Merge pull request #69 from daily-co/add-github-linting-workflow

github: add linting workflow

.github/workflows/lint.yaml

@@ -0,0 +1,32 @@
+name: lint
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - "**"
+    paths-ignore:
+      - "docs/**"
+
+concurrency:
+  group: build-lint-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  autopep8:
+    name: "Formatting lints"
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+      - name: autopep8
+        id: autopep8
+        uses: peter-evans/autopep8@v2
+        with:
+          args: --exit-code -r -d -a -a src/
+      - name: Fail if autopep8 requires changes
+        if: steps.autopep8.outputs.exit-code == 2
+        run: exit 1

src/dailyai/pipeline/aggregators.py

@@ -57,7 +57,8 @@ class ResponseAggregator(FrameProcessor):
             # Sometimes VAD triggers quickly on and off. If we don't get any transcription,
             # it creates empty LLM message queue frames
             if len(self.aggregation) > 0:
-                self.messages.append({"role": self._role, "content": self.aggregation})
+                self.messages.append(
+                    {"role": self._role, "content": self.aggregation})
                 self.aggregation = ""
                 yield self._end_frame()
                 yield LLMMessagesQueueFrame(self.messages)
@@ -110,7 +111,8 @@ class LLMContextAggregator(AIService):
         self.pass_through = pass_through
 
     async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        # We don't do anything with non-text frames, pass it along to next in the pipeline.
+        # We don't do anything with non-text frames, pass it along to next in
+        # the pipeline.
         if not isinstance(frame, TextFrame):
             yield frame
             return
@@ -132,7 +134,8 @@ class LLMContextAggregator(AIService):
             # though we check it above
             self.sentence += frame.text
             if self.sentence.endswith((".", "?", "!")):
-                self.messages.append({"role": self.role, "content": self.sentence})
+                self.messages.append(
+                    {"role": self.role, "content": self.sentence})
                 self.sentence = ""
                 yield LLMMessagesQueueFrame(self.messages)
         else:
@@ -144,17 +147,24 @@ class LLMContextAggregator(AIService):
 
 class LLMUserContextAggregator(LLMContextAggregator):
     def __init__(
-        self, messages: list[dict], bot_participant_id=None, complete_sentences=True
+            self,
-    ):
+            messages: list[dict],
+            bot_participant_id=None,
+            complete_sentences=True):
         super().__init__(
-            messages, "user", bot_participant_id, complete_sentences, pass_through=False
+            messages,
-        )
+            "user",
+            bot_participant_id,
+            complete_sentences,
+            pass_through=False)
 
 
 class LLMAssistantContextAggregator(LLMContextAggregator):
     def __init__(
-        self, messages: list[dict], bot_participant_id=None, complete_sentences=True
+            self,
-    ):
+            messages: list[dict],
+            bot_participant_id=None,
+            complete_sentences=True):
         super().__init__(
             messages,
             "assistant",
@@ -328,7 +338,8 @@ class ParallelPipeline(FrameProcessor):
                 continue
             seen_ids.add(id(frame))
 
-            # Skip passing along EndParallelPipeQueueFrame, because we use them for our own flow control.
+            # Skip passing along EndParallelPipeQueueFrame, because we use them
+            # for our own flow control.
             if not isinstance(frame, EndPipeFrame):
                 yield frame
 

src/dailyai/pipeline/frames.py

@@ -13,7 +13,7 @@ class ControlFrame(Frame):
     # Control frames should contain no instance data, so
     # equality is based solely on the class.
     def __eq__(self, other):
-        return type(other) == self.__class__
+        return isinstance(other, self.__class__)
 
 
 class StartFrame(ControlFrame):

src/dailyai/pipeline/merge_pipeline.py

@@ -6,7 +6,8 @@ from dailyai.pipeline.pipeline import Pipeline
 class SequentialMergePipeline(Pipeline):
     """This class merges the sink queues from a list of pipelines. Frames from
     each pipeline's sink are merged in the order of pipelines in the list."""
-    def __init__(self, pipelines:List[Pipeline]):
+
+    def __init__(self, pipelines: List[Pipeline]):
         super().__init__([])
         self.pipelines = pipelines
 
