minor cleanup

src/dailyai/queue_aggregators.py

@@ -34,6 +34,7 @@ class LLMContextAggregator(AIService):
             bot_participant_id=None,
             complete_sentences=True,
             pass_through=True):
+        super().__init__()
         self.messages = messages
         self.bot_participant_id = bot_participant_id
         self.role = role
@@ -82,5 +83,5 @@ class LLMAssistantContextAggregator(LLMContextAggregator):
         self, messages: list[dict], bot_participant_id=None, complete_sentences=True
     ):
         super().__init__(
-            messages, "assistan", bot_participant_id, complete_sentences, pass_through=True
+            messages, "assistant", bot_participant_id, complete_sentences, pass_through=True
         )

src/dailyai/services/daily_transport_service.py

@@ -391,10 +391,10 @@ class DailyTransportService(EventHandler):
                                 all_audio_frames.extend(chunk)
 
                                 b.extend(chunk)
-                                l = len(b) - (len(b) % smallest_write_size)
+                                truncated_length: int = len(b) - (len(b) % smallest_write_size)
-                                if l:
+                                if truncated_length:
-                                    self.mic.write_frames(bytes(b[:l]))
+                                    self.mic.write_frames(bytes(b[:truncated_length]))
-                                    b = b[l:]
+                                    b = b[truncated_length:]
                             elif isinstance(frame, ImageQueueFrame):
                                 self._set_image(frame.image)
                             elif isinstance(frame, SpriteQueueFrame):
@@ -406,7 +406,8 @@ class DailyTransportService(EventHandler):
                         # if there are leftover audio bytes, write them now; failing to do so
                         # can cause static in the audio stream.
                         if len(b):
-                            self.mic.write_frames(bytes(b))
+                            truncated_length = len(b) - (len(b) % 160)
+                            self.mic.write_frames(bytes(b[:truncated_length]))
                             b = bytearray()
 
                         if isinstance(frame, StartStreamQueueFrame):

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

@@ -1,6 +1,5 @@
 import argparse
 import asyncio
-import logging
 
 import aiohttp
 

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

@@ -5,7 +5,7 @@ import aiohttp
 
 from dailyai.queue_frame import TextQueueFrame
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.open_ai_services import OpenAIImageGenService
+from dailyai.services.fal_ai_services import FalImageGenService
 
 local_joined = False
 participant_joined = False
@@ -25,14 +25,14 @@ async def main(room_url):
         transport.camera_width = 1024
         transport.camera_height = 1024
 
-        imagegen = OpenAIImageGenService(image_size="1024x1024", aiohttp_session=session)
+        imagegen = FalImageGenService(image_size="1024x1024", aiohttp_session=session)
         image_task = asyncio.create_task(
             imagegen.run_to_queue(
                 transport.send_queue, [
                     TextQueueFrame("a cat in the style of picasso")]))
 
-        @transport.event_handler("on_participant_joined")
+        @transport.event_handler("on_first_other_participant_joined")
-        async def on_participant_joined(transport, participant):
+        async def on_first_other_participant_joined(transport):
             await image_task
 
         await transport.run()

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

@@ -40,11 +40,8 @@ async def main(room_url: str):
             )
         )
 
-        @transport.event_handler("on_participant_joined")
+        @transport.event_handler("on_first_other_participant_joined")
-        async def on_joined(transport, participant):
+        async def on_first_other_participant_joined(transport):
-            if participant["id"] == transport.my_participant_id:
-                return
-
             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():

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

@@ -1,14 +1,10 @@
 import aiohttp
 import argparse
 import asyncio
-import requests
-import time
-import urllib.parse
 
 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.queue_frame import QueueFrame
 
 async def main(room_url:str):
     async with aiohttp.ClientSession() as session:
@@ -43,15 +39,17 @@ async def main(room_url:str):
                 # Run the LLMs synchronously for the back-and-forth
                 bot1_msg = await llm.run_llm(bot1_messages)
                 print(f"bot1_msg: {bot1_msg}")
-                bot1_messages.append({"role": "assistant", "content": bot1_msg})
+                if bot1_msg:
-                bot2_messages.append({"role": "user", "content": bot1_msg})
+                    bot1_messages.append({"role": "assistant", "content": bot1_msg})
+                    bot2_messages.append({"role": "user", "content": bot1_msg})
 
                 await tts1.say(bot1_msg, transport.send_queue)
 
                 bot2_msg = await llm.run_llm(bot2_messages)
                 print(f"bot2_msg: {bot2_msg}")
-                bot2_messages.append({"role": "assistant", "content": bot2_msg})
+                if bot2_msg:
-                bot1_messages.append({"role": "user", "content": bot2_msg})
+                    bot2_messages.append({"role": "assistant", "content": bot2_msg})
+                    bot1_messages.append({"role": "user", "content": bot2_msg})
 
                 await tts2.say(bot2_msg, transport.send_queue)
 

src/samples/foundational/08a-characters-arguing.py

@@ -1,16 +1,12 @@
 import aiohttp
 import argparse
 import asyncio
-import requests
-import time
-import urllib.parse
 
