cleaned up example logging (#46)

src/examples/foundational/01-say-one-thing.py

@@ -1,5 +1,6 @@
 import asyncio
 import aiohttp
+import logging
 import os
 
 from dailyai.services.daily_transport_service import DailyTransportService
@@ -8,6 +9,9 @@ from dailyai.services.playht_ai_service import PlayHTAIService
 
 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:
@@ -21,24 +25,14 @@ async def main(room_url):
         # to the daily-python basic API
         meeting_duration_minutes = 5
         transport = DailyTransportService(
-            room_url,
+            room_url, None, "Say One Thing", meeting_duration_minutes, mic_enabled=True
-            None,
-            "Say One Thing",
-            meeting_duration_minutes,
-            mic_enabled=True
         )
 
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        """
-        tts = PlayHTAIService(
-            api_key=os.getenv("PLAY_HT_API_KEY"),
-            user_id=os.getenv("PLAY_HT_USER_ID"),
-            voice_url=os.getenv("PLAY_HT_VOICE_URL"),
         )
-        """
 
         # Register an event handler so we can play the audio when the participant joins.
         @transport.event_handler("on_participant_joined")
@@ -56,7 +50,7 @@ async def main(room_url):
             await transport.stop_when_done()
 
         await transport.run()
-        del(tts)
+        del tts
 
 
 if __name__ == "__main__":

src/examples/foundational/01a-local-transport.py

@@ -1,17 +1,20 @@
 import asyncio
 import aiohttp
+import logging
 import os
 
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.services.local_transport_service import LocalTransportService
 
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
 
 async def main():
     async with aiohttp.ClientSession() as session:
         meeting_duration_minutes = 1
         transport = LocalTransportService(
-            duration_minutes=meeting_duration_minutes,
+            duration_minutes=meeting_duration_minutes, mic_enabled=True
-            mic_enabled=True
         )
         tts = ElevenLabsTTSService(
             aiohttp_session=session,

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

@@ -1,5 +1,6 @@
 import asyncio
 import os
+import logging
 
 import aiohttp
 
@@ -11,6 +12,9 @@ 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:
@@ -20,25 +24,23 @@ async def main(room_url):
             None,
             "Say One Thing From an LLM",
             duration_minutes=meeting_duration_minutes,
-            mic_enabled=True
+            mic_enabled=True,
         )
 
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        # tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+        )
-        # tts = DeepgramTTSService(aiohttp_session=session, api_key=os.getenv("DEEPGRAM_API_KEY"), voice=os.getenv("DEEPGRAM_VOICE"))
+        llm = OpenAILLMService(
-
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        llm = AzureLLMService(
+        )
-            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+        messages = [
-            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            {
-            model=os.getenv("AZURE_CHATGPT_MODEL"))
+                "role": "system",
-        # llm = OpenAILLMService(api_key=os.getenv("OPENAI_CHATGPT_API_KEY"))
+                "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
-        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,

src/examples/foundational/03-still-frame.py

@@ -1,5 +1,6 @@
 import asyncio
 import aiohttp
+import logging
 import os
 
 from dailyai.pipeline.frames import TextFrame
@@ -10,6 +11,9 @@ from dailyai.services.azure_ai_services import AzureImageGenServiceREST
 
 from examples.support.runner import configure
 
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
 local_joined = False
 participant_joined = False
 
@@ -25,21 +29,23 @@ async def main(room_url):
             mic_enabled=False,
             camera_enabled=True,
             camera_width=1024,
-            camera_height=1024
+            camera_height=1024,
         )
 
         imagegen = FalImageGenService(
             image_size="1024x1024",
             aiohttp_session=session,
             key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"))
+            key_secret=os.getenv("FAL_KEY_SECRET"),
+        )
         # imagegen = OpenAIImageGenService(aiohttp_session=session, api_key=os.getenv("OPENAI_DALLE_API_KEY"), image_size="1024x1024")
         # imagegen = AzureImageGenServiceREST(image_size="1024x1024", aiohttp_session=session, api_key=os.getenv("AZURE_DALLE_API_KEY"), endpoint=os.getenv("AZURE_DALLE_ENDPOINT"), model=os.getenv("AZURE_DALLE_MODEL"))
 
         image_task = asyncio.create_task(
             imagegen.run_to_queue(
-                transport.send_queue, [
+                transport.send_queue, [TextFrame("a cat in the style of picasso")]
-                    TextFrame("a cat in the style of picasso")]))
+            )
+        )
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):

src/examples/foundational/03a-image-local.py

@@ -1,5 +1,6 @@
 import asyncio
 import aiohttp
+import logging
 import os
 
 import tkinter as tk
@@ -8,6 +9,10 @@ from dailyai.pipeline.frames import TextFrame
 from dailyai.services.fal_ai_services import FalImageGenService
 from dailyai.services.local_transport_service import LocalTransportService
 
