WIP: environment cleanup (#19)

* removed env var usage from SDK services

* started consolidating configure.py

* 1–3 work

* cleaned up the rest

* more cleanup

* cleanup and 05 tinkering

* made fal keys optional

pyproject.toml

@@ -8,6 +8,7 @@ version = "0.0.1"
 description = "Orchestrator for AI bots with Daily"
 dependencies = [
     "daily-python",
+    "python-dotenv",
     "Pillow",
     "typing-extensions",
     "openai",

src/dailyai/services/azure_ai_services.py

@@ -17,13 +17,10 @@ from azure.cognitiveservices.speech import SpeechSynthesizer, SpeechConfig, Resu
 
 
 class AzureTTSService(TTSService):
-    def __init__(self, speech_key=None, speech_region=None):
+    def __init__(self, *, api_key, region):
         super().__init__()
 
-        speech_key = speech_key or os.getenv("AZURE_SPEECH_SERVICE_KEY")
+        self.speech_config = SpeechConfig(subscription=api_key, region=region)
-        speech_region = speech_region or os.getenv("AZURE_SPEECH_SERVICE_REGION")
-
-        self.speech_config = SpeechConfig(subscription=speech_key, region=speech_region)
         self.speech_synthesizer = SpeechSynthesizer(
             speech_config=self.speech_config, audio_config=None)
 
@@ -51,25 +48,13 @@ class AzureTTSService(TTSService):
 
 
 class AzureLLMService(LLMService):
-    def __init__(self, api_key=None, azure_endpoint=None, api_version=None, model=None):
+    def __init__(self, *, api_key, endpoint, api_version="2023-12-01-preview", model):
         super().__init__()
-        api_key = api_key or os.getenv("AZURE_CHATGPT_KEY")
+        self._model: str = model
 
-        azure_endpoint = azure_endpoint or os.getenv("AZURE_CHATGPT_ENDPOINT")
+        self._client = AsyncAzureOpenAI(
-        if not azure_endpoint:
-            raise Exception(
-                "No azure endpoint specified for Azure LLM, please set AZURE_CHATGPT_ENDPOINT in the environment or pass it to the AzureLLMService constructor")
-
-        model: str | None = model or os.getenv("AZURE_CHATGPT_DEPLOYMENT_ID")
-        if not model:
-            raise Exception(
-                "No model specified for Azure LLM, please set AZURE_CHATGPT_DEPLOYMENT_ID in the environment or pass it to the AzureLLMService constructor")
-        self.model: str = model
-
-        api_version = api_version or "2023-12-01-preview"
-        self.client = AsyncAzureOpenAI(
             api_key=api_key,
-            azure_endpoint=azure_endpoint,
+            azure_endpoint=endpoint,
             api_version=api_version,
         )
 
@@ -77,7 +62,7 @@ class AzureLLMService(LLMService):
         messages_for_log = json.dumps(messages)
         self.logger.debug(f"Generating chat via azure: {messages_for_log}")
 
-        chunks = await self.client.chat.completions.create(model=self.model, stream=True, messages=messages)
+        chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages)
         async for chunk in chunks:
             if len(chunk.choices) == 0:
                 continue
@@ -89,7 +74,7 @@ class AzureLLMService(LLMService):
         messages_for_log = json.dumps(messages)
         self.logger.debug(f"Generating chat via azure: {messages_for_log}")
 
-        response = await self.client.chat.completions.create(model=self.model, stream=False, messages=messages)
+        response = await self._client.chat.completions.create(model=self._model, stream=False, messages=messages)
         if response and len(response.choices) > 0:
             return response.choices[0].message.content
         else:
@@ -100,17 +85,19 @@ class AzureImageGenServiceREST(ImageGenService):
 
     def __init__(
             self,
+            *,
+            api_version="2023-06-01-preview",
             image_size: str,
             aiohttp_session: aiohttp.ClientSession,
-            api_key=None,
+            api_key,
-            azure_endpoint=None,
+            endpoint,
-            api_version=None,
+            model):
-            model=None):
         super().__init__(image_size=image_size)
-        self._api_key = api_key or os.getenv("AZURE_DALLE_KEY")
+
-        self._azure_endpoint = azure_endpoint or os.getenv("AZURE_DALLE_ENDPOINT")
+        self._api_key = api_key
-        self._api_version = api_version or "2023-06-01-preview"
+        self._azure_endpoint = endpoint
-        self._model = model or os.getenv("AZURE_DALLE_DEPLOYMENT_ID")
+        self._api_version = api_version
+        self._model = model
         self._aiohttp_session = aiohttp_session
 
     async def run_image_gen(self, sentence) -> tuple[str, bytes]:
@@ -125,8 +112,11 @@ class AzureImageGenServiceREST(ImageGenService):
         async with self._aiohttp_session.post(
             url, headers=headers, json=body
         ) as submission:
+            print(f"submission: {submission}")
+            # We never get past this line, because this header isn't
+            # defined on a 429 response, but something is eating our exceptions!
             operation_location = submission.headers['operation-location']
-
+            print(f"submission status: {submission.status}")
             status = ""
             attempts_left = 120
             json_response = None
@@ -145,9 +135,9 @@ class AzureImageGenServiceREST(ImageGenService):
             image_url = json_response["result"]["data"][0]["url"] if json_response else None
             if not image_url:
                 raise Exception("Image generation failed")
-
             # Load the image from the url
             async with self._aiohttp_session.get(image_url) as response:
                 image_stream = io.BytesIO(await response.content.read())
                 image = Image.open(image_stream)
+                print("i got an image file!")
                 return (image_url, image.tobytes())

src/dailyai/services/deepgram_ai_services.py

@@ -9,11 +9,11 @@ from dailyai.services.ai_services import TTSService
 
 
 class DeepgramTTSService(TTSService):
-    def __init__(self, aiohttp_session, speech_key=None, voice=None):
+    def __init__(self, *, aiohttp_session, api_key, voice="alpha-asteria-en-v2"):
         super().__init__()
 