@@ -14,7 +15,9 @@ class SequentialMergePipeline(Pipeline):
         for pipeline in self.pipelines:
             while True:
                 frame = await pipeline.sink.get()
-                if isinstance(frame, EndFrame) or isinstance(frame, EndPipeFrame):
+                if isinstance(
+                        frame, EndFrame) or isinstance(
+                        frame, EndPipeFrame):
                     break
                 await self.sink.put(frame)
 

src/dailyai/pipeline/opeanai_llm_aggregator.py

@@ -69,7 +69,10 @@ class OpenAIContextAggregator(FrameProcessor):
         else:
             yield frame
 
-    def string_aggregator(self, frame: Frame, aggregation: str | None) -> str | None:
+    def string_aggregator(
+            self,
+            frame: Frame,
+            aggregation: str | None) -> str | None:
         if not isinstance(frame, TextFrame):
             raise TypeError(
                 "Frame must be a TextFrame instance to be aggregated by a string aggregator."
@@ -94,7 +97,7 @@ class OpenAIUserContextAggregator(OpenAIContextAggregator):
 
 class OpenAIAssistantContextAggregator(OpenAIContextAggregator):
 
-    def __init__(self, context:OpenAILLMContext):
+    def __init__(self, context: OpenAILLMContext):
         super().__init__(
             context,
             aggregator=self.string_aggregator,

src/dailyai/pipeline/pipeline.py

@@ -89,7 +89,8 @@ class Pipeline:
                 ):
                     break
         except asyncio.CancelledError:
-            # this means there's been an interruption, do any cleanup necessary here.
+            # this means there's been an interruption, do any cleanup necessary
+            # here.
             for processor in self.processors:
                 await processor.interrupted()
             pass
@@ -107,4 +108,3 @@ class Pipeline:
                     yield final_frame
         else:
             yield initial_frame
-

src/dailyai/services/ai_services.py

@@ -28,6 +28,7 @@ class AIService(FrameProcessor):
     def __init__(self):
         self.logger = logging.getLogger("dailyai")
 
+
 class LLMService(AIService):
     """This class is a no-op but serves as a base class for LLM services."""
 
@@ -76,7 +77,8 @@ class TTSService(AIService):
             async for audio_chunk in self.run_tts(text):
                 yield AudioFrame(audio_chunk)
 
-            # note we pass along the text frame *after* the audio, so the text frame is completed after the audio is processed.
+            # note we pass along the text frame *after* the audio, so the text
+            # frame is completed after the audio is processed.
             yield TextFrame(text)
 
 

src/dailyai/services/anthropic_llm_service.py

@@ -9,7 +9,11 @@ from dailyai.services.ai_services import LLMService
 
 class AnthropicLLMService(LLMService):
 
-    def __init__(self, api_key, model="claude-3-opus-20240229", max_tokens=1024):
+    def __init__(
+            self,
+            api_key,
+            model="claude-3-opus-20240229",
+            max_tokens=1024):
         super().__init__()
         self.client = AsyncAnthropic(api_key=api_key)
         self.model = model

src/dailyai/services/azure_ai_services.py

@@ -44,8 +44,7 @@ class AzureTTSService(TTSService):
             "<mstts:express-as style='lyrical' styledegree='2' role='SeniorFemale'>"
             "<prosody rate='1.05'>"
             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:
@@ -55,16 +54,22 @@ class AzureTTSService(TTSService):
         elif result.reason == ResultReason.Canceled:
             cancellation_details = result.cancellation_details
             self.logger.info(
-                "Speech synthesis canceled: {}".format(cancellation_details.reason)
+                "Speech synthesis canceled: {}".format(
-            )
+                    cancellation_details.reason))
             if cancellation_details.reason == CancellationReason.Error:
                 self.logger.info(
-                    "Error details: {}".format(cancellation_details.error_details)
+                    "Error details: {}".format(
-                )
+                        cancellation_details.error_details))
 
 
 class AzureLLMService(BaseOpenAILLMService):
-    def __init__(self, *, api_key, endpoint, api_version="2023-12-01-preview", model):
+    def __init__(
+            self,
+            *,
+            api_key,
+            endpoint,
+            api_version="2023-12-01-preview",
+            model):
         self._endpoint = endpoint
         self._api_version = api_version
 
