Merge pull request #11 from daily-co/autopep

Autopep linter fixes

src/dailyai/conversation_wrappers.py

@@ -5,6 +5,7 @@ from typing import AsyncGenerator, Awaitable, Callable
 from dailyai.queue_aggregators import LLMContextAggregator
 from dailyai.queue_frame import EndStreamQueueFrame, QueueFrame, TranscriptionQueueFrame
 
+
 class InterruptibleConversationWrapper:
 
     def __init__(
@@ -14,7 +15,7 @@ class InterruptibleConversationWrapper:
             [str, LLMContextAggregator, LLMContextAggregator], Awaitable[None]
         ],
         interrupt: Callable[[], None],
-        my_participant_id: str|None,
+        my_participant_id: str | None,
         llm_messages: list[dict[str, str]],
         llm_context_aggregator_in=LLMContextAggregator,
         llm_context_aggregator_out=LLMContextAggregator,

src/dailyai/queue_aggregators.py

@@ -5,6 +5,7 @@ from dailyai.services.ai_services import AIService
 
 from typing import AsyncGenerator, List
 
+
 class QueueTee:
     async def run_to_queue_and_generate(
         self,
@@ -24,15 +25,21 @@ class QueueTee:
             for queue in output_queues:
                 await queue.put(frame)
 
+
 class LLMContextAggregator(AIService):
-    def __init__(self, messages: list[dict], role:str, bot_participant_id=None, complete_sentences=True):
+    def __init__(
+            self,
+            messages: list[dict],
+            role: str,
+            bot_participant_id=None,
+            complete_sentences=True):
         self.messages = messages
         self.bot_participant_id = bot_participant_id
         self.role = role
         self.sentence = ""
         self.complete_sentences = complete_sentences
 
-    async def process_frame(self, frame:QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         # TODO: split up transcription by participant
         if isinstance(frame, TextQueueFrame):
             if self.complete_sentences:

src/dailyai/queue_frame.py

@@ -2,39 +2,49 @@ from enum import Enum
 from dataclasses import dataclass
 from typing import Any
 
+
 class QueueFrame:
     pass
 
+
 class ControlQueueFrame(QueueFrame):
     pass
 
+
 class StartStreamQueueFrame(ControlQueueFrame):
     pass
 
+
 class EndStreamQueueFrame(ControlQueueFrame):
     pass
 
+
 @dataclass()
 class AudioQueueFrame(QueueFrame):
     data: bytes
 
+
 @dataclass()
 class ImageQueueFrame(QueueFrame):
     url: str | None
     image: bytes
 
+
 @dataclass()
 class TextQueueFrame(QueueFrame):
     text: str
 
+
 @dataclass()
 class TranscriptionQueueFrame(TextQueueFrame):
     participantId: str
     timestamp: str
 
+
 @dataclass()
 class LLMMessagesQueueFrame(QueueFrame):
-    messages: list[dict[str,str]] # TODO: define this more concretely!
+    messages: list[dict[str, str]]  # TODO: define this more concretely!
+
 
 class AppMessageQueueFrame(QueueFrame):
     message: Any

src/dailyai/services/ai_services.py

@@ -65,7 +65,7 @@ class AIService:
             raise e
 
     @abstractmethod
-    async def process_frame(self, frame:QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         if isinstance(frame, ControlQueueFrame):
             yield frame
 
@@ -75,6 +75,7 @@ class AIService:
         if False:
             yield QueueFrame()
 
+
 class LLMService(AIService):
     @abstractmethod
     async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
@@ -146,7 +147,7 @@ class ImageGenService(AIService):
 
     # Renders the image. Returns an Image object.
     @abstractmethod
-    async def run_image_gen(self, sentence:str) -> tuple[str, bytes]:
+    async def run_image_gen(self, sentence: str) -> tuple[str, bytes]:
         pass
 
     async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
@@ -157,15 +158,16 @@ class ImageGenService(AIService):
         (url, image_data) = await self.run_image_gen(frame.text)
         yield ImageQueueFrame(url, image_data)
 
+
 class STTService(AIService):
     """STTService is a base class for speech-to-text services."""
 