 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.fal_ai_services import FalImageGenService
-from dailyai.services.open_ai_services import OpenAIImageGenService
+from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
-from dailyai.queue_frame import QueueFrame, AudioQueueFrame, ImageQueueFrame
 
 async def main(room_url:str):
     async with aiohttp.ClientSession() as session:
@@ -34,7 +30,6 @@ async def main(room_url:str):
         tts1 = AzureTTSService()
         tts2 = ElevenLabsTTSService(session)
         dalle = FalImageGenService(image_size="1024x1024", aiohttp_session=session)
-        # dalle = OpenAIImageGenService(image_size="1024x1024")
 
         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."},
@@ -42,26 +37,28 @@ async def main(room_url:str):
         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."},
         ]
-        
+
         async def get_bot1_statement():
             # Run the LLMs synchronously for the back-and-forth
             bot1_msg = await llm.run_llm(bot1_messages)
             print(f"bot1_msg: {bot1_msg}")
-            bot1_messages.append({"role": "assistant", "content": bot1_msg})
+            if bot1_msg:
-            bot2_messages.append({"role": "user", "content": bot1_msg})
+                bot1_messages.append({"role": "assistant", "content": bot1_msg})
+                bot2_messages.append({"role": "user", "content": bot1_msg})
 
             all_audio = bytearray()
             async for audio in tts1.run_tts(bot1_msg):
                 all_audio.extend(audio)
 
             return all_audio
-        
+
         async def get_bot2_statement():
             # Run the LLMs synchronously for the back-and-forth
             bot2_msg = await llm.run_llm(bot2_messages)
             print(f"bot2_msg: {bot2_msg}")
-            bot2_messages.append({"role": "assistant", "content": bot2_msg})
+            if bot2_msg:
-            bot1_messages.append({"role": "user", "content": bot2_msg})
+                bot2_messages.append({"role": "assistant", "content": bot2_msg})
+                bot1_messages.append({"role": "user", "content": bot2_msg})
 
             all_audio = bytearray()
             async for audio in tts2.run_tts(bot2_msg):

src/samples/foundational/08b-debate-generator.py

@@ -1,16 +1,12 @@
 import aiohttp
 import argparse
 import asyncio
-import requests
-import time
-import urllib.parse
 
 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.fal_ai_services import FalImageGenService
-from dailyai.services.open_ai_services import OpenAIImageGenService
+from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
-from dailyai.queue_frame import QueueFrame, AudioQueueFrame, ImageQueueFrame
 
 async def main(room_url:str):
     async with aiohttp.ClientSession() as session:
@@ -39,7 +35,7 @@ async def main(room_url:str):
         topic = "Are pokemon edible?"
         affirmative = "A woman dressed as a cowboy, outside on a ranch"
         negative = "Pikachu in a business suit"
-        
+
         topic = "Is a hot dog a sandwich?"
         affirmative = "A woman conservatively dressed as a librarian in a library surrounded by books"
         negative = "A cat dressed in a hot dog costume"
@@ -52,26 +48,28 @@ async def main(room_url:str):
         bot2_messages = [
             {"role": "system", "content": f"You are {negative}. You're in a debate, and the topic is: '{topic}'. You're arguing the negative.  Debate this with the user, only responding with a few sentences. Don't ever agree with the user."},
         ]
-        
+
         async def get_bot1_statement():
             # Run the LLMs synchronously for the back-and-forth
             bot1_msg = await llm.run_llm(bot1_messages)
             print(f"bot1_msg: {bot1_msg}")
-            bot1_messages.append({"role": "assistant", "content": bot1_msg})
+            if bot1_msg:
-            bot2_messages.append({"role": "user", "content": bot1_msg})
+                bot1_messages.append({"role": "assistant", "content": bot1_msg})
+                bot2_messages.append({"role": "user", "content": bot1_msg})
 
             all_audio = bytearray()
             async for audio in tts1.run_tts(bot1_msg):
                 all_audio.extend(audio)
 
             return all_audio
-        
+
         async def get_bot2_statement():
             # Run the LLMs synchronously for the back-and-forth
             bot2_msg = await llm.run_llm(bot2_messages)
             print(f"bot2_msg: {bot2_msg}")
-            bot2_messages.append({"role": "assistant", "content": bot2_msg})
+            if bot2_msg:
-            bot1_messages.append({"role": "user", "content": bot2_msg})
+                bot2_messages.append({"role": "assistant", "content": bot2_msg})
+                bot1_messages.append({"role": "user", "content": bot2_msg})
 
             all_audio = bytearray()
             async for audio in tts2.run_tts(bot2_msg):

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

@@ -73,7 +73,7 @@ class TranscriptFilter(AIService):
 
 
 class NameCheckFilter(AIService):
-    def __init__(self, names=None):
+    def __init__(self, names:list[str]):
         self.names = names
         self.sentence = ""