-        self._voice = voice or os.getenv("DEEPGRAM_VOICE") or "alpha-asteria-en-v2"
+        self._voice = voice
-        self._speech_key = speech_key or os.getenv("DEEPGRAM_API_KEY")
+        self._api_key = api_key
         self._aiohttp_session = aiohttp_session
 
     def get_mic_sample_rate(self):
@@ -23,7 +23,7 @@ class DeepgramTTSService(TTSService):
         self.logger.info(f"Running deepgram tts for {sentence}")
         base_url = "https://api.beta.deepgram.com/v1/speak"
         request_url = f"{base_url}?model={self._voice}&encoding=linear16&container=none&sample_rate=16000"
-        headers = {"authorization": f"token {self._speech_key}"}
+        headers = {"authorization": f"token {self._api_key}"}
         body = {"text": sentence}
         async with self._aiohttp_session.post(request_url, headers=headers, json=body) as r:
             async for data in r.content:

src/dailyai/services/elevenlabs_ai_service.py

@@ -12,14 +12,15 @@ class ElevenLabsTTSService(TTSService):
 
     def __init__(
         self,
+        *,
         aiohttp_session: aiohttp.ClientSession,
-        api_key=None,
+        api_key,
-        voice_id=None,
+        voice_id,
     ):
         super().__init__()
 
-        self._api_key = api_key or os.getenv("ELEVENLABS_API_KEY")
+        self._api_key = api_key
-        self._voice_id = voice_id or os.getenv("ELEVENLABS_VOICE_ID")
+        self._voice_id = voice_id
         self._aiohttp_session = aiohttp_session
 
     async def run_tts(self, sentence) -> AsyncGenerator[bytes, None]:

src/dailyai/services/fal_ai_services.py

@@ -2,18 +2,22 @@ import fal
 import aiohttp
 import asyncio
 import io
+import os
 import json
 from PIL import Image
 
 
 from dailyai.services.ai_services import LLMService, TTSService, ImageGenService
-# Fal expects FAL_KEY_ID and FAL_KEY_SECRET to be set in the env
 
 
 class FalImageGenService(ImageGenService):
-    def __init__(self, image_size, aiohttp_session: aiohttp.ClientSession):
+    def __init__(self, *, image_size, aiohttp_session: aiohttp.ClientSession, key_id=None, key_secret=None):
         super().__init__(image_size)
         self._aiohttp_session = aiohttp_session
+        if key_id:
+            os.environ["FAL_KEY_ID"] = key_id
+        if key_secret:
+            os.environ["FAL_KEY_SECRET"] = key_secret
 
     async def run_image_gen(self, sentence) -> tuple[str, bytes]:
         def get_image_url(sentence, size):

src/dailyai/services/open_ai_services.py

@@ -13,26 +13,24 @@ from dailyai.services.ai_services import AIService, TTSService, LLMService, Imag
 
 
 class OpenAILLMService(LLMService):
-    def __init__(self, api_key=None, model=None):
+    def __init__(self, *, api_key, model="gpt-4"):
         super().__init__()
-        api_key = api_key or os.getenv("OPEN_AI_KEY")
+        self._model = model
-        self.model = model or os.getenv("OPEN_AI_LLM_MODEL") or "gpt-4"
+        self._client = AsyncOpenAI(api_key=api_key)
-        self.client = AsyncOpenAI(api_key=api_key)
 
     async def get_response(self, messages, stream):
-        return await self.client.chat.completions.create(
+        return await self._client.chat.completions.create(
             stream=stream,
             messages=messages,
-            model=self.model
+            model=self._model
         )
 
     async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
         messages_for_log = json.dumps(messages)
         self.logger.debug(f"Generating chat via openai: {messages_for_log}")
 
-        response = await self.get_response(messages, stream=True)
+        chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages)
-
+        async for chunk in chunks:
-        for chunk in response:
             if len(chunk.choices) == 0:
                 continue
 
@@ -43,7 +41,7 @@ class OpenAILLMService(LLMService):
         messages_for_log = json.dumps(messages)
         self.logger.debug(f"Generating chat via openai: {messages_for_log}")
 
-        response = await self.get_response(messages, stream=False)
+        response = await self._client.chat.completions.create(model=self._model, stream=False, messages=messages)
         if response and len(response.choices) > 0:
             return response.choices[0].message.content
         else:
@@ -54,14 +52,15 @@ class OpenAIImageGenService(ImageGenService):
 
     def __init__(
         self,
+        *,
         image_size: str,
         aiohttp_session: aiohttp.ClientSession,
-        api_key=None,
+        api_key,
-        model=None,
+        model="dall-e-3",
     ):
         super().__init__(image_size=image_size)
-        api_key = api_key or os.getenv("OPEN_AI_KEY")
+        self._model = model
-        self._model = model or os.getenv("OPEN_AI_IMAGE_MODEL") or "dall-e-3"
+        print(f"api key: {api_key}")
         self._client = AsyncOpenAI(api_key=api_key)
         self._aiohttp_session = aiohttp_session
 

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

@@ -1,11 +1,11 @@
-import argparse
 import asyncio
-
 import aiohttp
+import os
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
+from samples.foundational.support.runner import configure
 
