根據更新後的內容調整 Hash Tag。
-
-請生成最終摘要，確保其準確、全面、連貫，並符合用戶的反饋意見。"""
-
-    response = client.chat.completions.create(
-        model=config['openai_model'],
-        messages=[{"role": "system", "content": prompt}],
-        temperature=0.8,
-        max_tokens=1000
-    )
-    
-    refined_summary = response.choices[0].message.content.strip()
-    return refined_summary
-
-# 添加新的路由：
-@app.route('/refine_summary', methods=['POST'])
-def refine_summary():
-    global refinement_count
-    data = request.json
-    #logger.info(f"Received refinement request: {data}") #{'original_summary': .... 'user_feedback': .... 'video_title':...'video_url'...'processed_description'...
-    original_summary = data['original_summary']
-    user_feedback = data['user_feedback']
-    video_title = data['video_title']
-    video_url = data['video_url']
-    processed_description = data['processed_description']
-
-    if refinement_count >= config['max_refinement_count']:
-        return jsonify({"error": "已達到最大重新生成次數"}), 400
-
-    refined_summary = refine_final_summary(original_summary, user_feedback, video_title, processed_description)
-    refinement_count += 1
-    
-    # 添加視頻信息到摘要
-    refined_summary_with_info = f"影片名稱：{video_title}\n網址或路徑：{video_url}\n\n{refined_summary}"
-    
-    logger.info(f"Sending refined summary: {refined_summary_with_info}")
-    return jsonify({
-        "refined_summary": refined_summary_with_info,
-        "version": refinement_count,
-        "total_versions": refinement_count + 1
-    })
-
-
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/process', methods=['POST'])
-def process():
-    try:
-        url_or_path = request.form.get('url_or_path')
-        
-        if not url_or_path:
-            return jsonify({"error": "No URL or path provided"}), 400
-        
-        if url_or_path.startswith('http'):
-            # YouTube URL 處理邏輯保持不變
-            pass
-        else:
-            # 本地文件處理
-            if 'file' not in request.files:
-                return jsonify({"error": "No file uploaded"}), 400
-            file = request.files['file']
-            if file.filename == '':
-                return jsonify({"error": "No file selected"}), 400
-            if file:
-                filename = secure_filename(file.filename)
-                file_path = os.path.join(config['save_directory'], filename)
-                file.save(file_path)
-                url_or_path = file_path
-        
-        # 獲取本地視頻描述
-        local_video_description = request.form.get('localVideoDescription', '')
-        
-        logger.info(f"處理文件: {url_or_path}")
-        
-        q = Queue()
-        thread = Thread(target=process_video, args=(url_or_path, q, local_video_description))
-        thread.start()
-        return Response(event_stream(q), content_type='text/event-stream')
-    except Exception as e:
-        error_message = f"處理請求時出現錯誤: {str(e)}"
-        logger.error(error_message)
-        return jsonify({"error": error_message}), 500
-
-def event_stream(q):
-    while True:
-        message = q.get()
-        yield f"data: {json.dumps(message)}\n\n"
-        if message.get('status') == '處理完成' or message.get('status').startswith('錯誤'):
-            break
-
-if __name__ == '__main__':
-    port = int(os.environ.get('PORT', 5000))
+import os
+import json
+import datetime
+import subprocess
+from queue import Queue
+from threading import Thread
+
+import torch
+import yt_dlp
+from faster_whisper import WhisperModel
+from flask import Flask, render_template, request, Response, jsonify
+from openai import OpenAI
+import spacy
+from collections import Counter
+
+import time
+import uuid
+
+import logging
+from logging.handlers import RotatingFileHandler
+from werkzeug.utils import secure_filename
+from collections import deque
+
+# 設置基本日誌配置
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# 創建一個文件處理器，使用 RotatingFileHandler 來限制日誌文件大小
+log_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'app.log')
+file_handler = RotatingFileHandler(log_file_path, maxBytes=10*1024*1024, backupCount=5, encoding='utf-8')