@@ -101,7 +106,9 @@ class AzureImageGenServiceREST(ImageGenService):
 
     async def run_image_gen(self, sentence) -> tuple[str, bytes]:
         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,
@@ -112,7 +119,8 @@ class AzureImageGenServiceREST(ImageGenService):
             url, headers=headers, json=body
         ) as submission:
             # We never get past this line, because this header isn't
-            # defined on a 429 response, but something is eating our exceptions!
+            # defined on a 429 response, but something is eating our
+            # exceptions!
             operation_location = submission.headers["operation-location"]
             status = ""
             attempts_left = 120
@@ -130,8 +138,7 @@ class AzureImageGenServiceREST(ImageGenService):
                 status = json_response["status"]
 
             image_url = (
-                json_response["result"]["data"][0]["url"] if json_response else None
+                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

src/dailyai/services/base_transport_service.py

@@ -127,12 +127,14 @@ class BaseTransportService:
 
         self._logger: logging.Logger = logging.getLogger()
 
-    async def run(self, pipeline:Pipeline | None=None, override_pipeline_source_queue=True):
+    async def run(self, pipeline: Pipeline | None = None, override_pipeline_source_queue=True):
         self._prerun()
 
-        async_output_queue_marshal_task = asyncio.create_task(self._marshal_frames())
+        async_output_queue_marshal_task = asyncio.create_task(
+            self._marshal_frames())
 
-        self._camera_thread = threading.Thread(target=self._run_camera, daemon=True)
+        self._camera_thread = threading.Thread(
+            target=self._run_camera, daemon=True)
         self._camera_thread.start()
 
         self._frame_consumer_thread = threading.Thread(
@@ -182,7 +184,7 @@ class BaseTransportService:
         if self._vad_enabled:
             self._vad_thread.join()
 
-    async def run_pipeline(self, pipeline:Pipeline, override_pipeline_source_queue=True):
+    async def run_pipeline(self, pipeline: Pipeline, override_pipeline_source_queue=True):
         pipeline.set_sink(self.send_queue)
         if override_pipeline_source_queue:
             pipeline.set_source(self.receive_queue)
@@ -217,7 +219,8 @@ class BaseTransportService:
                     break
 
         if post_processor:
-            post_process_task = asyncio.create_task(post_process(post_processor))
+            post_process_task = asyncio.create_task(
+                post_process(post_processor))
 
         started = False
 
@@ -244,7 +247,7 @@ class BaseTransportService:
 
         await asyncio.gather(pipeline_task, post_process_task)
 
-    async def say(self, text:str, tts:TTSService):
+    async def say(self, text: str, tts: TTSService):
         """Say a phrase. Use with caution; this bypasses any running pipelines."""
         async for frame in tts.process_frame(TextFrame(text)):
             await self.send_queue.put(frame)
@@ -290,7 +293,8 @@ class BaseTransportService:
             audio_chunk = self.read_audio_frames(self._vad_samples)
             audio_int16 = np.frombuffer(audio_chunk, np.int16)
             audio_float32 = int2float(audio_int16)
-            new_confidence = model(torch.from_numpy(audio_float32), 16000).item()
+            new_confidence = model(
+                torch.from_numpy(audio_float32), 16000).item()
             speaking = new_confidence > 0.5
 
             if speaking:
@@ -320,8 +324,8 @@ class BaseTransportService:
             ):
                 if self._loop:
                     asyncio.run_coroutine_threadsafe(
-                        self.receive_queue.put(UserStartedSpeakingFrame()), self._loop
+                        self.receive_queue.put(
-                    )
+                            UserStartedSpeakingFrame()), self._loop)
                 # self.interrupt()
                 self._vad_state = VADState.SPEAKING
                 self._vad_starting_count = 0
@@ -331,8 +335,8 @@ class BaseTransportService:
             ):
                 if self._loop:
                     asyncio.run_coroutine_threadsafe(
-                        self.receive_queue.put(UserStoppedSpeakingFrame()), self._loop
+                        self.receive_queue.put(
-                    )
+                            UserStoppedSpeakingFrame()), self._loop)
                 self._vad_state = VADState.QUIET
                 self._vad_stopping_count = 0
 
@@ -370,7 +374,9 @@ class BaseTransportService:
                     self.receive_queue.put(frame), self._loop
                 )
 
-        asyncio.run_coroutine_threadsafe(self.receive_queue.put(EndFrame()), self._loop)
+        asyncio.run_coroutine_threadsafe(
+            self.receive_queue.put(
+                EndFrame()), self._loop)
 
     def _set_image(self, image: bytes):
         self._images = itertools.cycle([image])
@@ -378,7 +384,7 @@ class BaseTransportService:
     def _set_images(self, images: list[bytes], start_frame=0):
         self._images = itertools.cycle(images)
 