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
@@ -17,7 +17,7 @@ async def main(room_url):
         #
         # the abstract transport service APIs presumably can map pretty closely
         # to the daily-python basic API
-        meeting_duration_minutes = 1
+        meeting_duration_minutes = 5
         transport = DailyTransportService(
             room_url,
             None,
@@ -25,7 +25,7 @@ async def main(room_url):
             meeting_duration_minutes,
         )
         transport.mic_enabled = True
-        tts = ElevenLabsTTSService(session, voice_id="ErXwobaYiN019PkySvjV")
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
 
         # Register an event handler so we can play the audio when the participant joins.
         @transport.event_handler("on_participant_joined")
@@ -45,11 +45,5 @@ async def main(room_url):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    asyncio.run(main(args.url))

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

@@ -1,13 +1,16 @@
 import argparse
 import asyncio
+import os
 
 import aiohttp
 
 from dailyai.queue_frame import LLMMessagesQueueFrame
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.azure_ai_services import AzureLLMService
+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 samples.foundational.support.runner import configure
 
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
@@ -20,9 +23,12 @@ async def main(room_url):
         )
         transport.mic_enabled = True
 
-        tts = ElevenLabsTTSService(session, voice_id="29vD33N1CtxCmqQRPOHJ")
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
-        llm = AzureLLMService()
+        # 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 = 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("OPENAI_CHATGPT_API_KEY"))
         messages = [{
             "role": "system",
             "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world."
@@ -43,10 +49,5 @@ async def main(room_url):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-    asyncio.run(main(args.url))

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

@@ -1,11 +1,15 @@
 import argparse
 import asyncio
-
 import aiohttp
+import os
 
 from dailyai.queue_frame import TextQueueFrame
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.services.open_ai_services import OpenAIImageGenService
+from dailyai.services.azure_ai_services import AzureImageGenServiceREST
+
+from samples.foundational.support.runner import configure
 
 local_joined = False
 participant_joined = False
@@ -25,7 +29,10 @@ async def main(room_url):
         transport.camera_width = 1024
         transport.camera_height = 1024
 
-        imagegen = FalImageGenService(image_size="1024x1024", aiohttp_session=session)
+        imagegen = FalImageGenService(image_size="1024x1024", aiohttp_session=session, key_id=os.getenv("FAL_KEY_ID"), 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, [
@@ -39,11 +46,5 @@ async def main(room_url):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    asyncio.run(main(args.url))

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

@@ -1,5 +1,6 @@
 import argparse
 import asyncio
+import os
 import re
 
 import aiohttp
@@ -9,6 +10,7 @@ from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.queue_frame import EndStreamQueueFrame, LLMMessagesQueueFrame
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
+from samples.foundational.support.runner import configure
 
 async def main(room_url: str):
     async with aiohttp.ClientSession() as session:
@@ -22,9 +24,9 @@ async def main(room_url: str):
         transport.mic_sample_rate = 16000
         transport.camera_enabled = False
 
-        llm = AzureLLMService()
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-        azure_tts = AzureTTSService()
+        azure_tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
-        elevenlabs_tts = ElevenLabsTTSService(session, voice_id="ErXwobaYiN019PkySvjV")
+        elevenlabs_tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
 
         messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
 
@@ -60,11 +62,5 @@ async def main(room_url: str):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    asyncio.run(main(args.url))

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

@@ -1,14 +1,16 @@
 import argparse
 import asyncio
-
 import aiohttp
+import os
 
 from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
-from dailyai.services.azure_ai_services import AzureLLMService
+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
+from dailyai.services.open_ai_services import OpenAIImageGenService
 
+from samples.foundational.support.runner import configure
 
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
@@ -25,10 +27,13 @@ async def main(room_url):
         transport.camera_width = 1024
         transport.camera_height = 1024
 
-        llm = AzureLLMService()
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-        dalle = FalImageGenService(aiohttp_session=session, image_size="1024x1024")
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id="ErXwobaYiN019PkySvjV")
-        tts = ElevenLabsTTSService(aiohttp_session=session, voice_id="ErXwobaYiN019PkySvjV")
+        # tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
-        # dalle = OpenAIImageGenService(image_size="1024x1024")
+
+        dalle = FalImageGenService(image_size="1024x1024", aiohttp_session=session, key_id=os.getenv("FAL_KEY_ID"), key_secret=os.getenv("FAL_KEY_SECRET"))
+        # dalle = OpenAIImageGenService(aiohttp_session=session, api_key=os.getenv("OPENAI_DALLE_API_KEY"), image_size="1024x1024")
+        # dalle = 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"))
         
         # Get a complete audio chunk from the given text. Splitting this into its own
         # coroutine lets us ensure proper ordering of the audio chunks on the send queue.
@@ -54,10 +59,11 @@ 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))
+            print(f"about to gather tasks for {month}")
             (audio, image_data) = await asyncio.gather(
                 audio_task, image_task
             )
-
+            print(f"about to return from get_month_data for {month}")
             return {
                 "month": month,
                 "text": image_description,
@@ -72,22 +78,32 @@ async def main(room_url):
             "March",
             "April",
             "May",
-            "June",
+            "June"
-            "July",
-            "August",
-            "September",
-            "October",
-            "November",
-            "December",
         ]
-
+        """
+        "February",
+        "March",
+        "April",
+        "May",
+        "June",
+        "July",
+        "August",
+        "September",
+        "October",
+        "November",
+        "December",
+        """
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
             # This will play the months in the order they're completed. The benefit
             # is we'll have as little delay as possible before the first month, and
             # likely no delay between months, but the months won't display in order.
             for month_data_task in asyncio.as_completed(month_tasks):
-                data = await month_data_task
+                print(f"month_data_task: {month_data_task}")
+                try:
+                    data = await month_data_task
+                except Exception:
+                    print("OMG EXCEPTION!!!!")
                 if data:
                     await transport.send_queue.put(
                         [
@@ -104,11 +120,5 @@ async def main(room_url):
         await transport.run()
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    asyncio.run(main(args.url))