     _frame_rate: int
+
     def __init__(self, frame_rate: int = 16000, **kwargs):
         super().__init__(**kwargs)
         self._frame_rate = frame_rate
 
-
     @abstractmethod
     async def run_stt(self, audio: BinaryIO) -> str:
         """Returns transcript as a string"""
@@ -188,6 +190,7 @@ class STTService(AIService):
         text = await self.run_stt(content)
         yield TextQueueFrame(text)
 
+
 @dataclass
 class AIServiceConfig:
     tts: TTSService

src/dailyai/services/azure_ai_services.py

@@ -15,6 +15,7 @@ from PIL import Image
 # See .env.example for Azure configuration needed
 from azure.cognitiveservices.speech import SpeechSynthesizer, SpeechConfig, ResultReason, CancellationReason
 
+
 class AzureTTSService(TTSService):
     def __init__(self, speech_key=None, speech_region=None):
         super().__init__()
@@ -23,18 +24,19 @@ class AzureTTSService(TTSService):
         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)
+        self.speech_synthesizer = SpeechSynthesizer(
+            speech_config=self.speech_config, audio_config=None)
 
     async def run_tts(self, sentence) -> AsyncGenerator[bytes, None]:
         self.logger.info("Running azure tts")
         ssml = "<speak version='1.0' xml:lang='en-US' xmlns='http://www.w3.org/2001/10/synthesis' " \
-           "xmlns:mstts='http://www.w3.org/2001/mstts'>" \
+            "xmlns:mstts='http://www.w3.org/2001/mstts'>" \
-           "<voice name='en-US-SaraNeural'>" \
+            "<voice name='en-US-SaraNeural'>" \
-           "<mstts:silence type='Sentenceboundary' value='20ms' />" \
+            "<mstts:silence type='Sentenceboundary' value='20ms' />" \
-           "<mstts:express-as style='lyrical' styledegree='2' role='SeniorFemale'>" \
+            "<mstts:express-as style='lyrical' styledegree='2' role='SeniorFemale'>" \
-           "<prosody rate='1.05'>" \
+            "<prosody rate='1.05'>" \
-           f"{sentence}" \
+            f"{sentence}" \
-           "</prosody></mstts:express-as></voice></speak> "
+            "</prosody></mstts:express-as></voice></speak> "
         result = await asyncio.to_thread(self.speech_synthesizer.speak_ssml, (ssml))
         self.logger.info("Got azure tts result")
         if result.reason == ResultReason.SynthesizingAudioCompleted:
@@ -47,6 +49,7 @@ class AzureTTSService(TTSService):
             if cancellation_details.reason == CancellationReason.Error:
                 self.logger.info("Error details: {}".format(cancellation_details.error_details))
 
+
 class AzureLLMService(LLMService):
     def __init__(self, api_key=None, azure_endpoint=None, api_version=None, model=None):
         super().__init__()
@@ -54,11 +57,13 @@ class AzureLLMService(LLMService):
 
         azure_endpoint = azure_endpoint or os.getenv("AZURE_CHATGPT_ENDPOINT")
         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")
+            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")
+            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"
@@ -90,9 +95,16 @@ class AzureLLMService(LLMService):
         else:
             return None
 
+
 class AzureImageGenServiceREST(ImageGenService):
 
-    def __init__(self, image_size:str, api_key=None, azure_endpoint=None, api_version=None, model=None):
+    def __init__(
+            self,
+            image_size: str,
+            api_key=None,
+            azure_endpoint=None,
+            api_version=None,
+            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")
@@ -103,7 +115,7 @@ class AzureImageGenServiceREST(ImageGenService):
         # TODO hoist the session to app-level
         async with aiohttp.ClientSession() as session:
             url = f"{self.azure_endpoint}openai/images/generations:submit?api-version={self.api_version}"
-            headers= { "api-key": self.api_key, "Content-Type": "application/json" }
+            headers = {"api-key": self.api_key, "Content-Type": "application/json"}
             body = {
                 # Enter your prompt text here
                 "prompt": sentence,

src/dailyai/services/daily_transport_service.py

@@ -30,6 +30,7 @@ from daily import (
     VirtualSpeakerDevice,
 )
 
