import os
import re
import uuid
import torch
import numpy as np
import scipy.io.wavfile
import gradio as gr
import edge_tts
import asyncio
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from groq import Groq

# API 키 및 환경 설정
GENAI_KEY = os.getenv("GEMINI_KEY") # 기존 키 유지 시
GROQ_API_KEY = os.getenv("GROQ_API_KEY")

class ModelManager:
    _llm_pipeline = None
    _music_pipeline = None
    _groq_client = None

    @classmethod
    def get_qwen(cls):
        if cls._llm_pipeline is None:
            # CPU 최적화 버전 Qwen 0.5B
            model_id = "Qwen/Qwen2.5-0.5B-Instruct"
            tokenizer = AutoTokenizer.from_pretrained(model_id)
            model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cpu", torch_dtype=torch.float32)
            cls._llm_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
        return cls._llm_pipeline

    @classmethod
    def get_groq(cls):
        if cls._groq_client is None and GROQ_API_KEY:
            cls._groq_client = Groq(api_key=GROQ_API_KEY)
        return cls._groq_client

    @classmethod
    def get_music(cls):
        if cls._music_pipeline is None:
            cls._music_pipeline = pipeline("text-to-audio", "facebook/musicgen-small", device="cpu")
        return cls._music_pipeline

# 8인 멤버 보이스 매핑
MEMBERS_VOICE = {
    "서윤 (Korea)": "ko-KR-SunHiNeural", "Chloe (USA)": "en-US-AriaNeural", 
    "Naomi (Japan)": "ja-JP-NanamiNeural", "Beatrice (Brazil)": "pt-BR-FranciscaNeural",
    "Elena (Spain)": "es-ES-ElviraNeural", "Amira (Egypt)": "ar-EG-SalmaNeural",
    "Liwei (China)": "zh-CN-XiaoxiaoNeural", "Sophie (France)": "fr-FR-DeniseNeural"
}

async def band_consulting(user_input, member_name, lang_code):
    req_id = str(uuid.uuid4())[:8]
    voice_path = f"/tmp/v_{req_id}.mp3"
    music_path = f"/tmp/m_{req_id}.wav"
    
    system_prompt = f"당신은 밴드 멤버 {member_name}입니다. {lang_code}로 답변하세요. 5줄 이내 요약, 상세 설명은 [TAB]에, 음악 프롬프트는 [MUSIC: 영어프롬프트]에 넣으세요."
    
    ai_text_raw = ""
    # 1. Groq 시도
    groq_client = ModelManager.get_groq()
    if groq_client:
        try:
            res = groq_client.chat.completions.create(
                messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_input}],
                model="llama-3.3-70b-versatile"
            )
            ai_text_raw = res.choices[0].message.content
        except: pass

    # 2. 로컬 Qwen Fallback
    if not ai_text_raw:
        qwen = ModelManager.get_qwen()
        out = qwen(f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_input}<|im_end|>\nassistant\n", max_new_tokens=512)
        ai_text_raw = out[0]['generated_text'].split("assistant\n")[-1]

    # 파싱
    tab_match = re.search(r'\[TAB\](.*?)(\[|$)', ai_text_raw, re.DOTALL | re.IGNORECASE)
    music_match = re.search(r'\[MUSIC:(.*?)\]', ai_text_raw, re.IGNORECASE)
    tab_display = tab_match.group(1).strip() if tab_match else "상세 악보 준비 중..."
    clean_text = re.sub(r'\[TAB\].*?(\[|$)', '', ai_text_raw, flags=re.DOTALL | re.IGNORECASE)
    clean_text = re.sub(r'\[MUSIC:.*?\]', '', clean_text, flags=re.IGNORECASE).strip()
    
    # 3. TTS 음성 생성 (복구)
    tts_text = "\n".join(clean_text.split('\n')[:5])
    voice_name = MEMBERS_VOICE.get(member_name, "ko-KR-SunHiNeural")
    communicate = edge_tts.Communicate(re.sub(r'[\*\#\-\_\~\|]', '', tts_text), voice_name)
    await communicate.save(voice_path)

    # 4. 음악 생성 (길이 연장: 512 토큰 = 약 12초)
    music_gen = ModelManager.get_music()
    music_p = music_match.group(1).strip() if music_match else "energetic rock guitar solo"
    music_output = music_gen(music_p, forward_params={"max_new_tokens": 512})
    audio_data = np.squeeze(music_output["audio"])
    audio_int16 = (audio_data * 32767).astype(np.int16)
    scipy.io.wavfile.write(music_path, music_output["sampling_rate"], audio_int16)

    return tts_text, voice_path, music_path, tab_display

with gr.Blocks() as demo:
    i1 = gr.Textbox(); i2 = gr.Textbox(); i3 = gr.Textbox()
    o1 = gr.Textbox(); o2 = gr.Audio(); o3 = gr.Audio(); o4 = gr.Textbox()
    btn = gr.Button("GO"); btn.click(band_consulting, [i1, i2, i3], [o1, o2, o3, o4], api_name="predict")

demo.queue().launch()