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

@@ -1,13 +1,10 @@
-import argparse
 import asyncio
-import requests
+import os
-import time
-import urllib.parse
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.queue_aggregators import LLMAssistantContextAggregator, LLMContextAggregator, LLMUserContextAggregator
-
+from samples.foundational.support.runner import configure
 
 async def main(room_url: str, token):
     global transport
@@ -24,8 +21,8 @@ async def main(room_url: str, token):
     transport.mic_sample_rate = 16000
     transport.camera_enabled = False
 
-    llm = AzureLLMService()
+    llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-    tts = AzureTTSService()
+    tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
 
     @transport.event_handler("on_first_other_participant_joined")
     async def on_first_other_participant_joined(transport):
@@ -54,35 +51,5 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url, token))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=True,
-        help="Daily API Key (needed to create token)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    # Create a meeting token for the given room with an expiration 1 hour in the future.
-    room_name: str = urllib.parse.urlparse(args.url).path[1:]
-    expiration: float = time.time() + 60 * 60
-
-    res: requests.Response = requests.post(
-        f"https://api.daily.co/v1/meeting-tokens",
-        headers={"Authorization": f"Bearer {args.apikey}"},
-        json={
-            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
-        },
-    )
-
-    if res.status_code != 200:
-        raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
-
-    asyncio.run(main(args.url, token))

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

@@ -16,6 +16,7 @@ from dailyai.services.ai_services import AIService
 from dailyai.queue_aggregators import LLMAssistantContextAggregator, LLMUserContextAggregator
 from dailyai.services.fal_ai_services import FalImageGenService
 
+from samples.foundational.support.runner import configure
 
 class ImageSyncAggregator(AIService):
     def __init__(self, speaking_path: str, waiting_path: str):
@@ -32,7 +33,7 @@ class ImageSyncAggregator(AIService):
 
 
 async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as aiohttp_session:
+    async with aiohttp.ClientSession() as session:
         transport = DailyTransportService(
             room_url,
             token,
@@ -45,9 +46,9 @@ async def main(room_url: str, token):
         transport.mic_enabled = True
         transport.mic_sample_rate = 16000
 
-        llm = AzureLLMService()
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-        tts = AzureTTSService()
+        tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
-        img = FalImageGenService(image_size="1024x1024", aiohttp_session=aiohttp_session)
+        img = FalImageGenService(image_size="1024x1024", aiohttp_session=session, key_id=os.getenv("FAL_KEY_ID"), key_secret=os.getenv("FAL_KEY_SECRET"))
 
         async def get_images():
             get_speaking_task = asyncio.create_task(
@@ -100,35 +101,5 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url, token))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=True,
-        help="Daily API Key (needed to create token)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    # Create a meeting token for the given room with an expiration 1 hour in the future.
-    room_name: str = urllib.parse.urlparse(args.url).path[1:]
-    expiration: float = time.time() + 60 * 60
-
-    res: requests.Response = requests.post(
-        f"https://api.daily.co/v1/meeting-tokens",
-        headers={"Authorization": f"Bearer {args.apikey}"},
-        json={
-            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
-        },
-    )
-
-    if res.status_code != 200:
-        raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
-
-    asyncio.run(main(args.url, token))

src/samples/foundational/07-interruptible.py

@@ -1,16 +1,14 @@
-import argparse
 import asyncio
 import aiohttp
-import requests
+import os
-import time
-import urllib.parse
 from dailyai.conversation_wrappers import InterruptibleConversationWrapper
 
 from dailyai.queue_frame import StartStreamQueueFrame, TextQueueFrame
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.azure_ai_services import AzureLLMService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
+from samples.foundational.support.runner import configure
 
 async def main(room_url: str, token):
     async with aiohttp.ClientSession() as session:
@@ -25,8 +23,8 @@ async def main(room_url: str, token):
         transport.camera_enabled = False
         transport.start_transcription = True
 
-        llm = AzureLLMService()
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-        tts = ElevenLabsTTSService(session, voice_id="ErXwobaYiN019PkySvjV")
+        tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
 
         async def run_response(user_speech, tma_in, tma_out):
             await tts.run_to_queue(
@@ -63,35 +61,5 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url, token))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=True,
-        help="Daily API Key (needed to create token)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    # Create a meeting token for the given room with an expiration 1 hour in the future.
-    room_name: str = urllib.parse.urlparse(args.url).path[1:]
-    expiration: float = time.time() + 60 * 60
-
-    res: requests.Response = requests.post(
-        f"https://api.daily.co/v1/meeting-tokens",
-        headers={"Authorization": f"Bearer {args.apikey}"},
-        json={
-            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
-        },
-    )
-
-    if res.status_code != 200:
-        raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
-
-    asyncio.run(main(args.url, token))

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

@@ -1,10 +1,14 @@
 import aiohttp
-import argparse
 import asyncio
+import os
 
 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.queue_frame import AudioQueueFrame, ImageQueueFrame
+
+from samples.foundational.support.runner import configure
 
 async def main(room_url:str):
     async with aiohttp.ClientSession() as session:
@@ -16,52 +20,85 @@ async def main(room_url:str):
             room_url,
             None,
             "Respond bot",
-            5,
+            600,
         )
         transport.mic_enabled = True
         transport.mic_sample_rate = 16000
-        transport.camera_enabled = False
+        transport.camera_enabled = True
+        transport.camera_width = 1024
+        transport.camera_height = 1024
 