+
 class DailyTransportService(EventHandler):
     _daily_initialized = False
     _lock = threading.Lock()
@@ -111,7 +112,8 @@ class DailyTransportService(EventHandler):
                     if self.loop:
                         asyncio.run_coroutine_threadsafe(handler(*args, **kwargs), self.loop)
                     else:
-                        raise Exception("No event loop to run coroutine. In order to use async event handlers, you must run the DailyTransportService in an asyncio event loop.")
+                        raise Exception(
+                            "No event loop to run coroutine. In order to use async event handlers, you must run the DailyTransportService in an asyncio event loop.")
                 else:
                     handler(*args, **kwargs)
         except Exception as e:
@@ -126,8 +128,11 @@ class DailyTransportService(EventHandler):
         if event_name not in [method[0] for method in methods]:
             raise Exception(f"Event handler {event_name} not found")
 
-        if not event_name in self.event_handlers:
+        if event_name not in self.event_handlers:
-            self.event_handlers[event_name] = [getattr(self, event_name), types.MethodType(handler, self)]
+            self.event_handlers[event_name] = [
+                getattr(
+                    self, event_name), types.MethodType(
+                    handler, self)]
             setattr(self, event_name, partial(self.patch_method, event_name))
         else:
             self.event_handlers[event_name].append(types.MethodType(handler, self))
@@ -317,7 +322,7 @@ class DailyTransportService(EventHandler):
     def on_app_message(self, message, sender):
         pass
 
-    def on_transcription_message(self, message:dict):
+    def on_transcription_message(self, message: dict):
         if self.loop:
             participantId = ""
             if "participantId" in message:
@@ -360,7 +365,7 @@ class DailyTransportService(EventHandler):
                 frames_or_frame: QueueFrame | list[QueueFrame] = self.threadsafe_send_queue.get()
                 if isinstance(frames_or_frame, QueueFrame):
                     frames: list[QueueFrame] = [frames_or_frame]
-                elif isinstance(frames_or_frame,  list):
+                elif isinstance(frames_or_frame, list):
                     frames: list[QueueFrame] = frames_or_frame
                 else:
                     raise Exception("Unknown type in output queue")

src/dailyai/services/deepgram_ai_services.py

@@ -7,13 +7,14 @@ import requests
 from collections.abc import AsyncGenerator
 from dailyai.services.ai_services import TTSService
 
+
 class DeepgramTTSService(TTSService):
     def __init__(self, speech_key=None, voice=None):
         super().__init__()
 
         self.voice = voice or os.getenv("DEEPGRAM_VOICE") or "alpha-asteria-en-v2"
         self.speech_key = speech_key or os.getenv("DEEPGRAM_API_KEY")
-    
+
     def get_mic_sample_rate(self):
         return 24000
 
@@ -22,8 +23,8 @@ class DeepgramTTSService(TTSService):
         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}"}
-        body = { "text": sentence }
+        body = {"text": sentence}
         async with aiohttp.ClientSession() as session:
             async with session.post(request_url, headers=headers, json=body) as r:
                 async for data in r.content:
-                    yield data
+                    yield data

src/dailyai/services/fal_ai_services.py

@@ -8,6 +8,8 @@ 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):
         super().__init__(image_size)
@@ -18,9 +20,9 @@ class FalImageGenService(ImageGenService):
             handler = fal.apps.submit(
                 "110602490-fast-sdxl",
                 arguments={
-                "prompt": sentence
+                    "prompt": sentence
                 },
-                )
+            )
             print("past fal handler init, about to wait for iter_events...")
             for event in handler.iter_events():
                 if isinstance(event, fal.apps.InProgress):

src/dailyai/services/open_ai_services.py

@@ -49,8 +49,9 @@ class OpenAILLMService(LLMService):
         else:
             return None
 