-    def send_app_message(self, message: Any, participantId:str|None):
+    def send_app_message(self, message: Any, participantId: str | None):
         """ Child classes should override this to send a custom message to the room. """
         pass
 
@@ -401,17 +407,18 @@ class BaseTransportService:
         largest_write_size = 8000
         while True:
             try:
-                frames_or_frame: Frame | list[Frame] = self._threadsafe_send_queue.get()
+                frames_or_frame: Frame | list[Frame] = self._threadsafe_send_queue.get(
+                )
                 if (
                     isinstance(frames_or_frame, AudioFrame)
                     and len(frames_or_frame.data) > largest_write_size
                 ):
                     # subdivide large audio frames to enable interruption
                     frames = []
-                    for i in range(0, len(frames_or_frame.data), largest_write_size):
+                    for i in range(0, len(frames_or_frame.data),
-                        frames.append(
+                                   largest_write_size):
-                            AudioFrame(frames_or_frame.data[i : i + largest_write_size])
+                        frames.append(AudioFrame(
-                        )
+                            frames_or_frame.data[i: i + largest_write_size]))
                 elif isinstance(frames_or_frame, Frame):
                     frames: list[Frame] = [frames_or_frame]
                 elif isinstance(frames_or_frame, list):
@@ -430,7 +437,8 @@ class BaseTransportService:
                             )
                         return
 
-                    # if interrupted, we just pull frames off the queue and discard them
+                    # if interrupted, we just pull frames off the queue and
+                    # discard them
                     if not self._is_interrupted.is_set():
                         if frame:
                             if isinstance(frame, AudioFrame):
@@ -441,14 +449,16 @@ class BaseTransportService:
                                     len(b) % smallest_write_size
                                 )
                                 if truncated_length:
-                                    self.write_frame_to_mic(bytes(b[:truncated_length]))
+                                    self.write_frame_to_mic(
+                                        bytes(b[:truncated_length]))
                                     b = b[truncated_length:]
                             elif isinstance(frame, ImageFrame):
                                 self._set_image(frame.image)
                             elif isinstance(frame, SpriteFrame):
                                 self._set_images(frame.images)
                             elif isinstance(frame, SendAppMessageFrame):
-                                self.send_app_message(frame.message, frame.participantId)
+                                self.send_app_message(
+                                    frame.message, frame.participantId)
                         elif len(b):
                             self.write_frame_to_mic(bytes(b))
                             b = bytearray()
@@ -457,7 +467,8 @@ class BaseTransportService:
                         # can cause static in the audio stream.
                         if len(b):
                             truncated_length = len(b) - (len(b) % 160)
-                            self.write_frame_to_mic(bytes(b[:truncated_length]))
+                            self.write_frame_to_mic(
+                                bytes(b[:truncated_length]))
                             b = bytearray()
 
                         if isinstance(frame, StartFrame):
@@ -479,5 +490,6 @@ class BaseTransportService:
 
                 b = bytearray()
             except Exception as e:
-                self._logger.error(f"Exception in frame_consumer: {e}, {len(b)}")
+                self._logger.error(
+                    f"Exception in frame_consumer: {e}, {len(b)}")
                 raise e

src/dailyai/services/daily_transport_service.py

@@ -48,7 +48,8 @@ class DailyTransportService(BaseTransportService, EventHandler):
         start_transcription: bool = False,
         **kwargs,
     ):
-        super().__init__(**kwargs)  # This will call BaseTransportService.__init__ method, not EventHandler
+        # This will call BaseTransportService.__init__ method, not EventHandler
+        super().__init__(**kwargs)
 
         self._room_url: str = room_url
         self._bot_name: str = bot_name
@@ -83,9 +84,11 @@ class DailyTransportService(BaseTransportService, EventHandler):
             for handler in self._event_handlers[event_name]:
                 if inspect.iscoroutinefunction(handler):
                     if self._loop:
-                        future = asyncio.run_coroutine_threadsafe(handler(*args, **kwargs), self._loop)
+                        future = asyncio.run_coroutine_threadsafe(
+                            handler(*args, **kwargs), self._loop)
 
