app.py

import gradio as gr
import openai
from pydub import AudioSegment
import os
import re

# 轉譯音檔函式，使用 OpenAI Whisper 模型
def transcribe(filename, api_key):
    client = openai.OpenAI(api_key=api_key)
    with open(filename, "rb") as audio_file:
        transcript_txt = client.audio.transcriptions.create(
            model="whisper-1",
            file=audio_file,
            response_format="text"
        )
    return transcript_txt

# 檢查檔案大小並分段處理（若檔案過大）
def transcribe_large_audio(filename, api_key, segment_length_ms=30 * 60 * 1000):
    def get_file_size_in_mb(file_path):
        return os.path.getsize(file_path) / (1024 * 1024)

    def split_audio_file(file_path, segment_length_ms=30 * 60 * 1000):
        audio = AudioSegment.from_file(file_path, format="mp3")
        segment_filenames = []
        for i in range(0, len(audio), segment_length_ms):
            end = min(i + segment_length_ms, len(audio))
            segment = audio[i:end]
            segment_filename = f"{file_path}_part{len(segment_filenames) + 1}.mp3"
            segment.export(segment_filename, format="mp3", bitrate="36k")
            segment_filenames.append(segment_filename)
        return segment_filenames

    transcript_txt = ""

    if get_file_size_in_mb(filename) > 25:
        audio_chunks = split_audio_file(filename)
        for chunk_filename in audio_chunks:
            transcript_txt += transcribe(chunk_filename, api_key)
            os.remove(chunk_filename)
    else:
        transcript_txt = transcribe(filename, api_key)

    return transcript_txt

# 字數統計函式
def count_words(text):
    # 移除空白字符後計算字數
    cleaned_text = re.sub(r'\s+', '', text)
    return len(cleaned_text)

# 自動斷句與標點符號功能
def auto_punctuate(text, api_key):
    openai.api_key = api_key
    prompt = """請幫我將以下逐字稿加入適當的標點符號和段落分隔，使文本更容易閱讀：
    原文：
    """ + text
    
    completion = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return completion.choices[0].message.content

# OpenAI 翻譯文本的函式
def openai_translate_text(text, target_lang, api_key):
    openai.api_key = api_key
    language_mapping = {
        "繁體中文": "繁體中文",
        "英文": "英文",
        "日文": "日文",
        "韓文": "韓文",
        "法文": "法文",
        "德文": "德文",
        "西班牙文": "西班牙文"
    }
    
    prompt = f"請將以下文本翻譯成{language_mapping[target_lang]}，保持原文的語氣和風格：\n\n{text}"
    completion = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return completion.choices[0].message.content

# OpenAI 生成摘要的函式（根據不同受眾）
def openai_generate_summary(text, audience_type, api_key):
    openai.api_key = api_key
    audience_prompts = {
        "學生": "請將以下會議內容整理成適合學生閱讀的摘要，重點放在學習價值和知識傳遞：",
        "老師": "請將以下會議內容整理成適合教師參考的摘要，重點放在教學應用和教育意義：",
        "會議": "請將以下會議內容整理成正式的會議摘要，重點放在決策、行動項目和重要討論：",
        "主管": "請將以下會議內容整理成適合管理層閱讀的摘要，重點放在策略決策和關鍵績效：",
        "技術人員": "請將以下會議內容整理成適合技術團隊閱讀的摘要，重點放在技術細節和實作方向：",
        "行銷人員": "請將以下會議內容整理成適合行銷團隊閱讀的摘要，重點放在市場策略和推廣重點：",
        "一般員工": "請將以下會議內容整理成適合一般員工閱讀的摘要，重點放在執行重點和日常工作相關內容：",
        "客戶": "請將以下會議內容整理成適合客戶閱讀的摘要，重點放在服務優化和價值傳遞：",
        "投資者": "請將以下會議內容整理成適合投資者閱讀的摘要，重點放在財務表現和未來展望：",
        "研究人員": "請將以下會議內容整理成適合研究人員閱讀的摘要，重點放在研究方法和數據分析："
    }
    
    prompt = f"{audience_prompts[audience_type]}\n\n{text}"
    completion = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return completion.choices[0].message.content