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
+
 local_joined = False
 participant_joined = False
 
@@ -46,5 +51,6 @@ async def main():
 
         await asyncio.gather(transport.run(), image_task, run_tk())
 
+
 if __name__ == "__main__":
     asyncio.run(main())

src/examples/foundational/04-utterance-and-speech.py

@@ -1,4 +1,5 @@
 import asyncio
+import logging
 import os
 
 import aiohttp
@@ -8,9 +9,12 @@ from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.pipeline.frames import EndFrame, LLMMessagesQueueFrame
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-
 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: str):
     async with aiohttp.ClientSession() as session:
@@ -21,20 +25,23 @@ async def main(room_url: str):
             duration_minutes=1,
             mic_enabled=True,
             mic_sample_rate=16000,
-            camera_enabled=False
+            camera_enabled=False,
         )
 
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
             endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"))
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
         azure_tts = AzureTTSService(
             api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-            region=os.getenv("AZURE_SPEECH_REGION"))
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
         elevenlabs_tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
 
         messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
 
@@ -43,14 +50,19 @@ async def main(room_url: str):
         # speak the LLM response.
         buffer_queue = asyncio.Queue()
         source_queue = asyncio.Queue()
-        pipeline = Pipeline(source = source_queue, sink=buffer_queue, processors=[llm, elevenlabs_tts])
+        pipeline = Pipeline(
+            source=source_queue, sink=buffer_queue, processors=[llm, elevenlabs_tts]
+        )
         await source_queue.put(LLMMessagesQueueFrame(messages))
         await source_queue.put(EndFrame())
         pipeline_run_task = pipeline.run_pipeline()
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            await azure_tts.say("My friend the LLM is now going to tell a joke about llamas.", transport.send_queue)
+            await azure_tts.say(
+                "My friend the LLM is now going to tell a joke about llamas.",
+                transport.send_queue,
+            )
 
             async def buffer_to_send_queue():
                 while True:

src/examples/foundational/05-sync-speech-and-image.py

@@ -2,11 +2,27 @@ import asyncio
 from re import S
 import aiohttp
 import os
-from dailyai.pipeline.aggregators import GatedAggregator, LLMFullResponseAggregator, ParallelPipeline, SentenceAggregator
+import logging
 
-from dailyai.pipeline.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesQueueFrame, LLMResponseStartFrame
+from dailyai.pipeline.aggregators import (
+    GatedAggregator,
+    LLMFullResponseAggregator,
+    ParallelPipeline,
+    SentenceAggregator,
+)
+from dailyai.pipeline.frames import (
+    AudioFrame,
+    EndFrame,
+    ImageFrame,
+    LLMMessagesQueueFrame,
+    LLMResponseStartFrame,
+)
 from dailyai.pipeline.pipeline import Pipeline
-from dailyai.services.azure_ai_services import AzureLLMService, AzureImageGenServiceREST, AzureTTSService
+from dailyai.services.azure_ai_services import (
+    AzureLLMService,
+    AzureImageGenServiceREST,
+    AzureTTSService,
+)
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.fal_ai_services import FalImageGenService
@@ -14,6 +30,10 @@ from dailyai.services.open_ai_services import OpenAIImageGenService
 
 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:
@@ -27,23 +47,26 @@ async def main(room_url):
             camera_enabled=True,
             mic_sample_rate=16000,
             camera_width=1024,
-            camera_height=1024
+            camera_height=1024,
         )
 
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
             endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"))
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="ErXwobaYiN019PkySvjV")
+            voice_id="ErXwobaYiN019PkySvjV",
+        )
 
         dalle = FalImageGenService(
             image_size="1024x1024",
             aiohttp_session=session,
             key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"))
+            key_secret=os.getenv("FAL_KEY_SECRET"),
+        )
 
         source_queue = asyncio.Queue()
 
@@ -101,6 +124,7 @@ async def main(room_url):
 
         await transport.run()
 
+
 if __name__ == "__main__":
     (url, token) = configure()
     asyncio.run(main(url))

src/examples/foundational/05a-local-sync-speech-and-text.py

@@ -1,6 +1,7 @@
 import aiohttp
 import argparse
 import asyncio
+import logging
 import tkinter as tk
 import os
 
@@ -10,6 +11,10 @@ from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.services.fal_ai_services import FalImageGenService
 from dailyai.services.local_transport_service import LocalTransportService
 