-        llm = AzureLLMService()
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-        tts1 = AzureTTSService()
+        tts1 = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
-        tts2 = ElevenLabsTTSService(session)
+        tts2 = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), 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"))
+
+        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."},
+        ]
+        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}")
+            if 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}")
+            if 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):
+                all_audio.extend(audio)
+
+            return all_audio
 
         async def argue():
-            bot1_messages = [
+            for i in range(100):
-                {"role": "system", "content": "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. Your responses should only be a few sentences long."},
-            ]
-            bot2_messages = [
-                {"role": "system", "content": "You strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences."},
-            ]
-
-            for i in range(1, 5):
                 print(f"In iteration {i}")
-                # Run the LLMs synchronously for the back-and-forth
-                bot1_msg = await llm.run_llm(bot1_messages)
-                print(f"bot1_msg: {bot1_msg}")
-                if 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)
+                bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
 
-                bot2_msg = await llm.run_llm(bot2_messages)
+                (audio1, image_data1) = await asyncio.gather(
-                print(f"bot2_msg: {bot2_msg}")
+                    get_bot1_statement(), dalle.run_image_gen(bot1_description)
-                if bot2_msg:
+                )
-                    bot2_messages.append({"role": "assistant", "content": bot2_msg})
+                await transport.send_queue.put(
-                    bot1_messages.append({"role": "user", "content": bot2_msg})
+                    [
+                        ImageQueueFrame(None, image_data1[1]),
+                        AudioQueueFrame(audio1),
+                    ]
+                )
 
-                await tts2.say(bot2_msg, transport.send_queue)
+                bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
+
+                (audio2, image_data2) = await asyncio.gather(
+                    get_bot2_statement(), dalle.run_image_gen(bot2_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageQueueFrame(None, image_data2[1]),
+                        AudioQueueFrame(audio2),
+                    ]
+                )
 
         await asyncio.gather(transport.run(), argue())
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    asyncio.run(main(args.url))

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

@@ -1,108 +0,0 @@
-import aiohttp
-import argparse
-import asyncio
-
-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.queue_frame import AudioQueueFrame, ImageQueueFrame
-
-async def main(room_url:str):
-    async with aiohttp.ClientSession() as session:
-        global transport
-        global llm
-        global tts
-
-        transport = DailyTransportService(
-            room_url,
-            None,
-            "Respond bot",
-            600,
-        )
-        transport.mic_enabled = True
-        transport.mic_sample_rate = 16000
-        transport.camera_enabled = True
-        transport.camera_width = 1024
-        transport.camera_height = 1024
-
-        llm = AzureLLMService()
-        tts1 = AzureTTSService()
-        tts2 = ElevenLabsTTSService(session)
-        dalle = FalImageGenService(image_size="1024x1024", aiohttp_session=session)
-
-        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."},
-        ]
-        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}")
-            if 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}")
-            if 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):
-                all_audio.extend(audio)
-
-            return all_audio
-
-        async def argue():
-            for i in range(100):
-                print(f"In iteration {i}")
-
-                bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
-
-                (audio1, image_data1) = await asyncio.gather(
-                    get_bot1_statement(), dalle.run_image_gen(bot1_description)
-                )
-                await transport.send_queue.put(
-                    [
-                        ImageQueueFrame(None, image_data1[1]),
-                        AudioQueueFrame(audio1),
-                    ]
-                )
-
-                bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
-
-                (audio2, image_data2) = await asyncio.gather(
-                    get_bot2_statement(), dalle.run_image_gen(bot2_description)
-                )
-                await transport.send_queue.put(
-                    [
-                        ImageQueueFrame(None, image_data2[1]),
-                        AudioQueueFrame(audio2),
-                    ]
-                )
-
-        await asyncio.gather(transport.run(), argue())
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
-    parser.add_argument(
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    asyncio.run(main(args.url))

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

@@ -1,119 +0,0 @@
-import aiohttp
-import argparse
-import asyncio
-
-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.queue_frame import AudioQueueFrame, ImageQueueFrame
-
-async def main(room_url:str):
-    async with aiohttp.ClientSession() as session:
-        global transport
-        global llm
-        global tts
-
-        transport = DailyTransportService(
-            room_url,
-            None,
-            "Respond bot",
-            600,
-        )
-        transport.mic_enabled = True
-        transport.mic_sample_rate = 16000
-        transport.camera_enabled = True
-        transport.camera_width = 1024
-        transport.camera_height = 1024
-
-        llm = AzureLLMService()
-        tts1 = AzureTTSService()
-        tts2 = ElevenLabsTTSService(session)
-        dalle = FalImageGenService(image_size="1024x1024", aiohttp_session=session)
-        # dalle = OpenAIImageGenService(image_size="1024x1024")
-
-        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"
-
-
-
-        bot1_messages = [
-            {"role": "system", "content": f"You are {affirmative}. You're in a debate, and the topic is: '{topic}'. You're arguing the affirmative. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever agree with the user. Your responses should only be a few sentences long."},
-        ]
-        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}")
-            if 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}")
-            if 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):
-                all_audio.extend(audio)
-
-            return all_audio
-
-        async def argue():
-            for i in range(100):
-                print(f"In iteration {i}")
-
-                bot1_description = f"{affirmative}, cartoon, highly detailed"
-
-                (audio1, image_data1) = await asyncio.gather(
-                    get_bot1_statement(), dalle.run_image_gen(bot1_description)
-                )
-                await transport.send_queue.put(
-                    [
-                        ImageQueueFrame(None, image_data1[1]),
-                        AudioQueueFrame(audio1),
-                    ]
-                )
-
-                bot2_description = f"{negative}, cartoon, bright colors, funny, highly detailed"
-
-                (audio2, image_data2) = await asyncio.gather(
-                    get_bot2_statement(), dalle.run_image_gen(bot2_description)
-                )
-                await transport.send_queue.put(
-                    [
-                        ImageQueueFrame(None, image_data2[1]),
-                        AudioQueueFrame(audio2),
-                    ]
-                )
-
-        await asyncio.gather(transport.run(), argue())
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
-    parser.add_argument(
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    asyncio.run(main(args.url))

