app.py

import gradio as gr
from pydub import AudioSegment
import google.generativeai as genai
from google.generativeai.types import HarmCategory, HarmBlockThreshold
import json
import uuid
import io
import edge_tts
import asyncio
import aiofiles
import pypdf
import os
import time
from typing import List, Dict, Tuple
import openai

class PodcastGenerator:
    def __init__(self):
        pass

    async def generate_script(self, prompt: str, language: str, api_key: str) -> Dict:
        """
        非同步生成基於給定提示和語言的Podcast劇本。

        引數：
            prompt (str): 用於生成Podcast劇本的使用者輸入文字。
            language (str): Podcast指劇本所需的語言。
            api_key (str): 用於訪問 SambaNova API 服務的 API 金鑰。

        返回：
            Dict: 包含以 JSON 格式生成Podcast劇本的字典。

        異常：
            gr.Error: 如果 API 金鑰或速率限制出現問題。

        此方法使用 SambaNova API 根據使用者的輸入生成Podcast劇本。
        它處理語言選擇，使用適當的配置設定 AI 模型，並處理生成的響應。
        """
        # 定義一個示例JSON結構，用於指導AI生成類似格式的Podcast劇本
        example = """
        {
            "topic": "AGI",
            "podcast": [
                {
                    "speaker": 2,
                    "line": "So, AGI, huh? Seems like everyone's talking about it these days."
                },
                {
                    "speaker": 1,
                    "line": "Yeah, it's definitely having a moment, isn't it?"
                },
                {
                    "speaker": 2,
                    "line": "It is and for good reason, right? I mean, you've been digging into this stuff, listening to the podcasts and everything. What really stood out to you? What got you hooked?"
                },
                {
                    "speaker": 1,
                    "line": "Honestly, it's the sheer scale of what AGI could do. We're talking about potentially reshaping well everything."
                },
                ......
                {
                    "speaker": 2,
                    "line": "So to everyone listening out there I'll leave you with this. As AGI continues to develop, what role do you want to play in shaping its future?"
                },
                {
                    "speaker": 1,
                    "line": "That's a question worth pondering."
                },
                {
                    "speaker": 2,
                    "line": "It certainly is and on that note, we'll wrap up this deep dive. Thanks for listening, everyone."
                },
                {
                    "speaker": 1,
                    "line": "Peace."
                }
            ]
        }
        """

        # 根據使用者選擇的語言設定指令
        if language == "Auto Detect":
            language_instruction = "- The podcast MUST be in the same language as the user input."
        else:
            language_instruction = f"- The podcast MUST be in {language} language"

        # 設定系統提示，指導AI如何生成Podcast指令碼
        system_prompt = f"""
        You are a professional podcast generator. Your task is to generate a professional podcast script based on the user input.
        {language_instruction}
        - The podcast should have 2 speakers.
        - The podcast should be long.
        - Do not use names for the speakers.
        - The podcast should be interesting, lively, and engaging, and hook the listener from the start.
        - The input text might be disorganized or unformatted, originating from sources like PDFs or text files. Ignore any formatting inconsistencies or irrelevant details; your task is to distill the essential points, identify key definitions, and highlight intriguing facts that would be suitable for discussion in a podcast.
        - The script must be in JSON format.
        Follow this example structure carefully:
        {example}
        """

        # 設定使用者提示，包含使用者輸入的內容
        user_prompt = f"Please generate a podcast script based on the following user input:\n{prompt}"

        # 配置 SambaNova API client
        if api_key:
            openai.api_key = api_key
        else:
            openai.api_key = os.getenv("YOUR_API_TOKEN")
        client = openai.OpenAI(
            api_key=openai.api_key,
            base_url="https://api.sambanova.ai/v1",
        )

        # 嘗試生成內容
        try:
            response = client.chat.completions.create(
                model='Meta-Llama-3.1-405B-Instruct',
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": user_prompt}
                ],
                temperature=1,
                max_tokens=4096
            )
            generated_text = response.choices[0].message.content
        except Exception as e:
            # 處理可能的錯誤
            if "API key not valid" in str(e):
                raise gr.Error("Invalid API key. Please provide a valid SambaNova API key.")
            elif "rate limit" in str(e).lower():
                raise gr.Error("Rate limit exceeded for the API key. Please try again later or provide your own SambaNova API key.")
            else:
                raise gr.Error(f"Failed to generate podcast script: {e}")

        # 列印生成的Podcast指令碼
        print(f"Generated podcast script:\n{generated_text}")

        # 返回解析後的JSON資料
        return json.loads(generated_text)

    async def tts_generate(self, text: str, speaker: int, speaker1: str, speaker2: str) -> str:
        """
        非同步生成文字轉語音音訊檔案。