+
 class OpenAIImageGenService(ImageGenService):
-    def __init__(self, image_size:str, api_key=None, model=None):
+    def __init__(self, image_size: str, api_key=None, model=None):
         super().__init__(image_size=image_size)
         api_key = api_key or os.getenv("OPEN_AI_KEY")
         self.model = model or os.getenv("OPEN_AI_IMAGE_MODEL") or "dall-e-3"

src/dailyai/services/to_be_updated/cloudflare_ai_service.py

@@ -4,6 +4,8 @@ from services.ai_service import AIService
 
 # Note that Cloudflare's AI workers are still in beta.
 # https://developers.cloudflare.com/workers-ai/
+
+
 class CloudflareAIService(AIService):
     def __init__(self):
         super().__init__()
@@ -19,11 +21,11 @@ class CloudflareAIService(AIService):
         return response.json()
 
     # https://developers.cloudflare.com/workers-ai/models/llm/
-    def run_llm(self, messages, latest_user_message=None, stream = True):
+    def run_llm(self, messages, latest_user_message=None, stream=True):
         input = {
             "messages": [
-                { "role": "system", "content": "You are a friendly assistant" },
+                {"role": "system", "content": "You are a friendly assistant"},
-                { "role": "user", "content": sentence }
+                {"role": "user", "content": sentence}
             ]
         }
 
@@ -57,9 +59,9 @@ class CloudflareAIService(AIService):
     # https://developers.cloudflare.com/workers-ai/models/embedding/
     def run_embeddings(self, texts, size="medium"):
         models = {
-            "small": "@cf/baai/bge-small-en-v1.5", # 384 output dimensions
+            "small": "@cf/baai/bge-small-en-v1.5",  # 384 output dimensions
-            "medium": "@cf/baai/bge-base-en-v1.5", # 768 output dimensions
+            "medium": "@cf/baai/bge-base-en-v1.5",  # 768 output dimensions
-            "large": "@cf/baai/bge-large-en-v1.5" #1024 output dimensions
+            "large": "@cf/baai/bge-large-en-v1.5"  # 1024 output dimensions
         }
 
         return self.run(models[size], {"text": texts})

src/dailyai/services/to_be_updated/deepgram_ai_service.py

@@ -17,7 +17,8 @@ class DeepgramAIService(AIService):
     def run_tts(self, sentence):
         self.logger.info(f"Running deepgram tts for {sentence}")
         base_url = "https://api.beta.deepgram.com/v1/speak"
-        voice = os.getenv("DEEPGRAM_VOICE") or "alpha-apollo-en-v1"  # move this to an environment variable
+        # move this to an environment variable
+        voice = os.getenv("DEEPGRAM_VOICE") or "alpha-apollo-en-v1"
         request_url = f"{base_url}?model={voice}&encoding=linear16&container=none"
         headers = {"authorization": f"token {self.api_key}"}
 

src/dailyai/services/to_be_updated/google_ai_service.py

@@ -2,9 +2,12 @@ from services.ai_service import AIService
 import openai
 import os
 
-# To use Google Cloud's AI products, you'll need to install Google Cloud CLI and enable the TTS and in your project: https://cloud.google.com/sdk/docs/install
+# To use Google Cloud's AI products, you'll need to install Google Cloud
+# CLI and enable the TTS and in your project:
+# https://cloud.google.com/sdk/docs/install
 from google.cloud import texttospeech
 
+
 class GoogleAIService(AIService):
     def __init__(self):
         super().__init__()
@@ -15,11 +18,14 @@ class GoogleAIService(AIService):
         )
 
         self.audio_config = texttospeech.AudioConfig(
-            audio_encoding = texttospeech.AudioEncoding.LINEAR16,
+            audio_encoding=texttospeech.AudioEncoding.LINEAR16,
-            sample_rate_hertz = 16000
+            sample_rate_hertz=16000
         )
 
     def run_tts(self, sentence):
-        synthesis_input = texttospeech.SynthesisInput(text = sentence.strip())
+        synthesis_input = texttospeech.SynthesisInput(text=sentence.strip())
-        result = self.client.synthesize_speech(input=synthesis_input, voice=self.voice, audio_config=self.audio_config)
+        result = self.client.synthesize_speech(
+            input=synthesis_input,
+            voice=self.voice,
+            audio_config=self.audio_config)
         return result

src/dailyai/services/to_be_updated/huggingface_ai_service.py

@@ -1,7 +1,12 @@
 from services.ai_service import AIService
 from transformers import pipeline
 