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

@@ -1,18 +1,15 @@
 import aiohttp
-import argparse
 import asyncio
 import os
 import random
-import requests
+from typing import AsyncGenerator
-import time
-import urllib.parse
 
 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.queue_aggregators import LLMContextAggregator
+from dailyai.queue_aggregators import LLMUserContextAggregator, LLMAssistantContextAggregator
 from dailyai.queue_frame import (
     QueueFrame,
     TextQueueFrame,
@@ -22,7 +19,8 @@ from dailyai.queue_frame import (
 )
 from dailyai.services.ai_services import AIService
 
-from typing import AsyncGenerator
+from samples.foundational.support.runner import configure
+
 
 sprites = {}
 image_files = [
@@ -123,8 +121,8 @@ async def main(room_url: str, token):
         transport.camera_width = 720
         transport.camera_height = 1280
 
-        llm = AzureLLMService()
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-        tts = ElevenLabsTTSService(session)
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id="jBpfuIE2acCO8z3wKNLl")
         isa = ImageSyncAggregator()
 
         @transport.event_handler("on_first_other_participant_joined")
@@ -136,11 +134,11 @@ async def main(room_url: str, token):
                 {"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 = LLMContextAggregator(
+            tma_in = LLMUserContextAggregator(
-                messages, "user", transport.my_participant_id
+                messages, transport.my_participant_id
             )
-            tma_out = LLMContextAggregator(
+            tma_out = LLMAssistantContextAggregator(
-                messages, "assistant", transport.my_participant_id
+                messages, transport.my_participant_id
             )
             tf = TranscriptFilter(transport.my_participant_id)
             ncf = NameCheckFilter(["Santa Cat", "Santa"])
@@ -169,35 +167,5 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url, token))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=True,
-        help="Daily API Key (needed to create token)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    # Create a meeting token for the given room with an expiration 24 hours in the future.
-    room_name: str = urllib.parse.urlparse(args.url).path[1:]
-    expiration: float = time.time() + 60 * 60 * 24
-
-    res: requests.Response = requests.post(
-        f"https://api.daily.co/v1/meeting-tokens",
-        headers={"Authorization": f"Bearer {args.apikey}"},
-        json={
-            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
-        },
-    )
-
-    if res.status_code != 200:
-        raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
-
-    asyncio.run(main(args.url, token))

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

@@ -1,19 +1,19 @@
-import argparse
+import aiohttp
 import asyncio
 import logging
 import os
 import wave
-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_aggregators import LLMContextAggregator, LLMUserContextAggregator, LLMAssistantContextAggregator
 from dailyai.services.ai_services import AIService, FrameLogger
 from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
 from typing import AsyncGenerator
 
+from samples.foundational.support.runner import configure
+
 logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s") # or whatever
 logger = logging.getLogger("dailyai")
 logger.setLevel(logging.DEBUG)
@@ -64,52 +64,56 @@ class InboundSoundEffectWrapper(AIService):
 
 
 async def main(room_url: str, token):
-    global transport
+    async with aiohttp.ClientSession() as session:
-    global llm
-    global tts
 
-    transport = DailyTransportService(
+        global transport
-        room_url,
+        global llm
-        token,
+        global tts
-        "Respond bot",
-        5,
-    )
-    transport.mic_enabled = True
-    transport.mic_sample_rate = 16000
-    transport.camera_enabled = False
 
-    llm = AzureLLMService()
+        transport = DailyTransportService(
-    tts = AzureTTSService()
+            room_url,
-
+            token,
-    @transport.event_handler("on_first_other_participant_joined")
+            "Respond bot",
-    async def on_first_other_participant_joined(transport):
+            5,
-        await tts.say("Hi, I'm listening!", transport.send_queue)
-        await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
-    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(
+        transport.mic_enabled = True
-            messages, transport.my_participant_id
+        transport.mic_sample_rate = 16000
-        )
+        transport.camera_enabled = False
-        out_sound = OutboundSoundEffectWrapper()
+
-        in_sound = InboundSoundEffectWrapper()
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
-        fl = FrameLogger("LLM Out")
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id="ErXwobaYiN019PkySvjV")
-        fl2 = FrameLogger("Transcription In")
+
-        await out_sound.run_to_queue(
+
-            transport.send_queue,
+        @transport.event_handler("on_first_other_participant_joined")
-            tts.run(
+        async def on_first_other_participant_joined(transport):
-                fl.run(
+            await tts.say("Hi, I'm listening!", transport.send_queue)
-                    tma_out.run(
+            await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
-                        llm.run(
+        async def handle_transcriptions():
-                            fl2.run(
+            messages = [
-                                in_sound.run(
+                {"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.run(
+            ]
-                                        transport.get_receive_frames()
+
+            tma_in = LLMUserContextAggregator(
+                messages, transport.my_participant_id
+            )
+            tma_out = LLMAssistantContextAggregator(
+                messages, transport.my_participant_id
+            )
+            out_sound = OutboundSoundEffectWrapper()
+            in_sound = InboundSoundEffectWrapper()
+            fl = FrameLogger("LLM Out")
+            fl2 = FrameLogger("Transcription In")
+            await out_sound.run_to_queue(
+                transport.send_queue,
+                tts.run(
+                    fl.run(
+                        tma_out.run(
+                            llm.run(
+                                fl2.run(
+                                    in_sound.run(
+                                        tma_in.run(
+                                            transport.get_receive_frames()
+                                        )
                                     )
                                 )
                             )
@@ -117,43 +121,12 @@ async def main(room_url: str, token):
                     )
                 )
             )
-        )
             
