small streamlining of example 02

src/examples/foundational/02-llm-say-one-thing.py

@@ -6,24 +6,22 @@ import aiohttp
 
 from dailyai.pipeline.frames import LLMMessagesQueueFrame
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.services.deepgram_ai_services import DeepgramTTSService
 from dailyai.services.open_ai_services import OpenAILLMService
+
 from examples.support.runner import configure
 
 logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
 logger = logging.getLogger("dailyai")
 logger.setLevel(logging.DEBUG)
 
+
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
-        meeting_duration_minutes = 1
         transport = DailyTransportService(
             room_url,
             None,
             "Say One Thing From an LLM",
-            duration_minutes=meeting_duration_minutes,
             mic_enabled=True,
         )
 
@@ -32,25 +30,24 @@ async def main(room_url):
             api_key=os.getenv("ELEVENLABS_API_KEY"),
             voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
         )
+
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
         )
+
         messages = [
             {
                 "role": "system",
                 "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
             }
         ]
-        tts_task = asyncio.create_task(
-            tts.run_to_queue(
-                transport.send_queue,
-                llm.run([LLMMessagesQueueFrame(messages)]),
-            )
-        )
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            await tts_task
+            await tts.run_to_queue(
+                transport.send_queue,
+                llm.run([LLMMessagesQueueFrame(messages)]),
+            )
             await transport.stop_when_done()
 
         await transport.run()