-# These functions are just intended for testing, not production use. If you'd like to use HuggingFace, you should use your own models, or do some research into the specific models that will work best for your use case.
+# These functions are just intended for testing, not production use. If
+# you'd like to use HuggingFace, you should use your own models, or do
+# some research into the specific models that will work best for your use
+# case.
+
+
 class HuggingFaceAIService(AIService):
     def __init__(self):
         super().__init__()
@@ -10,9 +15,12 @@ class HuggingFaceAIService(AIService):
         classifier = pipeline("sentiment-analysis")
         return classifier(sentence)
 
-    # available models at https://huggingface.co/Helsinki-NLP (**not all models use 2-character language codes**)
+    # available models at https://huggingface.co/Helsinki-NLP (**not all
+    # models use 2-character language codes**)
     def run_text_translation(self, sentence, source_language, target_language):
-        translator = pipeline(f"translation", model=f"Helsinki-NLP/opus-mt-{source_language}-{target_language}")
+        translator = pipeline(
+            f"translation",
+            model=f"Helsinki-NLP/opus-mt-{source_language}-{target_language}")
 
         return translator(sentence)[0]["translation_text"]
 

src/dailyai/services/to_be_updated/mock_ai_service.py

@@ -4,6 +4,7 @@ import time
 from PIL import Image
 from services.ai_service import AIService
 
+
 class MockAIService(AIService):
     def __init__(self):
         super().__init__()
@@ -20,8 +21,7 @@ class MockAIService(AIService):
         time.sleep(1)
         return (image_url, image)
 
-    def run_llm(self, messages, latest_user_message=None, stream = True):
+    def run_llm(self, messages, latest_user_message=None, stream=True):
         for i in range(5):
             time.sleep(1)
-            yield({"choices": [{"delta": {"content": f"hello {i}!"}}]})
+            yield ({"choices": [{"delta": {"content": f"hello {i}!"}}]})
-

src/dailyai/services/to_be_updated/playht_ai_service.py

@@ -8,6 +8,7 @@ from pyht.protos.api_pb2 import Format
 
 from services.ai_service import AIService
 
+
 class PlayHTAIService(AIService):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -23,8 +24,7 @@ class PlayHTAIService(AIService):
             voice="s3://voice-cloning-zero-shot/820da3d2-3a3b-42e7-844d-e68db835a206/sarah/manifest.json",
             sample_rate=16000,
             quality="higher",
-            format=Format.FORMAT_WAV
+            format=Format.FORMAT_WAV)
-        )
 
     def close(self):
         super().close()
@@ -43,14 +43,15 @@ class PlayHTAIService(AIService):
                     fh = io.BytesIO(b)
                     fh.seek(36)
                     (data, size) = struct.unpack('<4sI', fh.read(8))
-                    self.logger.info(f"first attempt: data: {data}, size: {hex(size)}, position: {fh.tell()}")
+                    self.logger.info(
+                        f"first attempt: data: {data}, size: {hex(size)}, position: {fh.tell()}")
                     while data != b'data':
                         fh.read(size)
                         (data, size) = struct.unpack('<4sI', fh.read(8))
-                        self.logger.info(f"subsequent data: {data}, size: {hex(size)}, position: {fh.tell()}, data != data: {data != b'data'}")
+                        self.logger.info(
+                            f"subsequent data: {data}, size: {hex(size)}, position: {fh.tell()}, data != data: {data != b'data'}")
                     self.logger.info("position: ", fh.tell())
                     in_header = False
             else:
                 if len(chunk):
                     yield chunk
-

src/dailyai/tests/test_ai_services.py

@@ -6,10 +6,12 @@ from typing import AsyncGenerator, Generator
 from dailyai.services.ai_services import AIService
 from dailyai.queue_frame import EndStreamQueueFrame, QueueFrame, TextQueueFrame
 