+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:
@@ -67,9 +72,7 @@ async def main(room_url):
             to_speak = f"{month}: {image_description}"
             audio_task = asyncio.create_task(get_all_audio(to_speak))
             image_task = asyncio.create_task(dalle.run_image_gen(image_description))
-            (audio, image_data) = await asyncio.gather(
+            (audio, image_data) = await asyncio.gather(audio_task, image_task)
-                audio_task, image_task
-            )
 
             return {
                 "month": month,
@@ -123,6 +126,7 @@ async def main(room_url):
 
         await asyncio.gather(transport.run(), show_images(), run_tk())
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
     parser.add_argument(

src/examples/foundational/06-listen-and-respond.py

@@ -1,65 +1,81 @@
 import asyncio
+import aiohttp
+import logging
 import os
 from dailyai.pipeline.pipeline import Pipeline
 
 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.open_ai_services import OpenAILLMService
 from dailyai.services.ai_services import FrameLogger
-from dailyai.pipeline.aggregators import LLMAssistantContextAggregator, LLMUserContextAggregator
+from dailyai.pipeline.aggregators import (
+    LLMAssistantContextAggregator,
+    LLMUserContextAggregator,
+)
 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: str, token):
-    transport = DailyTransportService(
+    async with aiohttp.ClientSession() as session:
-        room_url,
+        transport = DailyTransportService(
-        token,
+            room_url,
-        "Respond bot",
+            token,
-        duration_minutes=5,
+            "Respond bot",
-        start_transcription=True,
+            duration_minutes=5,
-        mic_enabled=True,
+            start_transcription=True,
-        mic_sample_rate=16000,
+            mic_enabled=True,
-        camera_enabled=False,
+            mic_sample_rate=16000,
-        vad_enabled=True
+            camera_enabled=False,
-    )
+            vad_enabled=True,
-
-    llm = AzureLLMService(
-        api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
-        endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-        model=os.getenv("AZURE_CHATGPT_MODEL"))
-    tts = AzureTTSService(
-        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-        region=os.getenv("AZURE_SPEECH_REGION"))
-    fl = FrameLogger("Inner")
-    fl2 = FrameLogger("Outer")
-    @transport.event_handler("on_first_other_participant_joined")
-    async def on_first_other_participant_joined(transport):
-        await tts.say("Hi, I'm listening!", transport.send_queue)
-
-    async def handle_transcriptions():
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
-        tma_out = LLMAssistantContextAggregator(messages, transport._my_participant_id)
-        pipeline = Pipeline(
-            processors=[
-                fl,
-                tma_in,
-                llm,
-                fl2,
-                tts,
-                tma_out,
-            ],
         )
-        await transport.run_uninterruptible_pipeline(pipeline)
 
-    transport.transcription_settings["extra"]["endpointing"] = True
+        tts = ElevenLabsTTSService(
-    transport.transcription_settings["extra"]["punctuate"] = True
+            aiohttp_session=session,
-    await asyncio.gather(transport.run(), handle_transcriptions())
+            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"
+        )
+        fl = FrameLogger("Inner")
+        fl2 = FrameLogger("Outer")
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            await tts.say("Hi, I'm listening!", transport.send_queue)
+
+        async def handle_transcriptions():
+            messages = [
+                {
+                    "role": "system",
+                    "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way.",
+                },
+            ]
+
+            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+            tma_out = LLMAssistantContextAggregator(
+                messages, transport._my_participant_id
+            )
+            pipeline = Pipeline(
+                processors=[
+                    fl,
+                    tma_in,
+                    llm,
+                    fl2,
+                    tts,
+                    tma_out,
+                ],
+            )
+            await transport.run_uninterruptible_pipeline(pipeline)
+
+        transport.transcription_settings["extra"]["endpointing"] = True
+        transport.transcription_settings["extra"]["punctuate"] = True
+        await asyncio.gather(transport.run(), handle_transcriptions())
 
 
 if __name__ == "__main__":

src/examples/foundational/06a-image-sync.py

@@ -1,23 +1,29 @@
 import argparse
 import asyncio
 import os
+import logging
 from typing import AsyncGenerator
 import aiohttp
 import requests
 import time
 import urllib.parse
-
 from PIL import Image
-from dailyai.pipeline.frames import ImageFrame, Frame
 
+from dailyai.pipeline.frames import ImageFrame, Frame
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.ai_services import AIService
-from dailyai.pipeline.aggregators import LLMAssistantContextAggregator, LLMUserContextAggregator
+from dailyai.pipeline.aggregators import (
+    LLMAssistantContextAggregator,
+    LLMUserContextAggregator,
+)
 from dailyai.services.fal_ai_services import FalImageGenService
-
 from examples.support.runner import configure
 
