Create app.py

app.py

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+import gradio as gr
+import os
+import asyncio
+import torch
+import io
+import json
+import re
+import httpx
+import tempfile
+import wave
+import base64
+import numpy as np
+import soundfile as sf
+import subprocess
+import shutil
+from dataclasses import dataclass
+from typing import List, Tuple, Dict, Optional
+from pathlib import Path
+from threading import Thread
+from dotenv import load_dotenv
+
+# Edge TTS imports
+import edge_tts
+from pydub import AudioSegment
+
+# OpenAI imports
+from openai import OpenAI
+
+# Transformers imports (for legacy local mode)
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
+    BitsAndBytesConfig,
+)
+
+# Llama CPP imports (for new local mode)
+try:
+    from llama_cpp import Llama
+    from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
+    from llama_cpp_agent.providers import LlamaCppPythonProvider
+    from llama_cpp_agent.chat_history import BasicChatHistory
+    from llama_cpp_agent.chat_history.messages import Roles
+    from huggingface_hub import hf_hub_download
+    LLAMA_CPP_AVAILABLE = True
+except ImportError:
+    LLAMA_CPP_AVAILABLE = False
+
+# Spark TTS imports
+try:
+    from huggingface_hub import snapshot_download
+    SPARK_AVAILABLE = True
+except:
+    SPARK_AVAILABLE = False
+
+# MeloTTS imports (for local mode)
+try:
+    os.system("python -m unidic download")
+    from melo.api import TTS as MeloTTS
+    MELO_AVAILABLE = True
+except:
+    MELO_AVAILABLE = False
+
+load_dotenv()
+
+
+@dataclass
+class ConversationConfig:
+    max_words: int = 6000
+    prefix_url: str = "https://r.jina.ai/"
+    api_model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"
+    legacy_local_model_name: str = "NousResearch/Hermes-2-Pro-Llama-3-8B"
+    # 새로운 로컬 모델 설정
+    local_model_name: str = "Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503.gguf"
+    local_model_repo: str = "ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503"
+
+
+class UnifiedAudioConverter:
+    def __init__(self, config: ConversationConfig):
+        self.config = config
+        self.llm_client = None
+        self.legacy_local_model = None
+        self.legacy_tokenizer = None
+        # 새로운 로컬 LLM 관련
+        self.local_llm = None
+        self.local_llm_model = None
+        self.melo_models = None
+        self.spark_model_dir = None
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+    def initialize_api_mode(self, api_key: str):
+        """Initialize API mode with Together API (now fallback)"""
+        self.llm_client = OpenAI(api_key=api_key, base_url="https://api.together.xyz/v1")
+        
+    def initialize_local_mode(self):
+        """Initialize new local mode with Llama CPP"""
+        if not LLAMA_CPP_AVAILABLE:
+            raise RuntimeError("Llama CPP dependencies not available. Please install llama-cpp-python and llama-cpp-agent.")
+        
+        if self.local_llm is None or self.local_llm_model != self.config.local_model_name:
+            try:
+                # 모델 다운로드
+                model_path = hf_hub_download(
+                    repo_id=self.config.local_model_repo,
+                    filename=self.config.local_model_name,
+                    local_dir="./models"
+                )
+                
+                model_path_local = os.path.join("./models", self.config.local_model_name)
+                
+                if not os.path.exists(model_path_local):
+                    raise RuntimeError(f"Model file not found at {model_path_local}")
+                
+                # Llama 모델 초기화
+                self.local_llm = Llama(
+                    model_path=model_path_local,
+                    flash_attn=True,
+                    n_gpu_layers=81 if torch.cuda.is_available() else 0,
+                    n_batch=1024,
+                    n_ctx=8192,
+                )
+                self.local_llm_model = self.config.local_model_name
+                print(f"Local LLM initialized: {model_path_local}")
+                
+            except Exception as e:
+                print(f"Failed to initialize local LLM: {e}")
+                raise RuntimeError(f"Failed to initialize local LLM: {e}")
+
+    def initialize_legacy_local_mode(self):
+        """Initialize legacy local mode with Hugging Face model (fallback)"""
+        if self.legacy_local_model is None:
+            quantization_config = BitsAndBytesConfig(
+                load_in_4bit=True, 
+                bnb_4bit_compute_dtype=torch.float16
+            )
+            self.legacy_local_model = AutoModelForCausalLM.from_pretrained(
+                self.config.legacy_local_model_name, 
+                quantization_config=quantization_config
+            )
+            self.legacy_tokenizer = AutoTokenizer.from_pretrained(
+                self.config.legacy_local_model_name,
+                revision='8ab73a6800796d84448bc936db9bac5ad9f984ae'
+            )
+
+    def initialize_spark_tts(self):
+        """Initialize Spark TTS model by downloading if needed"""
+        if not SPARK_AVAILABLE:
+            raise RuntimeError("Spark TTS dependencies not available")
+        
+        model_dir = "pretrained_models/Spark-TTS-0.5B"
+        
+        # Check if model exists, if not download it
+        if not os.path.exists(model_dir):
+            print("Downloading Spark-TTS model...")