+
 class SimpleAIService(AIService):
     async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         yield frame
 
+
 class TestBaseAIService(unittest.IsolatedAsyncioTestCase):
     async def test_async_input(self):
         service = SimpleAIService()
@@ -18,6 +20,7 @@ class TestBaseAIService(unittest.IsolatedAsyncioTestCase):
             TextQueueFrame("hello"),
             EndStreamQueueFrame()
         ]
+
         async def iterate_frames() -> AsyncGenerator[QueueFrame, None]:
             for frame in input_frames:
                 yield frame

src/samples/deprecated/simple-sample/simple-sample.py

@@ -15,11 +15,12 @@ from dailyai.services.ai_services import AIServiceConfig
 from dailyai.services.azure_ai_services import AzureImageGenService, AzureTTSService, AzureLLMService
 from dailyai.services.deepgram_ai_services import DeepgramTTSService
 
+
 def add_bot_to_room(room_url, token, expiration) -> None:
 
     # A simple prompt for a simple sample.
     message_handler = MessageHandler(
-    """
+        """
         You are a sample bot in a WebRTC session. You'll receive input as transcriptions of user's
         speech, and your responses will be converted to audio via a TTS service.
         Answer user's questions and be friendly, and if you can, give some ideas about how someone
@@ -62,6 +63,7 @@ def add_bot_to_room(room_url, token, expiration) -> None:
     services.tts.close()
     services.llm.close()
 
+
 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")

src/samples/deprecated/static-sprite/sprite-sample.py

@@ -20,6 +20,7 @@ from dailyai.message_handler.message_handler import MessageHandler
 from dailyai.services.ai_services import AIServiceConfig
 from dailyai.services.azure_ai_services import AzureImageGenService, AzureTTSService, AzureLLMService
 
+
 class StaticSpriteResponse(OrchestratorResponse):
 
     def __init__(
@@ -29,8 +30,8 @@ class StaticSpriteResponse(OrchestratorResponse):
         output_queue
     ) -> None:
         super().__init__(services, message_handler, output_queue)
-        self.image_bytes:bytes | None = None
+        self.image_bytes: bytes | None = None
-        self.filenames = None # override this in subclasses
+        self.filenames = None  # override this in subclasses
 
     def start_preparation(self) -> None:
         full_path = os.path.join(os.path.dirname(__file__), "sprites/", self.filename)
@@ -82,7 +83,7 @@ def add_bot_to_room(room_url, token, expiration) -> None:
 
     # A simple prompt for a simple sample.
     message_handler = MessageHandler(
-    """
+        """
         You are a sample bot in a WebRTC session. You'll receive input as transcriptions of user's
         speech, and your responses will be converted to audio via a TTS service.
         Answer user's questions and be friendly, and if you can, give some ideas about how someone
@@ -143,6 +144,7 @@ def add_bot_to_room(room_url, token, expiration) -> None:
     services.image.close()
     services.llm.close()
 
+
 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")

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

@@ -4,6 +4,7 @@ import asyncio
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
+
 async def main(room_url):
     # create a transport service object using environment variables for
     # the transport service's API key, room url, and any other configuration.

src/samples/foundational/01a-greet-user.py

@@ -7,6 +7,7 @@ from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureTTSService
 from dailyai.services.deepgram_ai_services import DeepgramTTSService
 
+
 async def main(room_url):
     # create a transport service object using environment variables for
     # the transport service's API key, room url, and any other configuration.
@@ -33,17 +34,20 @@ async def main(room_url):
 