 
-    transport.transcription_settings["extra"]["punctuate"] = True
+        transport.transcription_settings["extra"]["punctuate"] = True
-    await asyncio.gather(transport.run(), handle_transcriptions())
+        await asyncio.gather(transport.run(), handle_transcriptions())
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url, token))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=True,
-        help="Daily API Key (needed to create token)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    # Create a meeting token for the given room with an expiration 1 hour in the future.
-    room_name: str = urllib.parse.urlparse(args.url).path[1:]
-    expiration: float = time.time() + 60 * 60
-
-    res: requests.Response = requests.post(
-        f"https://api.daily.co/v1/meeting-tokens",
-        headers={"Authorization": f"Bearer {args.apikey}"},
-        json={
-            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
-        },
-    )
-
-    if res.status_code != 200:
-        raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
-
-    asyncio.run(main(args.url, token))

src/samples/foundational/11a-dial-out.py

@@ -1,165 +0,0 @@
-import argparse
-import asyncio
-import os
-import wave
-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.queue_aggregators import LLMContextAggregator
-from dailyai.services.ai_services import AIService, FrameLogger
-from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
-from typing import AsyncGenerator
-
-sounds = {}
-sound_files = [
-    'ding1.wav',
-    'ding2.wav'
-]
-
-script_dir = os.path.dirname(__file__)
-
-for file in sound_files:
-    # Build the full path to the image file
-    full_path = os.path.join(script_dir, "assets", file)
-    # Get the filename without the extension to use as the dictionary key
-    filename = os.path.splitext(os.path.basename(full_path))[0]
-    # Open the image and convert it to bytes
-    with wave.open(full_path) as audio_file:
-        sounds[file] = audio_file.readframes(-1)
-
-
-
-
-class OutboundSoundEffectWrapper(AIService):
-    def __init__(self):
-        pass
-
-    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
-        if isinstance(frame, LLMResponseEndQueueFrame):
-            yield AudioQueueFrame(sounds["ding1.wav"])
-            # In case anything else up the stack needs it
-            yield frame
-        else:
-            yield frame
-
-class InboundSoundEffectWrapper(AIService):
-    def __init__(self):
-        pass
-
-    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
-        if isinstance(frame, LLMMessagesQueueFrame):
-            yield AudioQueueFrame(sounds["ding2.wav"])
-            # In case anything else up the stack needs it
-            yield frame
-        else:
-            yield frame
-
-
-async def main(room_url: str, token, phone):
-    global transport
-    global llm
-    global tts
-
-    transport = DailyTransportService(
-        room_url,
-        token,
-        "Respond bot",
-        300,
-    )
-    transport.mic_enabled = True
-    transport.mic_sample_rate = 16000
-    transport.camera_enabled = False
-
-    llm = AzureLLMService()
-    tts = AzureTTSService()
-
-    @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)
-        await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
-    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 = LLMContextAggregator(
-            messages, "user", transport.my_participant_id
-        )
-        tma_out = LLMContextAggregator(
-            messages, "assistant", transport.my_participant_id
-        )
-        out_sound = OutboundSoundEffectWrapper()
-        in_sound = InboundSoundEffectWrapper()
-        fl = FrameLogger("LLM Out")
-        fl2 = FrameLogger("Transcription In")
-        await out_sound.run_to_queue(
-            transport.send_queue,
-            tts.run(
-                tma_out.run(
-                    llm.run(
-                        fl2.run(
-                            in_sound.run(
-                                tma_in.run(
-                                    transport.get_receive_frames()
-                                )
-                            )
-                        )
-                    )
-                )   
-            )
-        )
-
-    @transport.event_handler("on_participant_joined")
-    async def pax_joined(transport, pax):
-        print(f"PARTICIPANT JOINED: {pax}")
-        
-    @transport.event_handler("on_call_state_updated")
-    async def on_call_state_updated(transport, state):
-        if (state == "joined"):
-            if (phone):
-                transport.start_recording()
-                transport.dialout(phone)
-
-
-    transport.transcription_settings["extra"]["punctuate"] = True
-
-    await asyncio.gather(transport.run(), handle_transcriptions())
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
-    parser.add_argument(
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=True,
-        help="Daily API Key (needed to create token)",
-    )
-
-    parser.add_argument("-p", "--phone", type=str, required=False, help="A phone number to call when the bot joins the room")
-
-    args, unknown = parser.parse_known_args()
-
-    # Create a meeting token for the given room with an expiration 1 hour in the future.
-    room_name: str = urllib.parse.urlparse(args.url).path[1:]
-    expiration: float = time.time() + 60 * 60
-
-    res: requests.Response = requests.post(
-        f"https://api.staging.daily.co/v1/meeting-tokens",
-        headers={"Authorization": f"Bearer {args.apikey}"},
-        json={
-            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
-        },
-    )
-
-    if res.status_code != 200:
-        raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
-    asyncio.run(main(args.url, token, args.phone))

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

@@ -1,9 +1,9 @@
-import argparse
 import asyncio
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.whisper_ai_services import WhisperSTTService
 