+file_handler.setLevel(logging.DEBUG)
+
+# 創建一個控制台處理器
+console_handler = logging.StreamHandler()
+console_handler.setLevel(logging.INFO)
+
+# 創建一個格式器
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+file_handler.setFormatter(formatter)
+console_handler.setFormatter(formatter)
+
+# 將處理器添加到日誌器
+logger.addHandler(file_handler)
+logger.addHandler(console_handler)
+
+# 設置其他模塊的日誌級別
+logging.getLogger("faster_whisper").setLevel(logging.INFO)
+
+os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
+
+app = Flask(__name__, static_folder='static', static_url_path='/static')
+
+# 讀取設定檔
+current_directory = os.path.dirname(os.path.realpath(__file__))
+config_file_path = os.path.join(current_directory, 'config.json')
+try:
+    with open(config_file_path, 'r', encoding='utf-8') as f:
+        config = json.load(f)
+    logger.info("成功加載配置文件")
+except Exception as e:
+    logger.exception("加載配置文件時發生錯誤")
+    raise
+
+# 設置 OpenAI API 金鑰
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# 初始化 SpaCy
+nlp = spacy.load(config['spacy_model'])
+
+# 初始化 Whisper 模型
+model = WhisperModel(config['whisper_model'], device="auto", compute_type=config['whisper_compute_type'])
+
+# 設置 FFmpeg 路徑
+ffmpeg_path = config['ffmpeg_path']
+if ffmpeg_path not in os.environ["PATH"]:
+    os.environ["PATH"] += os.pathsep + ffmpeg_path
+
+def send_sse_message(q, data):
+    q.put_nowait(data)
+
+def clean_filename(filename):
+    return ''.join(c for c in filename if c.isalnum() or c in (' ', '.', '_')).rstrip()
+
+def download_audio(youtube_url, save_directory, q):
+    send_sse_message(q, {"status": "開始下載 YouTube 音頻..."})
+    unique_id = str(uuid.uuid4())[:8]  # 生成一個唯一的識別碼
+    output_filename = f"audio_{unique_id}"
+    output_path = os.path.join(save_directory, output_filename)
+
+    ydl_opts = {
+        'format': 'bestaudio/best',
+        'postprocessors': [{
+            'key': 'FFmpegExtractAudio',
+            'preferredcodec': 'mp3',
+            'preferredquality': '192',
+        }],
+        'ffmpeg_location': ffmpeg_path,
+        'outtmpl': output_path + ".%(ext)s",
+        'quiet': True,
+        'no_warnings': True,
+        'no_check_certificate': True,
+        'ignoreerrors': True,
+        'nocheckcertificate': True,
+        'logtostderr': False,
+        'user_agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',
+        'socket_timeout': 30,  # 增加超時時間
+        'retries': 5,  # 失敗時的重試次數
+        'verbose': True,  # 啟用詳細日誌輸出，有助於調試
+        'extract_flat': 'in_playlist',
+        'youtube_include_dash_manifest': False,
+        'source_address': '0.0.0.0',  # 綁定到所有接口
+    }
+
+    try:
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            info = ydl.extract_info(youtube_url, download=True)
+            video_title = clean_filename(info.get('title', 'Untitled'))
+
+        # 等待一小段時間，確保文件已經完全寫入
+        time.sleep(2)
+
+        # 檢查生成的文件
+        for file in os.listdir(save_directory):
+            if file.startswith(output_filename) and file.endswith('.mp3'):
+                converted_output_path = os.path.join(save_directory, file)
+                break
+        else:
+            raise FileNotFoundError("無法找到下載的音頻文件")
+
+        send_sse_message(q, {"status": f"音頻下載完成: {video_title}"})
+        return converted_output_path, video_title
+    except Exception as e:
+        send_sse_message(q, {"status": f"下載音頻時發生錯誤: {str(e)}"})
+        raise
+
+def process_local_video(video_path, save_directory, q):
+    send_sse_message(q, {"status": "正在處理本地視頻..."})
+    video_title = os.path.splitext(os.path.basename(video_path))[0]
+    output_path = os.path.join(save_directory, f"{video_title}_audio.mp3")
+
+    ffmpeg_command = [
+        os.path.join(ffmpeg_path, 'ffmpeg'),  # 使用完整路徑
+        '-i', video_path,
+        '-vn',  # 禁用視頻
+        '-acodec', 'libmp3lame',  # 使用 MP3 編碼器
+        '-q:a', '2',  # 音頻質量，2 是很好的質量
+        output_path
+    ]
+
+    logger.info(f"FFmpeg 命令: {' '.join(ffmpeg_command)}")
+    logger.info(f"輸入視頻路徑: {video_path}")
