Update app.py

app.py

@@ -148,19 +148,30 @@ async def stream_audio(filename: str):
 
 @app.post("/api/chat")
 async def chat(request: Request):
+    import time as _time
+    t0 = _time.time()
+
     body = await request.json()
     user_text = body.get("text", "").strip()
     if not user_text: raise HTTPException(400, "No text")
 
     logger.info(f"[CHAT] User: {user_text}")
 
+    # 1. LLM
+    t1 = _time.time()
     from llm import generate_darwin_response
     llm_result = generate_darwin_response(user_text)
     reply_text = llm_result['text']
+    t2 = _time.time()
     logger.info(f"[CHAT] Darwin: {reply_text}")
+    logger.info(f"[TIMING] LLM: {t2-t1:.2f}s")
 
+    # 2. TTS
     from tts import generate_audio
     audio_path = generate_audio(reply_text)
+    t3 = _time.time()
+    logger.info(f"[TIMING] TTS: {t3-t2:.2f}s")
+
     audio_url = None
     audio_duration = 0
     if audio_path and os.path.exists(audio_path):
@@ -172,6 +183,7 @@ async def chat(request: Request):
         except Exception:
             audio_duration = 3.0
 
+    # 3. Lipsync
     lipsync_video_url = None
     if FLOAT_ENABLED and audio_path and os.path.exists(audio_path):
         try:
@@ -179,15 +191,20 @@ async def chat(request: Request):
             lipsync = get_lipsync()
             if lipsync.ready:
                 logger.info("[CHAT] Running FLOAT lipsync...")
+                t4 = _time.time()
                 lp = lipsync.generate(audio_path)
+                t5 = _time.time()
+                logger.info(f"[TIMING] FLOAT total: {t5-t4:.2f}s")
                 if lp and os.path.exists(lp):
                     lipsync_video_url = f"/api/stream/{os.path.basename(lp)}"
-                    logger.info(f"[CHAT] Lipsync: {lipsync_video_url}")
             else:
                 logger.info("[CHAT] FLOAT not ready")
         except Exception as e:
             logger.error(f"[CHAT] Lipsync failed: {e}", exc_info=True)
 
+    t_end = _time.time()
+    logger.info(f"[TIMING] TOTAL request: {t_end-t0:.2f}s")
+
     return JSONResponse({
         "text": reply_text,
         "emotion": llm_result.get('emotion','neutral'),