+from samples.foundational.support.runner import configure
 
 async def main(room_url: str):
     global transport
@@ -35,10 +35,5 @@ async def main(room_url: str):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    (url, token) = configure()
-    parser.add_argument(
+    asyncio.run(main(url))
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-
-    args, unknown = parser.parse_known_args()
-    asyncio.run(main(args.url))

src/samples/foundational/support/runner.py

@@ -0,0 +1,52 @@
+import argparse
+import os
+import time
+import urllib
+import requests
+
+from dotenv import load_dotenv
+load_dotenv()
+
+def configure():
+    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
+    parser.add_argument(
+        "-k",
+        "--apikey",
+        type=str,
+        required=False,
+        help="Daily API Key (needed to create an owner token for the room)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    key = args.apikey or os.getenv("DAILY_API_KEY")
+
+    if not url:
+        raise Exception("No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL.")
+    
+    if not key:
+        raise Exception("No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers.")
+    
+
+    # Create a meeting token for the given room with an expiration 1 hour in the future.
+    room_name: str = urllib.parse.urlparse(url).path[1:]
+    expiration: float = time.time() + 60 * 60
+
+    res: requests.Response = requests.post(
+        f"https://api.daily.co/v1/meeting-tokens",
+        headers={"Authorization": f"Bearer {key}"},
+        json={
+            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
+        },
+    )
+
+    if res.status_code != 200:
+        raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
+
+    token: str = res.json()["token"]
+
+    return (url, token)

src/samples/internal/11a-dial-out.py

@@ -0,0 +1,135 @@
+import aiohttp
+import asyncio
+import os
+import wave
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.queue_aggregators import LLMContextAggregator
+from dailyai.services.ai_services import AIService, FrameLogger
+from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
+from typing import AsyncGenerator
+
+from samples.foundational.support.runner import configure
+
+sounds = {}
+sound_files = [
+    'ding1.wav',
+    'ding2.wav'
+]
+
+script_dir = os.path.dirname(__file__)
+
+for file in sound_files:
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, "assets", file)
+    # Get the filename without the extension to use as the dictionary key
+    filename = os.path.splitext(os.path.basename(full_path))[0]
+    # Open the image and convert it to bytes
+    with wave.open(full_path) as audio_file:
+        sounds[file] = audio_file.readframes(-1)
+
+
+
+
+class OutboundSoundEffectWrapper(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMResponseEndQueueFrame):
+            yield AudioQueueFrame(sounds["ding1.wav"])
+            # In case anything else up the stack needs it
+            yield frame
+        else:
+            yield frame
+
+class InboundSoundEffectWrapper(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMMessagesQueueFrame):
+            yield AudioQueueFrame(sounds["ding2.wav"])
+            # In case anything else up the stack needs it
+            yield frame
+        else:
+            yield frame
+
+
+async def main(room_url: str, token, phone):
+    async with aiohttp.ClientSession() as session:
+
+        global transport
+        global llm
+        global tts
+
+        transport = DailyTransportService(
+            room_url,
+            token,
+            "Respond bot",
+            300,
+        )
+        transport.mic_enabled = True
+        transport.mic_sample_rate = 16000
+        transport.camera_enabled = False
+
+        llm = AzureLLMService()
+        tts = AzureTTSService()
+
+        @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)
+            await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
+        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 = LLMContextAggregator(
+                messages, "user", transport.my_participant_id
+            )
+            tma_out = LLMContextAggregator(
+                messages, "assistant", transport.my_participant_id
+            )
+            out_sound = OutboundSoundEffectWrapper()
+            in_sound = InboundSoundEffectWrapper()
+            fl = FrameLogger("LLM Out")
+            fl2 = FrameLogger("Transcription In")
+            await out_sound.run_to_queue(
+                transport.send_queue,
+                tts.run(
+                    tma_out.run(
+                        llm.run(
+                            fl2.run(
+                                in_sound.run(
+                                    tma_in.run(
+                                        transport.get_receive_frames()
+                                    )
+                                )
+                            )
+                        )
+                    )   
+                )
+            )
+
+        @transport.event_handler("on_participant_joined")
+        async def pax_joined(transport, pax):
+            print(f"PARTICIPANT JOINED: {pax}")
+            
+        @transport.event_handler("on_call_state_updated")
+        async def on_call_state_updated(transport, state):
+            if (state == "joined"):
+                if (phone):
+                    transport.start_recording()
+                    transport.dialout(phone)
+
+
+        transport.transcription_settings["extra"]["punctuate"] = True
+
+        await asyncio.gather(transport.run(), handle_transcriptions())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/samples/server/daily-bot-manager.py

@@ -21,7 +21,7 @@ def start_bot(bot_path, args=None):
     daily_api_key = os.getenv("DAILY_API_KEY")
     api_path = os.getenv("DAILY_API_PATH") or "https://api.daily.co/v1"
 
-    timeout = int(os.getenv("ROOM_TIMEOUT") or os.getenv("BOT_MAX_DURATION") or 300)
+    timeout = int(os.getenv("DAILY_ROOM_TIMEOUT") or os.getenv("DAILY_BOT_MAX_DURATION") or 300)
     exp = time.time() + timeout
     res = requests.post(
         f"{api_path}/rooms",
@@ -82,7 +82,7 @@ def start_bot(bot_path, args=None):
     # Additional client config
     config = {}
     if os.getenv("CLIENT_VAD_TIMEOUT_SEC"):
-        config['vad_timeout_sec'] = float(os.getenv("CLIENT_VAD_TIMEOUT_SEC"))
+        config['vad_timeout_sec'] = float(os.getenv("DAILY_CLIENT_VAD_TIMEOUT_SEC"))
     else:
         config['vad_timeout_sec'] = 1.5