        引數：
            text (str): 要轉換爲語音的文字內容。
            speaker (int): 說話者的編號（1 或 2）。
            speaker1 (str): 第一位說話者的語音設定。
            speaker2 (str): 第二位說話者的語音設定。

        返回：
            str: 生成的臨時音訊檔案的檔名。

        此方法使用 Edge TTS 將文字轉換爲語音，並將結果儲存爲臨時音訊檔案。
        根據指定的說話者編號選擇相應的語音設定。
        """
        # 根據說話者選擇語音
        voice = speaker1 if speaker == 1 else speaker2
        # 建立語音合成對象
        speech = edge_tts.Communicate(text, voice)

        # 生成臨時檔名
        temp_filename = f"temp_{uuid.uuid4()}.wav"
        try:
            # 儲存語音檔案
            await speech.save(temp_filename)
            return temp_filename
        except Exception as e:
            # 如果出錯，刪除臨時檔案並丟擲異常
            if os.path.exists(temp_filename):
                os.remove(temp_filename)
            raise e

    async def combine_audio_files(self, audio_files: List[str]) -> str:
        """
        非同步合併音訊檔案。

        引數：
            audio_files (List[str]): 包含音訊檔案路徑的列表。

        返回：
            str: 合併後的音訊檔案的檔名。
        """
        # 建立空的音訊段
        combined_audio = AudioSegment.empty()
        # 遍歷所有音訊檔案並合併
        for audio_file in audio_files:
            combined_audio += AudioSegment.from_file(audio_file)
            os.remove(audio_file)  # 清理臨時檔案

        # 生成輸出文件名
        output_filename = f"output_{uuid.uuid4()}.wav"
        # 匯出合併後的音訊
        combined_audio.export(output_filename, format="wav")
        return output_filename

    async def generate_podcast(self, input_text: str, language: str, speaker1: str, speaker2: str, api_key: str) -> str:
        """
        非同步生成Podcast音訊檔案。

        引數：
            input_text (str): 用於生成Podcast指令碼的輸入文字。
            language (str): Podcast使用的語言。
            speaker1 (str): 第一位說話者的語音設定。
            speaker2 (str): 第二位說話者的語音設定。
            api_key (str): 用於訪問 Gemini AI 服務的 API 金鑰。

        返回：
            str: 生成的Podcast音訊檔案的檔名。

        此方法執行以下步驟：
        1. 使用 generate_script 方法生成Podcast劇本。
        2. 使用 tts_generate 方法爲每個對話行生成音訊檔案。
        3. 使用 combine_audio_files 方法將所有音訊檔案合併爲一個完整的Podcast。

        整個過程是非同步的，以提高效率。方法還會記錄並顯示每個步驟的執行時間。
        """
        # 生成Podcast劇本
        gr.Info("Generating podcast script...")
        start_time = time.time()
        podcast_json = await self.generate_script(input_text, language, api_key)
        end_time = time.time()
        gr.Info(f"Successfully generated podcast script in {(end_time - start_time):.2f} seconds!")

        # 生成Podcast音訊檔案
        gr.Info("Generating podcast audio files...")
        start_time = time.time()
        audio_files = await asyncio.gather(*[self.tts_generate(item['line'], item['speaker'], speaker1, speaker2) for item in podcast_json['podcast']])
        end_time = time.time()
        gr.Info(f"Successfully generated podcast audio files in {(end_time - start_time):.2f} seconds!")

        # 合併音訊檔案
        combined_audio = await self.combine_audio_files(audio_files)
        return combined_audio

class TextExtractor:
    @staticmethod
    async def extract_from_pdf(file_path: str) -> str:
        # 從PDF檔案中提取文字
        async with aiofiles.open(file_path, 'rb') as file:
            content = await file.read()
            pdf_reader = pypdf.PdfReader(io.BytesIO(content))
            return "\n\n".join(page.extract_text() for page in pdf_reader.pages if page.extract_text())

    @staticmethod
    async def extract_from_txt(file_path: str) -> str:
        # 從TXT檔案中提取文字
        async with aiofiles.open(file_path, 'r') as file:
            return await file.read()

    @classmethod
    async def extract_text(cls, file_path: str) -> str:
        # 根據檔案型別選擇適當的提取方法
        _, file_extension = os.path.splitext(file_path)
        if file_extension.lower() == '.pdf':
            return await cls.extract_from_pdf(file_path)
        elif file_extension.lower() == '.txt':
            return await cls.extract_from_txt(file_path)
        else:
            raise gr.Error(f"Unsupported file type: {file_extension}")

async def process_input(input_text: str, input_file, language: str, speaker1: str, speaker2: str, api_key: str = "") -> str:
    """
    處理輸入並生成Podcast的非同步函式。