+            try:
+                os.makedirs("pretrained_models", exist_ok=True)
+                snapshot_download(
+                    "SparkAudio/Spark-TTS-0.5B", 
+                    local_dir=model_dir
+                )
+                print("Spark-TTS model downloaded successfully")
+            except Exception as e:
+                raise RuntimeError(f"Failed to download Spark-TTS model: {e}")
+        
+        self.spark_model_dir = model_dir
+        
+        # Check if we have the CLI inference script
+        if not os.path.exists("cli/inference.py"):
+            print("Warning: Spark-TTS CLI not found. Please clone the Spark-TTS repository.")
+
+    def initialize_melo_tts(self):
+        """Initialize MeloTTS models"""        
+        if MELO_AVAILABLE and self.melo_models is None:
+            self.melo_models = {"EN": MeloTTS(language="EN", device=self.device)}
+
+    def fetch_text(self, url: str) -> str:
+        """Fetch text content from URL"""
+        if not url:
+            raise ValueError("URL cannot be empty")
+            
+        if not url.startswith("http://") and not url.startswith("https://"):
+            raise ValueError("URL must start with 'http://' or 'https://'")
+
+        full_url = f"{self.config.prefix_url}{url}"
+        try:
+            response = httpx.get(full_url, timeout=60.0)
+            response.raise_for_status()
+            return response.text
+        except httpx.HTTPError as e:
+            raise RuntimeError(f"Failed to fetch URL: {e}")
+
+    def _get_messages_formatter_type(self, model_name):
+        """Get appropriate message formatter for the model"""
+        if "Mistral" in model_name or "BitSix" in model_name:
+            return MessagesFormatterType.CHATML
+        else:
+            return MessagesFormatterType.LLAMA_3
+
+    def _build_prompt(self, text: str, language: str = "English") -> str:
+        """Build prompt for conversation generation"""
+        if language == "Korean":
+            template = """
+            {
+                "conversation": [
+                    {"speaker": "", "text": ""},
+                    {"speaker": "", "text": ""}
+                ]
+            }
+            """
+            return (
+                f"{text}\n\n제공된 텍스트를 두 명의 전문가 간의 짧고 유익하며 명확한 "
+                f"팟캐스트 대화로 변환해주세요. 톤은 전문적이고 매력적이어야 합니다. "
+                f"다음 형식을 준수하고 JSON만 반환해주세요:\n{template}"
+            )
+        else:
+            template = """
+            {
+                "conversation": [
+                    {"speaker": "", "text": ""},
+                    {"speaker": "", "text": ""}
+                ]
+            }
+            """
+            return (
+                f"{text}\n\nConvert the provided text into a short, informative and crisp "
+                f"podcast conversation between two experts. The tone should be "
+                f"professional and engaging. Please adhere to the following "
+                f"format and return ONLY the JSON:\n{template}"
+            )
+
+    def _build_messages_for_local(self, text: str, language: str = "English") -> List[Dict]:
+        """Build messages for local LLM"""
+        if language == "Korean":
+            system_message = "당신은 한국어로 팟캐스트 대화를 생성하는 전문가입니다. 자연스럽고 유익한 한국어 대화를 만들어주세요."
+        else:
+            system_message = "You are an expert at creating podcast conversations in English. Create natural and informative English conversations."
+        
+        return [
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": self._build_prompt(text, language)}
+        ]
+
+    def extract_conversation_local(self, text: str, language: str = "English", progress=None) -> Dict:
+        """Extract conversation using new local LLM (primary method)"""
+        try:
+            # 먼저 새로운 로컬 LLM 시도
+            self.initialize_local_mode()
+            
+            chat_template = self._get_messages_formatter_type(self.config.local_model_name)
+            provider = LlamaCppPythonProvider(self.local_llm)
+
+            # 언어별 시스템 메시지
+            if language == "Korean":
+                system_message = "당신은 한국어로 팟캐스트 대화를 생성하는 전문가입니다. 자연스럽고 유익한 한국어 대화를 만들어주세요. JSON 형식으로만 응답하세요."
+            else:
+                system_message = "You are an expert at creating podcast conversations in English. Create natural and informative English conversations. Respond only in JSON format."
+
+            agent = LlamaCppAgent(
+                provider,
+                system_prompt=system_message,
+                predefined_messages_formatter_type=chat_template,
+                debug_output=False
+            )
+            
+            settings = provider.get_provider_default_settings()
+            settings.temperature = 0.7
+            settings.top_k = 40
+            settings.top_p = 0.95
+            settings.max_tokens = 2048
+            settings.repeat_penalty = 1.1
+            settings.stream = False
+
+            messages = BasicChatHistory()
+            
+            prompt = self._build_prompt(text, language)
+            response = agent.get_chat_response(
+                prompt,
+                llm_sampling_settings=settings,
+                chat_history=messages,
+                returns_streaming_generator=False,
+                print_output=False
+            )
+
+            # JSON 파싱
+            pattern = r"\{(?:[^{}]|(?:\{[^{}]*\}))*\}"
+            json_match = re.search(pattern, response)
+            
+            if json_match:
+                return json.loads(json_match.group())
+            else:
+                raise ValueError("No valid JSON found in local LLM response")
+                
+        except Exception as e:
+            print(f"Local LLM failed: {e}, falling back to legacy local method")
+            return self.extract_conversation_legacy_local(text, language, progress)
+
+    def extract_conversation_legacy_local(self, text: str, language: str = "English", progress=None) -> Dict:
+        """Extract conversation using legacy local model (fallback)"""
+        try:
+            self.initialize_legacy_local_mode()
+            
+            # 언어별 시스템 메시지
+            if language == "Korean":
+                system_message = "당신은 한국어로 팟캐스트 대화를 생성하는 전문가입니다. 자연스럽고 유익한 한국어 대화를 만들어주세요."
+            else:
+                system_message = "You are an expert at creating podcast conversations in English. Create natural and informative English conversations."
+
+            chat = [
+                {"role": "system", "content": system_message},
+                {"role": "user", "content": self._build_prompt(text, language)}
+            ]
+
+            terminators = [
+                self.legacy_tokenizer.eos_token_id,
+                self.legacy_tokenizer.convert_tokens_to_ids("<|eot_id|>")
+            ]
+
+            messages = self.legacy_tokenizer.apply_chat_template(
+                chat, tokenize=False, add_generation_prompt=True
+            )
+            model_inputs = self.legacy_tokenizer([messages], return_tensors="pt").to(self.device)
+            
+            streamer = TextIteratorStreamer(
+                self.legacy_tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
+            )
+            
+            generate_kwargs = dict(
+                model_inputs,
+                streamer=streamer,
+                max_new_tokens=4000,
+                do_sample=True,
+                temperature=0.9,
+                eos_token_id=terminators,
+            )
+
+            t = Thread(target=self.legacy_local_model.generate, kwargs=generate_kwargs)
+            t.start()
+
+            partial_text = ""
+            for new_text in streamer:
+                partial_text += new_text
+
+            pattern = r"\{(?:[^{}]|(?:\{[^{}]*\}))*\}"
+            json_match = re.search(pattern, partial_text)
+            
+            if json_match:
+                return json.loads(json_match.group())
+            else:
+                raise ValueError("No valid JSON found in legacy local response")
+                
+        except Exception as e:
+            print(f"Legacy local model also failed: {e}")
+            # Return default template
+            if language == "Korean":
+                return {
+                    "conversation": [
+                        {"speaker": "진행자", "text": "안녕하세요, 팟캐스트에 오신 것을 환영합니다."},
+                        {"speaker": "게스트", "text": "안녕하세요, 초대해 주셔서 감사합니다."}
+                    ]
+                }
+            else:
+                return {
+                    "conversation": [
+                        {"speaker": "Host", "text": "Welcome to our podcast."},
+                        {"speaker": "Guest", "text": "Thank you for having me."}
+                    ]
+                }
+
+    def extract_conversation_api(self, text: str, language: str = "English") -> Dict:
+        """Extract conversation using API (fallback method)"""
+        if not self.llm_client:
+            raise RuntimeError("API mode not initialized")
+
+        try:
+            # 언어별 프롬프트 구성
+            if language == "Korean":
+                system_message = "당신은 한국어로 팟캐스트 대화를 생성하는 전문가입니다. 자연스럽고 유익한 한국어 대화를 만들어주세요."
+            else:
+                system_message = "You are an expert at creating podcast conversations in English. Create natural and informative English conversations."
+
+            chat_completion = self.llm_client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": system_message},
+                    {"role": "user", "content": self._build_prompt(text, language)}
+                ],
+                model=self.config.api_model_name,
+            )
+
+            pattern = r"\{(?:[^{}]|(?:\{[^{}]*\}))*\}"
+            json_match = re.search(pattern, chat_completion.choices[0].message.content)
+
+            if not json_match:
+                raise ValueError("No valid JSON found in response")
+
+            return json.loads(json_match.group())
+        except Exception as e:
+            raise RuntimeError(f"Failed to extract conversation: {e}")
+
+    def parse_conversation_text(self, conversation_text: str) -> Dict:
+        """Parse conversation text back to JSON format"""
+        lines = conversation_text.strip().split('\n')
+        conversation_data = {"conversation": []}
+        
+        for line in lines:
+            if ':' in line:
+                speaker, text = line.split(':', 1)
+                conversation_data["conversation"].append({
+                    "speaker": speaker.strip(),
+                    "text": text.strip()
+                })
+        
+        return conversation_data
+
+    async def text_to_speech_edge(self, conversation_json: Dict, language: str = "English") -> Tuple[str, str]:
+        """Convert text to speech using Edge TTS"""
+        output_dir = Path(self._create_output_directory())
+        filenames = []
+
+        try:
+            # 언어별 음성 설정
+            if language == "Korean":
+                voices = [
+                    "ko-KR-SunHiNeural",  # 여성 음성 (자연스러운 한국어)
+                    "ko-KR-HyunsuNeural"  # 남성 음성 (자연스러운 한국어)
+                ]
+            else:
+                voices = [
+                    "en-US-AvaMultilingualNeural",    # 여성 음성
+                    "en-US-AndrewMultilingualNeural"  # 남성 음성
+                ]
+
+            for i, turn in enumerate(conversation_json["conversation"]):
+                filename = output_dir / f"output_{i}.wav"
+                voice = voices[i % len(voices)]
+
+                tmp_path = await self._generate_audio_edge(turn["text"], voice)
+                os.rename(tmp_path, filename)
+                filenames.append(str(filename))
+
+            # Combine audio files
+            final_output = os.path.join(output_dir, "combined_output.wav")
+            self._combine_audio_files(filenames, final_output)
+            
+            # Generate conversation text
+            conversation_text = "\n".join(
+                f"{turn.get('speaker', f'Speaker {i+1}')}: {turn['text']}" 
+                for i, turn in enumerate(conversation_json["conversation"])
+            )
+            
+            return final_output, conversation_text
+        except Exception as e:
+            raise RuntimeError(f"Failed to convert text to speech: {e}")
+
+    async def _generate_audio_edge(self, text: str, voice: str) -> str:
+        """Generate audio using Edge TTS"""
+        if not text.strip():
+            raise ValueError("Text cannot be empty")
+            
+        voice_short_name = voice.split(" - ")[0] if " - " in voice else voice
+        communicate = edge_tts.Communicate(text, voice_short_name)
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+            tmp_path = tmp_file.name
+            await communicate.save(tmp_path)
+
+        return tmp_path
+
+    def text_to_speech_spark(self, conversation_json: Dict, language: str = "English", progress=None) -> Tuple[str, str]:
+        """Convert text to speech using Spark TTS CLI"""
+        if not SPARK_AVAILABLE or not self.spark_model_dir:
+            raise RuntimeError("Spark TTS not available")
+
+        try:
+            output_dir = self._create_output_directory()
+            audio_files = []
+            
+            # Create different voice characteristics for different speakers
+            if language == "Korean":
+                voice_configs = [
+                    {"prompt_text": "안녕하세요, 오늘 팟캐스트 진행을 맡은 진행자입니다.", "gender": "female"},
+                    {"prompt_text": "안녕하세요, 오늘 게스트로 참여하게 되어 기쁩니다.", "gender": "male"}
+                ]
+            else:
+                voice_configs = [
+                    {"prompt_text": "Hello, welcome to our podcast. I'm your host today.", "gender": "female"},
+                    {"prompt_text": "Thank you for having me. I'm excited to be here.", "gender": "male"}
+                ]
+
+            for i, turn in enumerate(conversation_json["conversation"]):
+                text = turn["text"]
+                if not text.strip():
+                    continue
+                
+                # Use different voice config for each speaker
+                voice_config = voice_configs[i % len(voice_configs)]
+                
+                output_file = os.path.join(output_dir, f"spark_output_{i}.wav")
+                
+                # Run Spark TTS CLI inference
+                cmd = [
+                    "python", "-m", "cli.inference",
+                    "--text", text,
+                    "--device", "0" if torch.cuda.is_available() else "cpu",
+                    "--save_dir", output_dir,
+                    "--model_dir", self.spark_model_dir,
+                    "--prompt_text", voice_config["prompt_text"],
+                    "--output_name", f"spark_output_{i}.wav"
+                ]
+                
+                try:
+                    # Run the command
+                    result = subprocess.run(
+                        cmd, 
+                        capture_output=True, 
+                        text=True, 
+                        timeout=60,
+                        cwd="."  # Make sure we're in the right directory
+                    )
+                    
+                    if result.returncode == 0:
+                        audio_files.append(output_file)
+                    else:
+                        print(f"Spark TTS error for turn {i}: {result.stderr}")
+                        # Create a short silence as fallback
+                        silence = np.zeros(int(22050 * 1.0))  # 1 second of silence
+                        sf.write(output_file, silence, 22050)
+                        audio_files.append(output_file)
+                        
+                except subprocess.TimeoutExpired:
+                    print(f"Spark TTS timeout for turn {i}")
+                    # Create silence as fallback
+                    silence = np.zeros(int(22050 * 1.0))
+                    sf.write(output_file, silence, 22050)
+                    audio_files.append(output_file)
+                except Exception as e:
+                    print(f"Error running Spark TTS for turn {i}: {e}")
+                    # Create silence as fallback
+                    silence = np.zeros(int(22050 * 1.0))
+                    sf.write(output_file, silence, 22050)
+                    audio_files.append(output_file)
+
+            # Combine all audio files
+            if audio_files:
+                final_output = os.path.join(output_dir, "spark_combined.wav")
+                self._combine_audio_files(audio_files, final_output)
+            else:
+                raise RuntimeError("No audio files generated")
+
+            # Generate conversation text
+            conversation_text = "\n".join(
+                f"{turn.get('speaker', f'Speaker {i+1}')}: {turn['text']}" 
+                for i, turn in enumerate(conversation_json["conversation"])
+            )
+            
+            return final_output, conversation_text
+            
+        except Exception as e:
+            raise RuntimeError(f"Failed to convert text to speech with Spark TTS: {e}")
+
+    def text_to_speech_melo(self, conversation_json: Dict, progress=None) -> Tuple[str, str]:
+        """Convert text to speech using MeloTTS"""
+        if not MELO_AVAILABLE or not self.melo_models:
+            raise RuntimeError("MeloTTS not available")
+
+        speakers = ["EN-Default", "EN-US"]
+        combined_audio = AudioSegment.empty()
+
+        for i, turn in enumerate(conversation_json["conversation"]):
+            bio = io.BytesIO()
+            text = turn["text"]
+            speaker = speakers[i % 2]
+            speaker_id = self.melo_models["EN"].hps.data.spk2id[speaker]
+            
+            # Generate audio
+            self.melo_models["EN"].tts_to_file(
+                text, speaker_id, bio, speed=1.0, 
+                pbar=progress.tqdm if progress else None, 
+                format="wav"
+            )
+            
+            bio.seek(0)
+            audio_segment = AudioSegment.from_file(bio, format="wav")
+            combined_audio += audio_segment
+
+        # Save final audio
+        final_audio_path = "melo_podcast.mp3"
+        combined_audio.export(final_audio_path, format="mp3")
+        
+        # Generate conversation text
+        conversation_text = "\n".join(
+            f"{turn.get('speaker', f'Speaker {i+1}')}: {turn['text']}" 
+            for i, turn in enumerate(conversation_json["conversation"])
+        )
+        
+        return final_audio_path, conversation_text
+
+    def _create_output_directory(self) -> str:
+        """Create a unique output directory"""
+        random_bytes = os.urandom(8)
+        folder_name = base64.urlsafe_b64encode(random_bytes).decode("utf-8")
+        os.makedirs(folder_name, exist_ok=True)
+        return folder_name
+
+    def _combine_audio_files(self, filenames: List[str], output_file: str) -> None:
+        """Combine multiple audio files into one"""
+        if not filenames:
+            raise ValueError("No input files provided")
+
+        try:
+            audio_segments = []
+            for filename in filenames:
+                if os.path.exists(filename):
+                    audio_segment = AudioSegment.from_file(filename)
+                    audio_segments.append(audio_segment)
+
+            if audio_segments:
+                combined = sum(audio_segments)
+                combined.export(output_file, format="wav")
+
+            # Clean up temporary files
+            for filename in filenames:
+                if os.path.exists(filename):
+                    os.remove(filename)
+
+        except Exception as e:
+            raise RuntimeError(f"Failed to combine audio files: {e}")
+
+
+# Global converter instance
+converter = UnifiedAudioConverter(ConversationConfig())
+
+
+async def synthesize(article_url: str, mode: str = "Local", tts_engine: str = "Edge-TTS", language: str = "English"):
+    """Main synthesis function - Local is now primary, API is fallback"""
+    if not article_url:
+        return "Please provide a valid URL.", None
+
+    try:
+        # Fetch text from URL
+        text = converter.fetch_text(article_url)
+        
+        # Limit text to max words
+        words = text.split()
+        if len(words) > converter.config.max_words:
+            text = " ".join(words[:converter.config.max_words])
+
+        # Extract conversation based on mode
+        if mode == "Local":
+            # 로컬 모드가 기본 (새로운 Local LLM 사용)
+            try:
+                conversation_json = converter.extract_conversation_local(text, language)
+            except Exception as e:
+                print(f"Local mode failed: {e}, trying API fallback")
+                # API 폴백
+                api_key = os.environ.get("TOGETHER_API_KEY")
+                if api_key:
+                    converter.initialize_api_mode(api_key)
+                    conversation_json = converter.extract_conversation_api(text, language)
+                else:
+                    raise RuntimeError("Local mode failed and no API key available for fallback")
+        else:  # API mode (now secondary)
+            api_key = os.environ.get("TOGETHER_API_KEY")
+            if not api_key:
+                print("API key not found, falling back to local mode")
+                conversation_json = converter.extract_conversation_local(text, language)
+            else:
+                try:
+                    converter.initialize_api_mode(api_key)
+                    conversation_json = converter.extract_conversation_api(text, language)
+                except Exception as e:
+                    print(f"API mode failed: {e}, falling back to local mode")
+                    conversation_json = converter.extract_conversation_local(text, language)
+
+        # Generate conversation text
+        conversation_text = "\n".join(
+            f"{turn.get('speaker', f'Speaker {i+1}')}: {turn['text']}" 
+            for i, turn in enumerate(conversation_json["conversation"])
+        )
+
+        return conversation_text, None
+        
+    except Exception as e:
+        return f"Error: {str(e)}", None
+
+
+async def regenerate_audio(conversation_text: str, tts_engine: str = "Edge-TTS", language: str = "English"):
+    """Regenerate audio from edited conversation text"""
+    if not conversation_text.strip():
+        return "Please provide conversation text.", None
+
+    try:
+        # Parse the conversation text back to JSON format
+        conversation_json = converter.parse_conversation_text(conversation_text)
+        
+        if not conversation_json["conversation"]:
+            return "No valid conversation found in the text.", None
+
+        # 한국어인 경우 Edge-TTS만 사용 (다른 TTS는 한국어 지원이 제한적)
+        if language == "Korean" and tts_engine != "Edge-TTS":
+            return "한국어는 Edge-TTS만 지원됩니다. TTS 엔진이 자동으로 Edge-TTS로 변경됩니다.", None
+
+        # Generate audio based on TTS engine
+        if tts_engine == "Edge-TTS":
+            output_file, _ = await converter.text_to_speech_edge(conversation_json, language)
+        elif tts_engine == "Spark-TTS":
+            if not SPARK_AVAILABLE:
+                return "Spark TTS not available. Please install required dependencies and clone the Spark-TTS repository.", None
+            converter.initialize_spark_tts()
+            output_file, _ = converter.text_to_speech_spark(conversation_json, language)
+        else:  # MeloTTS
+            if not MELO_AVAILABLE:
+                return "MeloTTS not available. Please install required dependencies.", None
+            if language == "Korean":
+                return "MeloTTS does not support Korean. Please use Edge-TTS for Korean.", None
+            converter.initialize_melo_tts()
+            output_file, _ = converter.text_to_speech_melo(conversation_json)
+
+        return "Audio generated successfully!", output_file
+        
+    except Exception as e:
+        return f"Error generating audio: {str(e)}", None
+
+
+def synthesize_sync(article_url: str, mode: str = "Local", tts_engine: str = "Edge-TTS", language: str = "English"):
+    """Synchronous wrapper for async synthesis"""
+    return asyncio.run(synthesize(article_url, mode, tts_engine, language))
+
+
+def regenerate_audio_sync(conversation_text: str, tts_engine: str = "Edge-TTS", language: str = "English"):
+    """Synchronous wrapper for async audio regeneration"""
+    return asyncio.run(regenerate_audio(conversation_text, tts_engine, language))
+
+
+def update_tts_engine_for_korean(language):
+    """한국어 선택 시 TTS 엔진 옵션 업데이트"""
+    if language == "Korean":
+        return gr.Radio(
+            choices=["Edge-TTS"],
+            value="Edge-TTS",
+            label="TTS Engine",
+            info="한국어는 Edge-TTS만 지원됩니다",
+            interactive=False
+        )
+    else:
+        return gr.Radio(
+            choices=["Edge-TTS", "Spark-TTS", "MeloTTS"],
+            value="Edge-TTS",
+            label="TTS Engine",
+            info="Edge-TTS: Cloud-based, natural voices | Spark-TTS: Local AI model | MeloTTS: Local, requires GPU",
+            interactive=True
+        )
+
+
+# Gradio Interface
+with gr.Blocks(theme='soft', title="URL to Podcast Converter") as demo:
+    gr.Markdown("# 🎙️ URL to Podcast Converter")
+    gr.Markdown("Convert any article, blog, or news into an engaging podcast conversation!")
+    
+    # 상단에 로컬 LLM 상태 표시
+    with gr.Row():
+        gr.Markdown(f"""
+        ### 🤖 LLM Configuration:
+        - **Primary**: Local LLM ({converter.config.local_model_name}) - Runs on your device
+        - **Fallback**: API LLM ({converter.config.api_model_name}) - Used when local fails
+        - **Status**: {"✅ Llama CPP Available" if LLAMA_CPP_AVAILABLE else "❌ Llama CPP Not Available - Install llama-cpp-python"}
+        """)
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            url_input = gr.Textbox(
+                label="Article URL", 
+                placeholder="Enter the article URL here...",
+                value=""
+            )
+        with gr.Column(scale=1):
+            # 언어 선택 추가
+            language_selector = gr.Radio(
+                choices=["English", "Korean"],
+                value="English",
+                label="Language / 언어",
+                info="Select output language / 출력 언어를 선택하세요"
+            )
+            
+            mode_selector = gr.Radio(
+                choices=["Local", "API"],
+                value="Local",
+                label="Processing Mode",
+                info="Local: Runs on device (Primary) | API: Cloud-based (Fallback)"
+            )
+            
+            # TTS 엔진 선택
+            with gr.Group():
+                gr.Markdown("### TTS Engine Selection")
+                tts_selector = gr.Radio(
+                    choices=["Edge-TTS", "Spark-TTS", "MeloTTS"],
+                    value="Edge-TTS",
+                    label="TTS Engine",
+                    info="Edge-TTS: Cloud-based, natural voices | Spark-TTS: Local AI model | MeloTTS: Local, requires GPU"
+                )
+                
+                gr.Markdown("""
+                **Recommended:**
+                - 🌟 **Edge-TTS**: Best quality, cloud-based, instant setup