# 處理音檔轉文字
def process_audio(audio_file, api_key):
    if not audio_file or not api_key:
        return "請確保上傳音檔和輸入API金鑰", "", 0
    
    try:
        # 轉譯音檔
        transcript = transcribe_large_audio(audio_file, api_key)
        # 計算字數
        word_count = count_words(transcript)
        return transcript, f"字數統計：{word_count} 字", word_count
    except Exception as e:
        return f"處理失敗：{str(e)}", "", 0

# 處理標點與段落
def process_punctuation(text, api_key):
    if not text or not api_key:
        return "請確保有文本內容和API金鑰"
    try:
        return auto_punctuate(text, api_key)
    except Exception as e:
        return f"標點處理失敗：{str(e)}"

# 處理翻譯
def process_translation(text, target_lang, api_key):
    if not all([text, target_lang, api_key]):
        return "請確保所有必要欄位都已填寫"
    try:
        return openai_translate_text(text, target_lang, api_key)
    except Exception as e:
        return f"翻譯失敗：{str(e)}"

# 處理摘要
def process_summary(text, audience_type, api_key):
    if not all([text, audience_type, api_key]):
        return "請確保所有必要欄位都已填寫"
    try:
        return openai_generate_summary(text, audience_type, api_key)
    except Exception as e:
        return f"摘要生成失敗：{str(e)}"

# 建立 Gradio 介面
with gr.Blocks() as demo:
    gr.Markdown("會議音檔轉文字處理系統")
    
    with gr.Row():
        audio_file_input = gr.Audio(type="filepath", label="上傳音檔")
        api_key_input = gr.Textbox(label="輸入 OpenAI API 金鑰", type="password")
    
    with gr.Row():
        transcript_output = gr.Textbox(label="原始逐字稿", lines=5)
        word_count_output = gr.Textbox(label="字數統計")
    
    with gr.Row():
        punctuated_output = gr.Textbox(label="加入標點符號後的文本", lines=5)
    
    with gr.Row():
        target_lang_input = gr.Dropdown(
            choices=["繁體中文", "英文", "日文", "韓文", "法文", "德文", "西班牙文"],
            label="選擇目標語言",
            value="繁體中文"
        )
        translated_output = gr.Textbox(label="翻譯結果", lines=5)
    
    with gr.Row():
        audience_type_input = gr.Dropdown(
            choices=[
                "學生", "老師", "會議", "主管", "技術人員", 
                "行銷人員", "一般員工", "客戶", "投資者", "研究人員"
            ],
            label="選擇摘要類型",
            value="會議"
        )
        summary_output = gr.Textbox(label="客製化摘要", lines=5)
    
    with gr.Row():
        transcribe_button = gr.Button("1. 開始轉譯")
        punctuate_button = gr.Button("2. 添加標點符號")
        translate_button = gr.Button("3. 翻譯文本")
        summary_button = gr.Button("4. 生成摘要")

    # 連接按鈕和函式
    transcribe_button.click(
        process_audio,
        inputs=[audio_file_input, api_key_input],
        outputs=[transcript_output, word_count_output]
    )
    
    punctuate_button.click(
        process_punctuation,
        inputs=[transcript_output, api_key_input],
        outputs=punctuated_output
    )
    
    translate_button.click(
        process_translation,
        inputs=[punctuated_output, target_lang_input, api_key_input],
        outputs=translated_output
    )
    
    summary_button.click(
        process_summary,
        inputs=[translated_output, audience_type_input, api_key_input],
        outputs=summary_output
    )

# 啟動介面
if __name__ == "__main__":
    demo.launch()