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
+
 
 class ImageSyncAggregator(AIService):
     def __init__(self, speaking_path: str, waiting_path: str):
@@ -50,15 +56,18 @@ async def main(room_url: str, token):
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
             endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"))
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
         tts = AzureTTSService(
             api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-            region=os.getenv("AZURE_SPEECH_REGION"))
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
         img = FalImageGenService(
             image_size="1024x1024",
             aiohttp_session=session,
             key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"))
+            key_secret=os.getenv("FAL_KEY_SECRET"),
+        )
 
         async def get_images():
             get_speaking_task = asyncio.create_task(
@@ -80,12 +89,13 @@ async def main(room_url: str, token):
 
         async def handle_transcriptions():
             messages = [
-                {"role": "system", "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way."},
+                {
+                    "role": "system",
+                    "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way.",
+                },
             ]
 
-            tma_in = LLMUserContextAggregator(
+            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
-                messages, transport._my_participant_id
-            )
             tma_out = LLMAssistantContextAggregator(
                 messages, transport._my_participant_id
             )
@@ -96,14 +106,8 @@ async def main(room_url: str, token):
             await tts.run_to_queue(
                 transport.send_queue,
                 image_sync_aggregator.run(
-                    tma_out.run(
+                    tma_out.run(llm.run(tma_in.run(transport.get_receive_frames())))
-                        llm.run(
+                ),
-                            tma_in.run(
-                                transport.get_receive_frames()
-                            )
-                        )
-                    )
-                )
             )
 
         transport.transcription_settings["extra"]["punctuate"] = True

src/examples/foundational/07-interruptible.py

@@ -1,14 +1,23 @@
 import asyncio
 import aiohttp
+import logging
 import os
-from dailyai.pipeline.aggregators import LLMAssistantContextAggregator, LLMResponseAggregator, LLMUserContextAggregator, UserResponseAggregator
+from dailyai.pipeline.aggregators import (
+    LLMAssistantContextAggregator,
+    LLMResponseAggregator,
+    LLMUserContextAggregator,
+    UserResponseAggregator,
+)
 
 from dailyai.pipeline.pipeline import Pipeline
 from dailyai.services.ai_services import FrameLogger
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from examples.support.runner import configure
 
-from 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: str, token):
@@ -28,10 +37,12 @@ async def main(room_url: str, token):
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
             endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"))
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
         tts = AzureTTSService(
             api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-            region=os.getenv("AZURE_SPEECH_REGION"))
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
 
         pipeline = Pipeline([FrameLogger(), llm, FrameLogger(), tts])
 
@@ -41,17 +52,16 @@ async def main(room_url: str, token):
 
         async def run_conversation():
             messages = [
-                {"role": "system", "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way."},
+                {
+                    "role": "system",
+                    "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way.",
+                },
             ]
 
             await transport.run_interruptible_pipeline(
                 pipeline,
-                post_processor=LLMResponseAggregator(
+                post_processor=LLMResponseAggregator(messages),
-                    messages
+                pre_processor=UserResponseAggregator(messages),
-                ),
-                pre_processor=UserResponseAggregator(
-                    messages
-                ),
             )
 
         transport.transcription_settings["extra"]["punctuate"] = False

src/examples/foundational/08-bots-arguing.py

@@ -1,5 +1,6 @@
 import aiohttp
 import asyncio
+import logging
 import os
 
 from dailyai.services.daily_transport_service import DailyTransportService
@@ -7,9 +8,12 @@ from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.services.fal_ai_services import FalImageGenService
 from dailyai.pipeline.frames import AudioFrame, ImageFrame
-
 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: str):
     async with aiohttp.ClientSession() as session:
@@ -22,33 +26,41 @@ async def main(room_url: str):
             mic_sample_rate=16000,
             camera_enabled=True,
             camera_width=1024,
-            camera_height=1024
+            camera_height=1024,
         )
 
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
             endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"))
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
         tts1 = AzureTTSService(
             api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-            region=os.getenv("AZURE_SPEECH_REGION"))
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
         tts2 = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="jBpfuIE2acCO8z3wKNLl")
+            voice_id="jBpfuIE2acCO8z3wKNLl",
+        )
         dalle = FalImageGenService(
             image_size="1024x1024",
             aiohttp_session=session,
             key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"))
+            key_secret=os.getenv("FAL_KEY_SECRET"),
+        )
 
         bot1_messages = [
-            {"role": "system", "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long."},
+            {
+                "role": "system",
+                "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long.",
+            },
         ]
         bot2_messages = [
             {
                 "role": "system",
-                "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich."},
+                "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich.",
+            },
         ]
 
         async def get_bot1_statement():

src/examples/foundational/10-wake-word.py

@@ -1,15 +1,18 @@
 import aiohttp
 import asyncio
+import logging
 import os
 import random
 from typing import AsyncGenerator
-
 from PIL import Image
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.pipeline.aggregators import LLMUserContextAggregator, LLMAssistantContextAggregator
+from dailyai.pipeline.aggregators import (
+    LLMUserContextAggregator,
+    LLMAssistantContextAggregator,
+)
 from dailyai.pipeline.frames import (
     Frame,
     TextFrame,
@@ -18,19 +21,21 @@ from dailyai.pipeline.frames import (
     TranscriptionQueueFrame,
 )
 from dailyai.services.ai_services import AIService
-
 from examples.support.runner import configure
 