    引數:
    input_text (str): 使用者輸入的文字內容。
    input_file: 使用者上傳的檔案（可以是 PDF 或 TXT）。
    language (str): 選擇的語言。
    speaker1 (str): 第一位說話者的語音選擇。
    speaker2 (str): 第二位說話者的語音選擇。
    api_key (str): 用於生成 AI 的 API 金鑰，預設爲空字串。

    返回:
    str: 生成的Podcast音訊檔案路徑。

    此函式協調整個Podcast生成過程，包括文字提取、指令碼生成和音訊合成。
    它處理不同的輸入型別（文字或檔案），並使用指定的語音和語言設定來建立最終的Podcast。
    """
    # 開始生成Podcast
    gr.Info("Starting podcast generation...")
    start_time = time.time()

    # 定義語音名稱對映
    voice_names = {
        "Andrew - English (United States)": "en-US-AndrewMultilingualNeural",
        "Ava - English (United States)": "en-US-AvaMultilingualNeural",
        "Brian - English (United States)": "en-US-BrianMultilingualNeural",
        "Emma - English (United States)": "en-US-EmmaMultilingualNeural",
        "Florian - German (Germany)": "de-DE-FlorianMultilingualNeural",
        "Seraphina - German (Germany)": "de-DE-SeraphinaMultilingualNeural",
        "Remy - French (France)": "fr-FR-RemyMultilingualNeural",
        "Vivienne - French (France)": "fr-FR-VivienneMultilingualNeural"
    }

    # 獲取實際的語音名稱
    speaker1 = voice_names[speaker1]
    speaker2 = voice_names[speaker2]

    # 如果提供了輸入檔案，則從檔案中提取文字
    if input_file:
        input_text = await TextExtractor.extract_text(input_file.name)

    # 如果沒有提供API金鑰，則使用環境變數中的金鑰
    if not api_key:
        api_key = os.getenv("Your_API_KEY")

    # 建立PodcastGenerator實例並生成Podcast
    podcast_generator = PodcastGenerator()
    podcast = await podcast_generator.generate_podcast(input_text, language, speaker1, speaker2, api_key)

    # 計算總耗時並顯示資訊
    end_time = time.time()
    gr.Info(f"Successfully generated podcast in {(end_time - start_time):.2f} seconds!")

    return podcast

# 定義Gradio介面
iface = gr.Interface(
    fn=process_input,
    inputs=[
        gr.Textbox(label="Input Text"),
        gr.File(label="Or Upload a PDF or TXT file"),
        gr.Dropdown(label="Language", choices=[
            "Auto Detect",
            "Afrikaans", "Albanian", "Amharic", "Arabic", "Armenian", "Azerbaijani",
            "Bahasa Indonesian", "Bangla", "Basque", "Bengali", "Bosnian", "Bulgarian",
            "Burmese", "Catalan", "Chinese Cantonese", "Chinese Mandarin",
            "Chinese Taiwanese", "Croatian", "Czech", "Danish", "Dutch", "English",
            "Estonian", "Filipino", "Finnish", "French", "Galician", "Georgian",
            "German", "Greek", "Hebrew", "Hindi", "Hungarian", "Icelandic", "Irish",
            "Italian", "Japanese", "Javanese", "Kannada", "Kazakh", "Khmer", "Korean",
            "Lao", "Latvian", "Lithuanian", "Macedonian", "Malay", "Malayalam",
            "Maltese", "Mongolian", "Nepali", "Norwegian Bokmål", "Pashto", "Persian",
            "Polish", "Portuguese", "Romanian", "Russian", "Serbian", "Sinhala",
            "Slovak", "Slovene", "Somali", "Spanish", "Sundanese", "Swahili",
            "Swedish", "Tamil", "Telugu", "Thai", "Turkish", "Ukrainian", "Urdu",
            "Uzbek", "Vietnamese", "Welsh", "Zulu"
        ],
        value="Auto Detect"),
        gr.Dropdown(label="Speaker 1 Voice", choices=[
            "Andrew - English (United States)",
            "Ava - English (United States)",
            "Brian - English (United States)",
            "Emma - English (United States)",
            "Florian - German (Germany)",
            "Seraphina - German (Germany)",
            "Remy - French (France)",
            "Vivienne - French (France)"
        ],
        value="Andrew - English (United States)"),
        gr.Dropdown(label="Speaker 2 Voice", choices=[
            "Andrew - English (United States)",
            "Ava - English (United States)",
            "Brian - English (United States)",
            "Emma - English (United States)",
            "Florian - German (Germany)",
            "Seraphina - German (Germany)",
            "Remy - French (France)",
            "Vivienne - French (France)"
        ],
        value="Ava - English (United States)"),
        gr.Textbox(label="Your Gemini API Key (Optional) - In case you are getting rate limited"),
    ],
    outputs=[
        gr.Audio(label="Generated Podcast Audio")
    ],
    title="🎙️ PodcastGen 🎙️",
    description="Generate a 2-speaker podcast from text input or documents!",
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch()