Update app.py

app.py

@@ -1,4 +1,4 @@
-import os, re, uuid, torch, scipy.io.wavfile, edge_tts, asyncio, random
+import os, re, uuid, torch, scipy.io.wavfile, edge_tts, asyncio
 import numpy as np
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
@@ -31,16 +31,12 @@ class ModelManager:
             cls._music_pipeline = pipeline("text-to-audio", "facebook/musicgen-small", device="cpu")
         return cls._music_pipeline
 
-# 8인 멤버 설정 (국가별 언어 및 보이스)
-MEMBERS_CONFIG = {
-    "서윤 (Korea)": {"voice": "ko-KR-SunHiNeural", "lang": "Korean"},
-    "Chloe (USA)": {"voice": "en-US-AriaNeural", "lang": "English"},
-    "Naomi (Japan)": {"voice": "ja-JP-NanamiNeural", "lang": "Japanese"},
-    "Beatrice (Brazil)": {"voice": "pt-BR-FranciscaNeural", "lang": "Portuguese"},
-    "Elena (Spain)": {"voice": "es-ES-ElviraNeural", "lang": "Spanish"},
-    "Amira (Egypt)": {"voice": "ar-EG-SalmaNeural", "lang": "Arabic"},
-    "Liwei (China)": {"voice": "zh-CN-XiaoxiaoNeural", "lang": "Chinese"},
-    "Sophie (France)": {"voice": "fr-FR-DeniseNeural", "lang": "French"}
+# 8인 멤버 보이스 (상담 언어와 관계없이 멤버 고유 보이스 유지)
+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, consult_lang, g_inst, b_inst, d_inst, chords):
@@ -48,15 +44,11 @@ async def band_consulting(user_input, member_name, consult_lang, g_inst, b_inst,
     voice_path = f"/tmp/v_{req_id}.mp3"
     music_path = f"/tmp/m_{req_id}.wav"
     
-    # 1. 멤버 선정 로직: 영어가 선택되면 랜덤 멤버가 담당
-    actual_member = member_name
-    if consult_lang == "English":
-        actual_member = random.choice(list(MEMBERS_CONFIG.keys()))
-    
-    target_lang = MEMBERS_CONFIG[actual_member]["lang"] if consult_lang == "Native" else consult_lang
-    
     jam_context = f"Guitar: {g_inst}, Bass: {b_inst}, Drums: {d_inst}, Chords: {chords}"
-    system_prompt = f"""You are {actual_member}. Respond ONLY in {target_lang}.
+    
+    # 리더님 지시: 선택된 consult_lang으로 답변 강제
+    system_prompt = f"""You are {member_name}, a global rock star. 
+    You MUST respond ONLY in the language: {consult_lang}.
     Provide 5-7 lines of professional music advice. 
     [TAB] Section: Detailed chords/tabs.
     [MUSIC] Section: English prompt reflecting: {jam_context}"""
@@ -77,32 +69,30 @@ async def band_consulting(user_input, member_name, consult_lang, g_inst, b_inst,
         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=1024)
         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 "No Data"
+    tab_display = tab_match.group(1).strip() if tab_match else "No Score Data"
     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()
     
-    # TTS
-    voice_name = MEMBERS_CONFIG[actual_member]["voice"]
+    # TTS 보이스 (멤버 이름에 맞는 보이스 선택)
+    voice_name = MEMBERS_VOICE.get(member_name, "en-US-AriaNeural")
     communicate = edge_tts.Communicate(re.sub(r'[\*\#\-\_\~\|]', '', clean_text), voice_name)
     await communicate.save(voice_path)
 
-    # MusicGen (12초)
     music_gen = ModelManager.get_music()
-    music_p = music_match.group(1).strip() if music_match else "rock"
+    music_p = music_match.group(1).strip() if music_match else "rock guitar riff"
     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 f"[{actual_member}] {clean_text}", voice_path, music_path, tab_display
+    return clean_text, voice_path, music_path, tab_display
 
 with gr.Blocks() as demo:
-    in_list = [gr.Textbox(visible=False) for _ in range(7)]
-    out_list = [gr.Textbox(visible=False), gr.Audio(visible=False), gr.Audio(visible=False), gr.Textbox(visible=False)]
+    inputs = [gr.Textbox(visible=False) for _ in range(7)]
+    outputs = [gr.Textbox(visible=False), gr.Audio(visible=False), gr.Audio(visible=False), gr.Textbox(visible=False)]
     btn = gr.Button("API", visible=False)
-    btn.click(band_consulting, in_list, out_list, api_name="predict")
+    btn.click(band_consulting, inputs, outputs, api_name="predict")
 
 demo.queue().launch()