+                - 🤖 **Spark-TTS**: Local AI model (0.5B), zero-shot voice cloning
+                
+                **Additional Option:**
+                - ⚡ **MeloTTS**: Local processing, GPU recommended
+                
+                **한국어 지원:**
+                - 🇰🇷 한국어 선택 시 Edge-TTS만 사용 가능합니다
+                """)
+    
+    convert_btn = gr.Button("🎯 Generate Conversation / 대화 생성", variant="primary", size="lg")
+    
+    with gr.Row():
+        with gr.Column():
+            conversation_output = gr.Textbox(
+                label="Generated Conversation (Editable) / 생성된 대화 (편집 가능)",
+                lines=15,
+                max_lines=30,
+                interactive=True,
+                placeholder="Generated conversation will appear here. You can edit it before generating audio.\n생성된 대화가 여기에 표시됩니다. 오디오 생성 전에 편집할 수 있습니다.",
+                info="Edit the conversation as needed. Format: 'Speaker Name: Text' / 필요에 따라 대화를 편집하세요. 형식: '화자 이름: 텍스트'"
+            )
+            
+            # 오디오 생성 버튼 추가
+            with gr.Row():
+                generate_audio_btn = gr.Button("🎙️ Generate Audio from Text / 텍스트에서 오디오 생성", variant="secondary", size="lg")
+                gr.Markdown("*Edit the conversation above, then click to generate audio / 위의 대화를 편집한 후 클릭하여 오디오를 생성하세요*")
+            
+        with gr.Column():
+            audio_output = gr.Audio(
+                label="Podcast Audio / 팟캐스트 오디오",
+                type="filepath",
+                interactive=False
+            )
+            
+            # 상태 메시지 추가
+            status_output = gr.Textbox(
+                label="Status / 상태",
+                interactive=False,
+                visible=True
+            )
+    
+    # TTS 엔진별 설명 및 설치 안내 추가
+    with gr.Row():
+        gr.Markdown("""
+        ### TTS Engine Details / TTS 엔진 상세정보:
+        
+        - **Edge-TTS**: Microsoft's cloud TTS service with high-quality natural voices. Requires internet connection.
+          - 🇰🇷 **한국어 지원**: 자연스러운 한국어 음성 (여성: SunHi, 남성: InJoon)
+        - **Spark-TTS**: SparkAudio's local AI model (0.5B parameters) with zero-shot voice cloning capability. 
+          - **Setup required**: Clone [Spark-TTS repository](https://github.com/SparkAudio/Spark-TTS) in current directory
+          - Features: Bilingual support (Chinese/English), controllable speech generation
+          - License: CC BY-NC-SA (Non-commercial use only)
+          - ⚠️ **한국어 미지원**
+        - **MeloTTS**: Local TTS with multiple voice options. GPU recommended for better performance.
+          - ⚠️ **한국어 미지원**
+        
+        ### Local LLM Setup / 로컬 LLM 설정:
+        The system now uses **Private-BitSix-Mistral-Small-3.1-24B-Instruct** as the primary LLM, which runs locally on your device for privacy and independence. API fallback is available when needed.
+        
+        로컬 디바이스에서 개인정보 보호와 독립성을 위해 **Private-BitSix-Mistral-Small-3.1-24B-Instruct**를 기본 LLM으로 사용합니다. 필요시 API 폴백이 제공됩니다.
+        
+        ### Spark-TTS Setup Instructions:
+        ```bash
+        git clone https://github.com/SparkAudio/Spark-TTS.git
+        cd Spark-TTS
+        pip install -r requirements.txt
+        ```
+        """)
+    
+    gr.Examples(
+        examples=[
+            ["https://huggingface.co/blog/openfree/cycle-navigator", "Local", "Edge-TTS", "English"],
+            ["https://www.bbc.com/news/technology-67988517", "Local", "Spark-TTS", "English"],
+            ["https://arxiv.org/abs/2301.00810", "Local", "Edge-TTS", "Korean"],
+        ],
+        inputs=[url_input, mode_selector, tts_selector, language_selector],
+        outputs=[conversation_output, status_output],
+        fn=synthesize_sync,
+        cache_examples=False,
+    )
+    
+    # 언어 변경 시 TTS 엔진 옵션 업데이트
+    language_selector.change(
+        fn=update_tts_engine_for_korean,
+        inputs=[language_selector],
+        outputs=[tts_selector]
+    )
+    
+    # 이벤트 연결
+    convert_btn.click(
+        fn=synthesize_sync,
+        inputs=[url_input, mode_selector, tts_selector, language_selector],
+        outputs=[conversation_output, status_output]
+    )
+    
+    generate_audio_btn.click(
+        fn=regenerate_audio_sync,
+        inputs=[conversation_output, tts_selector, language_selector],
+        outputs=[status_output, audio_output]
+    )
+
+
+# Launch the app
+if __name__ == "__main__":
+    demo.queue(api_open=True, default_concurrency_limit=10).launch(
+        show_api=True,
+        share=False,
+        server_name="0.0.0.0",
+        server_port=7860
+    )