+    logger.info(f"輸出音頻路徑: {output_path}")
+
+    try:
+        # 檢查輸入文件是否存在
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"輸入視頻文件不存在: {video_path}")
+
+        # 檢查輸出目錄是否可寫
+        if not os.access(os.path.dirname(output_path), os.W_OK):
+            raise PermissionError(f"沒有寫入權限: {os.path.dirname(output_path)}")
+
+        result = subprocess.run(ffmpeg_command, check=True, capture_output=True, text=True)
+        logger.info(f"FFmpeg 輸出: {result.stdout}")
+        send_sse_message(q, {"status": f"本地視頻處理完成: {video_title}"})
+        return output_path, video_title
+    except subprocess.CalledProcessError as e:
+        error_message = f"處理本地視頻時出錯: {e}\n\nFFmpeg 輸出:\n{e.stdout}\n\nFFmpeg 錯誤:\n{e.stderr}"
+        logger.error(error_message)
+        send_sse_message(q, {"status": "錯誤", "error": error_message})
+        raise
+    except Exception as e:
+        error_message = f"處理本地視頻時出現意外錯誤: {str(e)}"
+        logger.error(error_message)
+        send_sse_message(q, {"status": "錯誤", "error": error_message})
+        raise
+
+def generate_transcript(audio_path, video_title, q):
+    send_sse_message(q, {"status": "開始音頻轉錄..."})
+    segments, info = model.transcribe(
+        audio_path, 
+        beam_size=config['whisper_beam_size'],
+        language=config['whisper_language'],
+        temperature=config['whisper_temperature'],
+        initial_prompt=video_title,
+        repetition_penalty=2,
+        condition_on_previous_text=False
+    )
+    transcript = "\n".join([segment.text for segment in segments])
+    send_sse_message(q, {"status": f"音頻轉錄完成，檢測到的語言: {info.language}", "transcript": transcript})
+    return transcript
+
+def smart_split_transcript(transcript, q):
+    send_sse_message(q, {"status": "開始智能分割轉錄文本..."})
+    doc = nlp(transcript)
+    segments = []
+    current_segment = ""
+    max_length = 1024
+
+    for sent in doc.sents:
+        if len(current_segment) + len(sent.text) <= max_length:
+            current_segment += " " + sent.text
+        else:
+            if current_segment:
+                segments.append(current_segment.strip())
+            current_segment = sent.text
+
+    if current_segment:
+        segments.append(current_segment.strip())
+
+    send_sse_message(q, {"status": f"轉錄文本分割完成，共 {len(segments)} 個段落"})
+    return segments
+
+def extract_keywords_and_entities(text):
+    doc = nlp(text)
+    keywords = [token.lemma_ for token in doc if not token.is_stop and not token.is_punct]
+    keyword_freq = Counter(keywords).most_common(5)
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
+    return [keyword for keyword, _ in keyword_freq], entities
+
+def process_youtube_description(description):
+    prompt = f"""請處理以下 YouTube 影片描述，移除所有渠道宣傳內容後，保留原文。
+
+描述內容：
+{description}"""
+
+    response = client.chat.completions.create(
+        model=config['openai_model'],
+        messages=[{"role": "system", "content": prompt}],
+        temperature=0.1,
+        max_tokens=500
+    )
+    
+    processed_description = response.choices[0].message.content.strip()
+    
+    # 在終端機打印處理後的描述
+    print("處理後的 YouTube 描述：")
+    print(processed_description)
+    print("------------------------")
+    
+    return processed_description
+
+def get_openai_summary(segment, video_title, is_final_summary, keywords, entities, processed_description, q):
+    if is_final_summary:
+        prompt = f"""以下是YouTube視頻'{video_title}'的多個段落摘要。請生成一個深入且全面的最終摘要，盡力保留主要內容、資訊細節、關鍵點和結論。摘要應該是連貫的、有條理的、詳細的，並且避免重複信息。在內容結尾，加入能夠方便搜尋器和 SEO 找到的 3 個 Hash Tag。請用繁體中文(香港)回應。
+
+影片描述提供的可靠資訊 (請特別使用來補充和糾正摘要中的信息，尤其是戈人名或專有名詞)：
+{processed_description}
+
+
+
+以下是待處理的摘要內容：
+{segment}"""
+    else:
+        keywords_str = ", ".join(keywords)
+        entities_str = ", ".join([f"{text}({label})" for text, label in entities])
+        prompt = f"""以下內容是YouTube視頻的部份字幕文本，每行以短句顯示，閱讀時需要將多行組合一起才是一句完整的句子，偶爾會出現音譯的錯別字，請修正。內容主題是關於：'{video_title}'，其中包含的關鍵詞有：{keywords_str}，和以下的NER實體：{entities_str}。
+
+影片描述提供的可靠資訊 (請特別使用來補充和糾正摘要中的信息，尤其是戈人名或專有名詞)：
+{processed_description}
+