     # Register an event handler so we can play the audio when the participant joins.
     print("settting up handler")
+
     @transport.event_handler("on_participant_joined")
     async def on_participant_joined(transport, participant):
         print(f"participant joined: {participant['info']['userName']}")
         if participant["info"]["isLocal"]:
             return
-        audio_generator: AsyncGenerator[bytes, None] = tts.run_tts(f"Hello there, {participant['info']['userName']}!")
+        audio_generator: AsyncGenerator[bytes, None] = tts.run_tts(
+            f"Hello there, {participant['info']['userName']}!")
 
         async for audio in audio_generator:
             transport.output_queue.put(QueueFrame(FrameType.AUDIO, audio))
 
     print("setting up call state handler")
+
     @transport.event_handler("on_call_state_updated")
     async def on_call_joined(transport, state):
         print(f"call state callback: {state}")

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

@@ -6,6 +6,7 @@ from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
+
 async def main(room_url):
     meeting_duration_minutes = 1
     transport = DailyTransportService(

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

@@ -8,6 +8,7 @@ from dailyai.services.open_ai_services import OpenAIImageGenService
 local_joined = False
 participant_joined = False
 
+
 async def main(room_url):
     meeting_duration_minutes = 1
     transport = DailyTransportService(
@@ -23,8 +24,9 @@ async def main(room_url):
 
     imagegen = OpenAIImageGenService(image_size="1024x1024")
     image_task = asyncio.create_task(
-        imagegen.run_to_queue(transport.send_queue, [TextQueueFrame("a cat in the style of picasso")])
+        imagegen.run_to_queue(
-    )
+            transport.send_queue, [
+                TextQueueFrame("a cat in the style of picasso")]))
 
     @transport.event_handler("on_participant_joined")
     async def on_participant_joined(transport, participant):

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

@@ -7,7 +7,8 @@ from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.queue_frame import EndStreamQueueFrame, LLMMessagesQueueFrame
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
-async def main(room_url:str):
+
+async def main(room_url: str):
     global transport
     global llm
     global tts

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

@@ -7,6 +7,7 @@ from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.fal_ai_services import FalImageGenService
 
+
 async def main(room_url):
     meeting_duration_minutes = 5
     transport = DailyTransportService(
@@ -98,7 +99,7 @@ async def main(room_url):
 
     await transport.run()
 
-if __name__=="__main__":
+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"

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

@@ -8,7 +8,8 @@ from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.queue_aggregators import LLMContextAggregator
 
-async def main(room_url:str, token):
+
+async def main(room_url: str, token):
     global transport
     global llm
     global tts

src/samples/foundational/07-interruptible.py

@@ -11,7 +11,7 @@ from dailyai.services.azure_ai_services import AzureLLMService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
 
-async def main(room_url:str, token):
+async def main(room_url: str, token):
     global transport
     global llm
     global tts

src/samples/image-gen.py

@@ -11,7 +11,8 @@ from dailyai.queue_frame import QueueFrame, FrameType
 from dailyai.services.fal_ai_services import FalImageGenService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
-async def main(room_url:str, token):
+
+async def main(room_url: str, token):
     global transport
     global llm
     global tts
@@ -32,7 +33,6 @@ async def main(room_url:str, token):
     tts = AzureTTSService()
     img = FalImageGenService()
 
-
     async def handle_transcriptions():
         print("handle_transcriptions got called")
 
@@ -41,7 +41,7 @@ async def main(room_url:str, token):
             print(f"transcription message: {message}")
             if message["session_id"] == transport.my_participant_id:
                 continue
-            finder =  message["text"].find("start over")
+            finder = message["text"].find("start over")
             print(f"finder: {finder}")
             if finder >= 0:
                 async for audio in tts.run_tts(f"Resetting."):
@@ -69,7 +69,8 @@ async def main(room_url:str, token):
         if participant["info"]["isLocal"]:
             return
         async for audio in tts.run_tts("Describe an image, and I'll create it."):
-            audio_generator = tts.run_tts(f"Hello, {participant['info']['userName']}! Describe an image and I'll create it. To start over, just say 'start over'.")
+            audio_generator = tts.run_tts(
+                f"Hello, {participant['info']['userName']}! Describe an image and I'll create it. To start over, just say 'start over'.")
             async for audio in audio_generator:
                 transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))