-                        # wait for the coroutine to finish. This will also raise any exceptions raised by the coroutine.
+                        # wait for the coroutine to finish. This will also
+                        # raise any exceptions raised by the coroutine.
                         future.result()
                     else:
                         raise Exception(
@@ -98,7 +101,8 @@ class DailyTransportService(BaseTransportService, EventHandler):
 
     def add_event_handler(self, event_name: str, handler):
         if not event_name.startswith("on_"):
-            raise Exception(f"Event handler {event_name} must start with 'on_'")
+            raise Exception(
+                f"Event handler {event_name} must start with 'on_'")
 
         methods = inspect.getmembers(self, predicate=inspect.ismethod)
         if event_name not in [method[0] for method in methods]:
@@ -111,7 +115,8 @@ class DailyTransportService(BaseTransportService, EventHandler):
                     handler, self)]
             setattr(self, event_name, partial(self._patch_method, event_name))
         else:
-            self._event_handlers[event_name].append(types.MethodType(handler, self))
+            self._event_handlers[event_name].append(
+                types.MethodType(handler, self))
 
     def event_handler(self, event_name: str):
         def decorator(handler):
@@ -148,8 +153,7 @@ class DailyTransportService(BaseTransportService, EventHandler):
 
         if self._camera_enabled:
             self.camera: VirtualCameraDevice = Daily.create_camera_device(
-                "camera", width=self._camera_width, height=self._camera_height, color_format="RGB"
+                "camera", width=self._camera_width, height=self._camera_height, color_format="RGB")
-            )
 
         if self._speaker_enabled or self._vad_enabled:
             self._speaker: VirtualSpeakerDevice = Daily.create_speaker_device(
@@ -249,7 +253,7 @@ class DailyTransportService(BaseTransportService, EventHandler):
         if len(self.client.participants()) < self._min_others_count + 1:
             self._stop_threads.set()
 
-    def on_app_message(self, message:Any, sender:str):
+    def on_app_message(self, message: Any, sender: str):
         if self._loop:
             frame = ReceivedAppMessageFrame(message, sender)
             print(frame)
@@ -265,8 +269,10 @@ class DailyTransportService(BaseTransportService, EventHandler):
             elif "session_id" in message:
                 participantId = message["session_id"]
             if self._my_participant_id and participantId != self._my_participant_id:
-                frame = TranscriptionQueueFrame(message["text"], participantId, message["timestamp"])
+                frame = TranscriptionQueueFrame(
-                asyncio.run_coroutine_threadsafe(self.receive_queue.put(frame), self._loop)
+                    message["text"], participantId, message["timestamp"])
+                asyncio.run_coroutine_threadsafe(
+                    self.receive_queue.put(frame), self._loop)
 
     def on_transcription_error(self, message):
         self._logger.error(f"Transcription error: {message}")

src/dailyai/services/deepgram_ai_service.py

@@ -25,7 +25,9 @@ class DeepgramAIService(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={self._sample_rate}"
-        headers = {"authorization": f"token {self._api_key}", "Content-Type": "application/json"}
+        headers = {
+            "authorization": f"token {self._api_key}",
+            "Content-Type": "application/json"}
         data = {"text": sentence}
 
         async with self._aiohttp_session.post(

src/dailyai/services/deepgram_ai_services.py

@@ -9,7 +9,12 @@ from dailyai.services.ai_services import TTSService
 
 
 class DeepgramTTSService(TTSService):
-    def __init__(self, *, aiohttp_session, api_key, voice="alpha-asteria-en-v2"):
+    def __init__(
+            self,
+            *,
+            aiohttp_session,
+            api_key,
+            voice="alpha-asteria-en-v2"):
         super().__init__()
 
         self._voice = voice

src/dailyai/services/elevenlabs_ai_service.py

@@ -28,7 +28,9 @@ class ElevenLabsTTSService(TTSService):
     async def run_tts(self, sentence) -> AsyncGenerator[bytes, None]:
         url = f"https://api.elevenlabs.io/v1/text-to-speech/{self._voice_id}/stream"
         payload = {"text": sentence, "model_id": self._model}
-        querystring = {"output_format": "pcm_16000", "optimize_streaming_latency": 2}
+        querystring = {
+            "output_format": "pcm_16000",
+            "optimize_streaming_latency": 2}
         headers = {
             "xi-api-key": self._api_key,
             "Content-Type": "application/json",

src/dailyai/services/fal_ai_services.py

@@ -33,7 +33,7 @@ class FalImageGenService(ImageGenService):
         def get_image_url(sentence, size):
             handler = fal.apps.submit(
                 "110602490-fast-sdxl",
-                #"fal-ai/fast-sdxl",
+                # "fal-ai/fast-sdxl",
                 arguments={"prompt": sentence},
             )
             for event in handler.iter_events():