+請根據每個NER實體的意思，以及上述描述資訊，以不少於 200 字的繁體中文(香港) 重組文章段落。目標是盡量抽取與主題有關的所有觀點、事件、案例、學問、步驟、方法、時間、人物、數據、名詞的基礎資料，建構成一篇連貫的、全面的、詳細的紀錄。請特別注意使用描述資訊來糾正可能的錯誤，尤其是人名和地名。忽略重複的、單純抒發個人情緒的訊息、與 Youtuber 個人宣傳的訊息。
+
+你要處理的內容如下：
+{segment}"""
+    
+    response = client.chat.completions.create(
+        model=config['openai_model'],
+        messages=[{"role": "system", "content": prompt}],
+        temperature=0.6,
+        max_tokens=1000
+    )
+    
+    summary = response.choices[0].message.content.strip()
+    return summary
+
+def save_summary(text, video_title, url_or_path, save_directory):
+    current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+    cleaned_title = clean_filename(video_title)[:20]
+    summary_file_name = f"GPT_Summary_{cleaned_title}_{current_time}.txt"
+    summary_file_path = os.path.join(save_directory, summary_file_name)
+
+    # 移除文本開頭可能存在的影片名稱和 URL/路徑信息
+    lines = text.split('\n')
+    if lines[0].startswith("影片名稱：") and lines[1].startswith("網址或路徑："):
+        text = '\n'.join(lines[2:])
+
+    summary_text = f"影片名稱：\"{video_title}\"\n網址或路徑：\"{url_or_path}\"\n\n{text}"
+
+    with open(summary_file_path, "w", encoding="utf-8") as file:
+        file.write(summary_text)
+
+def save_transcript(transcript, video_title, url_or_path, save_directory):
+    current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+    cleaned_title = clean_filename(video_title)[:20]
+    transcript_file_name = f"Transcript_{cleaned_title}_{current_time}.txt"
+    transcript_file_path = os.path.join(save_directory, transcript_file_name)
+
+    with open(transcript_file_path, "w", encoding="utf-8") as file:
+        file.write(f"影片名稱：\"{video_title}\"\n網址或���徑：\"{url_or_path}\"\n\n{transcript}")
+
+    logger.info(f"轉錄文本已保存至 {transcript_file_path}")
+
+def save_segment_summary(summary_text, segment_index, video_title, save_directory):
+    current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+    cleaned_title = clean_filename(video_title)[:20]
+    summary_file_name = f"Segment_Summary_{cleaned_title}_{segment_index}_{current_time}.txt"
+    summary_file_path = os.path.join(save_directory, summary_file_name)
+    
+    with open(summary_file_path, "w", encoding="utf-8") as file:
+        file.write(summary_text)
+    
+    logger.info(f"段落摘要已保存至 {summary_file_path}")
+
+def process_video(url_or_path, q, local_video_description=''):
+    try:
+        logger.info(f"開始處理視頻: {url_or_path}")
+        save_directory = config['save_directory']
+
+        processed_description = ""
+        if url_or_path.startswith('http'):
+            logger.info("檢測到 YouTube URL，開始獲取視頻信息")
+            ydl_opts = {
+                'format': 'bestaudio/best',
+                'postprocessors': [{
+                    'key': 'FFmpegExtractAudio',
+                    'preferredcodec': 'mp3',
+                    'preferredquality': '192',
+                }],
+                'ffmpeg_location': config['ffmpeg_path'],
+                'outtmpl': os.path.join(save_directory, 'audio_%(id)s.%(ext)s'),
+                'quiet': True,
+                'no_warnings': True,
+                'no_check_certificate': True,
+                'ignoreerrors': True,
+                'nocheckcertificate': True,
+                'logtostderr': False,
+                'user_agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',
+                'socket_timeout': 30,
+                'retries': 5,
+                'verbose': True,
+                'extract_flat': 'in_playlist',
+                'youtube_include_dash_manifest': False,
+                'source_address': '0.0.0.0',
+            }
+
+            try:
+                with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+                    video_info = ydl.extract_info(url_or_path, download=False)
+                
+                video_data = {
+                    'title': video_info['title'],
+                    'duration': str(datetime.timedelta(seconds=video_info['duration'])),
+                    'view_count': video_info['view_count'],
+                    'like_count': video_info.get('like_count', 'N/A'),