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
 
 sprites = {}
 image_files = [
-    'sc-default.png',
+    "sc-default.png",
-    'sc-talk.png',
+    "sc-talk.png",
-    'sc-listen-1.png',
+    "sc-listen-1.png",
-    'sc-think-1.png',
+    "sc-think-1.png",
-    'sc-think-2.png',
+    "sc-think-2.png",
-    'sc-think-3.png',
+    "sc-think-3.png",
-    'sc-think-4.png'
+    "sc-think-4.png",
 ]
 
 script_dir = os.path.dirname(__file__)
@@ -47,16 +52,17 @@ for file in image_files:
 # When the bot isn't talking, show a static image of the cat listening
 quiet_frame = ImageFrame("", sprites["sc-listen-1.png"])
 # When the bot is talking, build an animation from two sprites
-talking_list = [sprites['sc-default.png'], sprites['sc-talk.png']]
+talking_list = [sprites["sc-default.png"], sprites["sc-talk.png"]]
 talking = [random.choice(talking_list) for x in range(30)]
 talking_frame = SpriteFrame(images=talking)
 
 # TODO: Support "thinking" as soon as we get a valid transcript, while LLM is processing
 thinking_list = [
-    sprites['sc-think-1.png'],
+    sprites["sc-think-1.png"],
-    sprites['sc-think-2.png'],
+    sprites["sc-think-2.png"],
-    sprites['sc-think-3.png'],
+    sprites["sc-think-3.png"],
-    sprites['sc-think-4.png']]
+    sprites["sc-think-4.png"],
+]
 thinking_frame = SpriteFrame(images=thinking_list)
 
 
@@ -115,7 +121,7 @@ async def main(room_url: str, token):
             mic_sample_rate=16000,
             camera_enabled=True,
             camera_width=720,
-            camera_height=1280
+            camera_height=1280,
         )
         transport._mic_enabled = True
         transport._mic_sample_rate = 16000
@@ -126,25 +132,31 @@ async def main(room_url: str, token):
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
             endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"))
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="jBpfuIE2acCO8z3wKNLl")
+            voice_id="jBpfuIE2acCO8z3wKNLl",
+        )
         isa = ImageSyncAggregator()
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            await tts.say("Hi! If you want to talk to me, just say 'hey Santa Cat'.", transport.send_queue)
+            await tts.say(
+                "Hi! If you want to talk to me, just say 'hey Santa Cat'.",
+                transport.send_queue,
+            )
 
         async def handle_transcriptions():
             messages = [
-                {"role": "system", "content": "You are Santa Cat, a cat that lives in Santa's workshop at the North Pole. You should be clever, and a bit sarcastic. You should also tell jokes every once in a while.  Your responses should only be a few sentences long."},
+                {
+                    "role": "system",
+                    "content": "You are Santa Cat, a cat that lives in Santa's workshop at the North Pole. You should be clever, and a bit sarcastic. You should also tell jokes every once in a while.  Your responses should only be a few sentences long.",
+                },
             ]
 
-            tma_in = LLMUserContextAggregator(
+            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
-                messages, transport._my_participant_id
-            )
             tma_out = LLMAssistantContextAggregator(
                 messages, transport._my_participant_id
             )
@@ -155,16 +167,10 @@ async def main(room_url: str, token):
                 isa.run(
                     tma_out.run(
                         llm.run(
-                            tma_in.run(
+                            tma_in.run(ncf.run(tf.run(transport.get_receive_frames())))
-                                ncf.run(
-                                    tf.run(
-                                        transport.get_receive_frames()
-                                    )
-                                )
-                            )
                         )
                     )
-                )
+                ),
             )
 
         async def starting_image():

src/examples/foundational/11-sound-effects.py

@@ -14,7 +14,7 @@ from typing import AsyncGenerator
 
 from examples.support.runner import configure
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")  # or whatever
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
 logger = logging.getLogger("dailyai")
 logger.setLevel(logging.DEBUG)
 

src/examples/foundational/13-whisper-transcription.py

@@ -1,10 +1,14 @@
 import asyncio
+import logging
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.whisper_ai_services import WhisperSTTService
-
 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: str):
     transport = DailyTransportService(
@@ -14,7 +18,7 @@ async def main(room_url: str):
         start_transcription=True,
         mic_enabled=False,
         camera_enabled=False,
-        speaker_enabled=True
+        speaker_enabled=True,
     )
 
     stt = WhisperSTTService()
@@ -28,9 +32,9 @@ async def main(room_url: str):
 
     async def handle_speaker():
         await stt.run_to_queue(
-            transcription_output_queue,
+            transcription_output_queue, transport.get_receive_frames()
-            transport.get_receive_frames()
         )
+
     await asyncio.gather(transport.run(), handle_speaker(), handle_transcription())
 