+                    'description': video_info['description']
+                }
+                send_sse_message(q, {"status": "獲取到視頻信息", "video_info": video_data})
+
+                # 處理 YouTube 描述
+                raw_description = video_info['description']
+                processed_description = process_youtube_description(raw_description)
+
+                logger.info("開始下載 YouTube 音頻")
+                audio_path, video_title = download_audio(url_or_path, save_directory, q)
+            except yt_dlp.utils.DownloadError as e:
+                if "Sign in to confirm you're not a bot" in str(e):
+                    send_sse_message(q, {"status": "錯誤：YouTube 要求人機驗證。請稍後再試或使用其他視頻。"})
+                else:
+                    send_sse_message(q, {"status": f"下載音頻時發生錯誤: {str(e)}"})
+                raise
+        else:
+            logger.info("檢測到本地文件路徑，開始處理本地視頻")
+            audio_path, video_title = process_local_video(url_or_path, save_directory, q)
+            processed_description = local_video_description if local_video_description else "這是一個本地視頻文件，用戶沒有提供視頻描述。"
+
+        if not audio_path or not os.path.exists(audio_path):
+            raise FileNotFoundError(f"音頻文件不存在: {audio_path}")
+
+        logger.info("開始生成轉錄文本")
+        transcript = generate_transcript(audio_path, video_title, q)
+        
+        # 保存轉錄文本
+        save_transcript(transcript, video_title, url_or_path, save_directory)
+        
+        logger.info("開始分割轉錄文本")
+        segments = smart_split_transcript(transcript, q)
+
+        all_summaries = []
+        for i, segment in enumerate(segments, start=1):
+            logger.info(f"開始為文本段 {i}/{len(segments)} 生成摘要")
+            send_sse_message(q, {"status": f"正在為文本段 {i}/{len(segments)} 生成摘要..."})
+            keywords, entities = extract_keywords_and_entities(segment)
+            segment_summary = get_openai_summary(segment, video_title, False, keywords, entities, processed_description, q)
+            if segment_summary:
+                all_summaries.append(segment_summary)
+                save_segment_summary(segment_summary, i, video_title, save_directory)
+                send_sse_message(q, {"status": f"段落 {i} 摘要完成", "summary": segment_summary})
+
+        logger.info("開始生成最終摘要")
+        send_sse_message(q, {"status": "正在生成最終摘要..."})
+        all_summaries_text = "\n\n".join(all_summaries)
+        final_summary = get_openai_summary(all_summaries_text, video_title, True, [], [], processed_description, q)
+        summary_versions.append(final_summary)
+        send_sse_message(q, {
+            "status": "處理完成", 
+            "final_summary": final_summary,
+            "version": 0,
+            "total_versions": len(summary_versions)
+        })
+        
+        # 添加影片名稱和 URL/路徑到最終摘要
+        final_summary_with_info = f'影片名稱："{video_title}"\n網址或路徑："{url_or_path}"\n\n{final_summary}'
+        
+        send_sse_message(q, {"status": "處理完成", "final_summary": final_summary_with_info})
+
+        # 保存最終摘要
+        logger.info("保存最終摘要")
+        save_summary(final_summary_with_info, video_title, url_or_path, save_directory)
+
+        # 刪除臨時音頻文件
+        if os.path.exists(audio_path):
+            try:
+                os.remove(audio_path)
+                logger.info("臨時音頻文件已刪除")
+                send_sse_message(q, {"status": "臨時音頻文件已刪除"})
+            except Exception as e:
+                logger.error(f"無法刪除臨時音頻文件: {str(e)}")
+                send_sse_message(q, {"status": f"無法刪除臨時音頻文件: {str(e)}"})
+
+        # 如果是本地上傳的 .mp4 文件，刪除臨時文件
+        if not url_or_path.startswith('http') and url_or_path.lower().endswith('.mp4'):
+            try:
+                os.remove(url_or_path)
+                logger.info("臨時上傳的 .mp4 文件已刪除")
+                send_sse_message(q, {"status": "臨時上傳的 .mp4 文件已刪除"})
+            except Exception as e:
+                logger.error(f"無法刪除臨時上傳的 .mp4 文件: {str(e)}")
+                send_sse_message(q, {"status": f"無法刪除臨時上傳的 .mp4 文件: {str(e)}"})
+
+        logger.info("視頻處理完成")
+
+    except Exception as e:
+        logger.exception("處理視頻時發生錯誤")
+        send_sse_message(q, {"status": f"錯誤: {str(e)}"})
+
+
+# 在全局變量部分添加：
+refinement_count = 0
+max_refinement_count = config.get('max_refinement_count', 5)  # 使用 get 方法，如果 config.json 配置中沒有，則使用默認值 5
+summary_versions = deque(maxlen=max_refinement_count + 1)
+
+# 添加新的函數：
+def refine_final_summary(original_summary, user_feedback, video_title, processed_description):
+    prompt = f"""你是一個專業的廣東話視頻內容摘要編輯。請根據用戶的反饋，改進以下內容摘要。標題是"{video_title}"。
+
+原始摘要：
+{original_summary}
+
+用戶反饋：
+{user_feedback}
+
+
+請遵循以下指引：
+1. 仔細閱讀原始摘要和用戶反饋，以用戶反饋的指示作為優先原則。
+2. 根據用戶反饋，補充、修正在原始摘要內，任何錯誤或不準確的資訊，確保摘要全面涵蓋主題內容。
+3. 保留原始摘要中準確和重要的部分。
+4. 確保摘要邏輯清晰，結構完整，易於閱讀理解。
+5. 如有必要，重新組織摘要結構以提高清晰度和連貫性。
+6. 保留原有的 Hash Tag（如果有的話），或根據更新後的內容調整 Hash Tag。
+
+請生成最終摘要，確保其準確、全面、連貫，並符合用戶的反饋意見。"""
+
+    response = client.chat.completions.create(
+        model=config['openai_model'],
+        messages=[{"role": "system", "content": prompt}],
+        temperature=0.8,
+        max_tokens=1000
+    )
+    
+    refined_summary = response.choices[0].message.content.strip()
+    return refined_summary
+
+# 添加新的路由：
+@app.route('/refine_summary', methods=['POST'])
+def refine_summary():
+    global refinement_count
+    data = request.json
+    #logger.info(f"Received refinement request: {data}") #{'original_summary': .... 'user_feedback': .... 'video_title':...'video_url'...'processed_description'...
+    original_summary = data['original_summary']
+    user_feedback = data['user_feedback']
+    video_title = data['video_title']
+    video_url = data['video_url']
+    processed_description = data['processed_description']
+
+    if refinement_count >= config['max_refinement_count']:
+        return jsonify({"error": "已達到最大重新生成次數"}), 400
+
+    refined_summary = refine_final_summary(original_summary, user_feedback, video_title, processed_description)
+    refinement_count += 1
+    
+    # 添加視頻信息到摘要
+    refined_summary_with_info = f"影片名稱：{video_title}\n網址或路徑：{video_url}\n\n{refined_summary}"
+    
+    logger.info(f"Sending refined summary: {refined_summary_with_info}")
+    return jsonify({
+        "refined_summary": refined_summary_with_info,
+        "version": refinement_count,
+        "total_versions": refinement_count + 1
+    })
+
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/process', methods=['POST'])
+def process():
+    try:
+        url_or_path = request.form.get('url_or_path')
+        
+        if not url_or_path:
+            return jsonify({"error": "No URL or path provided"}), 400
+        
+        if url_or_path.startswith('http'):
+            # YouTube URL 處理邏輯保持不變
+            pass
+        else:
+            # 本地文件處理
+            if 'file' not in request.files:
+                return jsonify({"error": "No file uploaded"}), 400
+            file = request.files['file']
+            if file.filename == '':
+                return jsonify({"error": "No file selected"}), 400
+            if file:
+                filename = secure_filename(file.filename)
+                file_path = os.path.join(config['save_directory'], filename)
+                file.save(file_path)
+                url_or_path = file_path
+        
+        # 獲取本地視頻描述
+        local_video_description = request.form.get('localVideoDescription', '')
+        
+        logger.info(f"處理文件: {url_or_path}")
+        
+        q = Queue()
+        thread = Thread(target=process_video, args=(url_or_path, q, local_video_description))
+        thread.start()
+        return Response(event_stream(q), content_type='text/event-stream')
+    except Exception as e:
+        error_message = f"處理請求時出現錯誤: {str(e)}"
+        logger.error(error_message)
+        return jsonify({"error": error_message}), 500
+
+def event_stream(q):
+    while True:
+        message = q.get()
+        yield f"data: {json.dumps(message)}\n\n"
+        if message.get('status') == '處理完成' or message.get('status').startswith('錯誤'):
+            break
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 5000))
     app.run(host='0.0.0.0', port=port)