 

src/examples/foundational/13a-whisper-local.py

@@ -1,11 +1,16 @@
 import argparse
 import asyncio
+import logging
 import wave
 from dailyai.pipeline.frames import EndFrame, TranscriptionQueueFrame
 
 from dailyai.services.local_transport_service import LocalTransportService
 from dailyai.services.whisper_ai_services import WhisperSTTService
 
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
+
 
 async def main(room_url: str):
     global transport
@@ -17,7 +22,7 @@ async def main(room_url: str):
         camera_enabled=False,
         speaker_enabled=True,
         duration_minutes=meeting_duration_minutes,
-        start_transcription=True
+        start_transcription=True,
     )
     stt = WhisperSTTService()
     transcription_output_queue = asyncio.Queue()

src/examples/starter-apps/patient-intake.py

@@ -2,35 +2,48 @@ import aiohttp
 import asyncio
 import json
 import random
+import logging
 import os
 import re
 import wave
 from typing import AsyncGenerator
 from PIL import Image
 
-import sys
-print('\n'.join(sys.path))
-
 from dailyai.pipeline.pipeline import Pipeline
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.open_ai_services import OpenAILLMService
 from dailyai.services.deepgram_ai_services import DeepgramTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.pipeline.aggregators import LLMAssistantContextAggregator, LLMContextAggregator, LLMUserContextAggregator, UserResponseAggregator, LLMResponseAggregator
+from dailyai.pipeline.aggregators import (
+    LLMAssistantContextAggregator,
+    LLMContextAggregator,
+    LLMUserContextAggregator,
+    UserResponseAggregator,
+    LLMResponseAggregator,
+)
 from examples.support.runner import configure
-from dailyai.pipeline.frames import LLMMessagesQueueFrame, TranscriptionQueueFrame, Frame, TextFrame, LLMFunctionCallFrame, LLMFunctionStartFrame, LLMResponseEndFrame, StartFrame, AudioFrame, SpriteFrame, ImageFrame
+from dailyai.pipeline.frames import (
+    LLMMessagesQueueFrame,
+    TranscriptionQueueFrame,
+    Frame,
+    TextFrame,
+    LLMFunctionCallFrame,
+    LLMFunctionStartFrame,
+    LLMResponseEndFrame,
+    StartFrame,
+    AudioFrame,
+    SpriteFrame,
+    ImageFrame,
+)
 from dailyai.services.ai_services import FrameLogger, AIService
 
-import logging
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
 
 sounds = {}
-sound_files = [
+sound_files = ["clack-short.wav", "clack.wav", "clack-short-quiet.wav"]
-    'clack-short.wav',
-    'clack.wav',
-    'clack-short-quiet.wav'
-]
 
 script_dir = os.path.dirname(__file__)
 
@@ -48,9 +61,11 @@ steps = [
     {
         "prompt": "Start by introducing yourself. Then, ask the user to confirm their identity by telling you their birthday, including the year. When they answer with their birthday, call the verify_birthday function.",
         "run_async": False,
-        "failed": "The user provided an incorrect birthday. Ask them for their birthday again. When they answer, call the verify_birthday function.", "tools": [{
+        "failed": "The user provided an incorrect birthday. Ask them for their birthday again. When they answer, call the verify_birthday function.",
-            "type": "function",
+        "tools": [
-            "function": {
+            {
+                "type": "function",
+                "function": {
                     "name": "verify_birthday",
                     "description": "Use this function to verify the user has provided their correct birthday.",
                     "parameters": {
@@ -58,18 +73,21 @@ steps = [
                         "properties": {
                             "birthday": {
                                 "type": "string",
-                                "description": "The user's birthdate, including the year. The user can provide it in any format, but convert it to YYYY-MM-DD format to call this function."
+                                "description": "The user's birthdate, including the year. The user can provide it in any format, but convert it to YYYY-MM-DD format to call this function.",
                             }
-                        }
+                        },
-                    }
+                    },
+                },
             }
-        }]},
+        ],
+    },
     {
         "prompt": "Next, thank the user for confirming their identity, then ask the user to list their current prescriptions. Each prescription needs to have a medication name and a dosage. Do not call the list_prescriptions function with any unknown dosages.",
         "run_async": True,
-        "tools": [{
+        "tools": [
-            "type": "function",
+            {
-            "function": {
+                "type": "function",
+                "function": {
                     "name": "list_prescriptions",
                     "description": "Once the user has provided a list of their prescription medications, call this function.",
                     "parameters": {
@@ -82,19 +100,20 @@ steps = [
                                     "properties": {
                                         "medication": {
                                             "type": "string",
-                                            "description": "The medication's name"
+                                            "description": "The medication's name",
                                         },
                                         "dosage": {
                                             "type": "string",
-                                            "description": "The prescription's dosage"
+                                            "description": "The prescription's dosage",
-                                        }
+                                        },
-                                    }
+                                    },
-                                }
+                                },
                             }
-                        }
+                        },
-                    }
+                    },
+                },
             }
-        }]
+        ],
     },
     {
         "prompt": "Next, ask the user if they have any allergies. Once they have listed their allergies or confirmed they don't have any, call the list_allergies function.",
@@ -115,16 +134,16 @@ steps = [
                                     "properties": {
                                         "name": {
                                             "type": "string",
-                                            "description": "What the user is allergic to"
+                                            "description": "What the user is allergic to",
                                         }
-                                    }
+                                    },
-                                }
+                                },
                             }
-                        }
+                        },
-                    }
+                    },
-                }
+                },
             }
-        ]
+        ],
     },
     {
         "prompt": "Now ask the user if they have any medical conditions the doctor should know about. Once they've answered the question, call the list_conditions function.",
@@ -145,14 +164,14 @@ steps = [
                                     "properties": {
                                         "name": {
                                             "type": "string",
-                                            "description": "The user's medical condition"
+                                            "description": "The user's medical condition",
                                         }
-                                    }
+                                    },
-                                }
+                                },
                             }
-                        }
+                        },
-                    }
+                    },
-                }
+                },
             },
         ],
     },
@@ -175,20 +194,23 @@ steps = [
                                     "properties": {
                                         "name": {
                                             "type": "string",
-                                            "description": "The user's reason for visiting the doctor"
+                                            "description": "The user's reason for visiting the doctor",
                                         }
-                                    }
+                                    },
-                                }
+                                },
                             }
-                        }
+                        },
-                    }
+                    },
-                }
+                },
             }
-        ]
+        ],
     },
-    {"prompt": "Now, thank the user and end the conversation.",
+    {
-        "run_async": True, "tools": []},
+        "prompt": "Now, thank the user and end the conversation.",
-    {"prompt": "", "run_async": True, "tools": []}
+        "run_async": True,
+        "tools": [],
+    },
+    {"prompt": "", "run_async": True, "tools": []},
 ]
 current_step = 0
 
@@ -219,15 +241,15 @@ class ChecklistProcessor(AIService):
             "list_prescriptions",
             "list_allergies",
             "list_conditions",
-            "list_visit_reasons"
+            "list_visit_reasons",
-
         ]
-        
+
         messages.append(
-            {"role": "system", "content": f"{self._id} {steps[0]['prompt']}"})
+            {"role": "system", "content": f"{self._id} {steps[0]['prompt']}"}
+        )
 
     def verify_birthday(self, args):
-        return args['birthday'] == "1983-01-01"
+        return args["birthday"] == "1983-01-01"
 
     def list_prescriptions(self, args):
         # print(f"--- Prescriptions: {args['prescriptions']}\n")
@@ -250,18 +272,21 @@ class ChecklistProcessor(AIService):
         this_step = steps[current_step]
         # TODO-CB: forcing a global here :/
         self._tools.clear()
-        self._tools.extend(this_step['tools'])
+        self._tools.extend(this_step["tools"])
         if isinstance(frame, LLMFunctionStartFrame):
             print(f"... Preparing function call: {frame.function_name}")
             self._function_name = frame.function_name
-            if this_step['run_async']:
+            if this_step["run_async"]:
                 # Get the LLM talking about the next step before getting the rest
                 # of the function call completion
                 current_step += 1
-                self._messages.append({
+                self._messages.append(
-                    "role": "system", "content": steps[current_step]['prompt']})
+                    {"role": "system", "content": steps[current_step]["prompt"]}
+                )
                 yield LLMMessagesQueueFrame(self._messages)
-                async for frame in llm.process_frame(LLMMessagesQueueFrame(self._messages), tool_choice="none"):
+                async for frame in llm.process_frame(
+                    LLMMessagesQueueFrame(self._messages), tool_choice="none"
+                ):
                     yield frame
             else:
                 # Insert a quick response while we run the function
@@ -270,29 +295,37 @@ class ChecklistProcessor(AIService):
         elif isinstance(frame, LLMFunctionCallFrame):
 
             if frame.function_name and frame.arguments:
-                print(
+                print(f"--> Calling function: {frame.function_name} with arguments:")
-                    f"--> Calling function: {frame.function_name} with arguments:")
+                pretty_json = re.sub(
-                pretty_json = re.sub("\n", "\n    ", json.dumps(
+                    "\n", "\n    ", json.dumps(json.loads(frame.arguments), indent=2)
-                    json.loads(frame.arguments), indent=2))
+                )
                 print(f"--> {pretty_json}\n")
                 if not frame.function_name in self._functions:
-                    raise Exception(f"The LLM tried to call a function named {frame.function_name}, which isn't in the list of known functions. Please check your prompt and/or self._functions.")
+                    raise Exception(
+                        f"The LLM tried to call a function named {frame.function_name}, which isn't in the list of known functions. Please check your prompt and/or self._functions."
+                    )
                 fn = getattr(self, frame.function_name)
                 result = fn(json.loads(frame.arguments))
 
-                if not this_step['run_async']:
+                if not this_step["run_async"]:
                     if result:
                         current_step += 1
-                        self._messages.append({
+                        self._messages.append(
-                            "role": "system", "content": steps[current_step]['prompt']})
+                            {"role": "system", "content": steps[current_step]["prompt"]}
+                        )
                         yield LLMMessagesQueueFrame(self._messages)
-                        async for frame in llm.process_frame(LLMMessagesQueueFrame(self._messages), tool_choice="none"):
+                        async for frame in llm.process_frame(
+                            LLMMessagesQueueFrame(self._messages), tool_choice="none"
+                        ):
                             yield frame
                     else:
-                        self._messages.append({
+                        self._messages.append(
-                            "role": "system", "content": this_step['failed']})
+                            {"role": "system", "content": this_step["failed"]}
+                        )
                         yield LLMMessagesQueueFrame(self._messages)
-                        async for frame in llm.process_frame(LLMMessagesQueueFrame(self._messages), tool_choice="none"):
+                        async for frame in llm.process_frame(
+                            LLMMessagesQueueFrame(self._messages), tool_choice="none"
+                        ):
                             yield frame
                     print(f"<-- Verify result: {result}\n")
 
@@ -315,7 +348,7 @@ async def main(room_url: str, token):
             mic_sample_rate=16000,
             camera_enabled=False,
             start_transcription=True,
-            vad_enabled=True
+            vad_enabled=True,
         )
         # TODO-CB: Go back to vad_enabled
 
@@ -324,12 +357,18 @@ async def main(room_url: str, token):
 
         # llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv(
         #     "AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-        llm = OpenAILLMService(api_key=os.getenv(
+        llm = OpenAILLMService(
-            "OPENAI_CHATGPT_API_KEY"), model="gpt-4-1106-preview", tools=tools)  # gpt-4-1106-preview
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
+            model="gpt-4-1106-preview",
+            tools=tools,
+        )  # gpt-4-1106-preview
         # tts = AzureTTSService(api_key=os.getenv(
         #     "AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
-        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv(
+        tts = ElevenLabsTTSService(
-            "ELEVENLABS_API_KEY"), voice_id="XrExE9yKIg1WjnnlVkGX")  # matilda
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id="XrExE9yKIg1WjnnlVkGX",
+        )  # matilda
         # tts = DeepgramTTSService(aiohttp_session=session, api_key=os.getenv(
         #     "DEEPGRAM_API_KEY"), voice="aura-asteria-en")
 
@@ -345,34 +384,23 @@ async def main(room_url: str, token):
             # TODO-CB: Make sure this message gets into the context somehow
             await tts.run_to_queue(
                 transport.send_queue,
-                    llm.run([LLMMessagesQueueFrame(messages)]),
+                llm.run([LLMMessagesQueueFrame(messages)]),
-
             )
-        
+
         async def handle_intake():
-            pipeline = Pipeline(
+            pipeline = Pipeline(processors=[fl, llm, fl2, checklist, tts])
-                processors=[
+            await transport.run_interruptible_pipeline(
-                    fl,
+                pipeline,
-                    llm,
+                post_processor=LLMResponseAggregator(messages),
-                    fl2,
+                pre_processor=UserResponseAggregator(messages),
-                    checklist,
-                    tts
-                ]
             )
-            await transport.run_interruptible_pipeline(pipeline,
-                post_processor=LLMResponseAggregator(
-                    messages
-                ),
-                pre_processor=UserResponseAggregator(messages)
-                )
-
 
         transport.transcription_settings["extra"]["endpointing"] = True
         transport.transcription_settings["extra"]["punctuate"] = True
         try:
             await asyncio.gather(transport.run(), handle_intake())
         except (asyncio.CancelledError, KeyboardInterrupt):
-            print('whoops')
+            print("whoops")
             transport.stop()