src/dailyai/services/local_transport_service.py

@@ -15,13 +15,15 @@ class LocalTransportService(BaseTransportService):
         self._tk_root = kwargs.get("tk_root") or None
 
         if self._camera_enabled and not self._tk_root:
-            raise ValueError("If camera is enabled, a tkinter root must be provided")
+            raise ValueError(
+                "If camera is enabled, a tkinter root must be provided")
 
         if self._speaker_enabled:
             self._speaker_buffer_pending = bytearray()
 
     async def _write_frame_to_tkinter(self, frame: bytes):
-        data = f"P6 {self._camera_width} {self._camera_height} 255 ".encode() + frame
+        data = f"P6 {self._camera_width} {self._camera_height} 255 ".encode() + \
+            frame
         photo = tk.PhotoImage(
             width=self._camera_width,
             height=self._camera_height,
@@ -29,7 +31,8 @@ class LocalTransportService(BaseTransportService):
             format="PPM")
         self._image_label.config(image=photo)
 
-        # This holds a reference to the photo, preventing it from being garbage collected.
+        # This holds a reference to the photo, preventing it from being garbage
+        # collected.
         self._image_label.image = photo  # type: ignore
 
     def write_frame_to_camera(self, frame: bytes):
@@ -61,8 +64,13 @@ class LocalTransportService(BaseTransportService):
         if self._camera_enabled:
             # Start with a neutral gray background.
             array = np.ones((1024, 1024, 3)) * 128
-            data = f"P5 {1024} {1024} 255 ".encode() + array.astype(np.uint8).tobytes()
+            data = f"P5 {1024} {1024} 255 ".encode(
-            photo = tk.PhotoImage(width=1024, height=1024, data=data, format="PPM")
+            ) + array.astype(np.uint8).tobytes()
+            photo = tk.PhotoImage(
+                width=1024,
+                height=1024,
+                data=data,
+                format="PPM")
             self._image_label = tk.Label(self._tk_root, image=photo)
             self._image_label.pack()
 

src/dailyai/services/openai_api_llm_service.py

@@ -110,7 +110,8 @@ class BaseOpenAILLMService(LLMService):
                     yield LLMFunctionStartFrame(function_name=tool_call.function.name)
                 if tool_call.function and tool_call.function.arguments:
                     # Keep iterating through the response to collect all the argument fragments and
-                    # yield a complete LLMFunctionCallFrame after run_llm_async completes
+                    # yield a complete LLMFunctionCallFrame after run_llm_async
+                    # completes
                     arguments += tool_call.function.arguments
             elif chunk.choices[0].delta.content:
                 yield TextFrame(chunk.choices[0].delta.content)

src/dailyai/services/openai_llm_context.py

@@ -16,7 +16,8 @@ class OpenAILLMContext:
         tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
         tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN
     ):
-        self.messages: List[ChatCompletionMessageParam] = messages if messages else []
+        self.messages: List[ChatCompletionMessageParam] = messages if messages else [
+        ]
         self.tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = tool_choice
         self.tools: List[ChatCompletionToolParam] | NotGiven = tools
 
@@ -25,13 +26,13 @@ class OpenAILLMContext:
         context = OpenAILLMContext()
         for message in messages:
             context.add_message({
-                "content":message["content"],
+                "content": message["content"],
-                "role":message["role"],
+                "role": message["role"],
-                "name":message["name"] if "name" in message else message["role"]
+                "name": message["name"] if "name" in message else message["role"]
             })
         return context
 
-    #def __deepcopy__(self, memo):
+    # def __deepcopy__(self, memo):
 
     def add_message(self, message: ChatCompletionMessageParam):
         self.messages.append(message)
@@ -44,9 +45,10 @@ class OpenAILLMContext:
     ):
         self.tool_choice = tool_choice
 
-    def set_tools(self, tools:List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN):
+    def set_tools(
+            self,
+            tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN):
         if tools != NOT_GIVEN and len(tools) == 0:
             tools = NOT_GIVEN
 
         self.tools = tools
-

src/dailyai/services/to_be_updated/cloudflare_ai_service.py

@@ -17,7 +17,10 @@ class CloudflareAIService(AIService):
 
     # base endpoint, used by the others
     def run(self, model, input):
-        response = requests.post(f"{self.api_base_url}{model}", headers=self.headers, json=input)
+        response = requests.post(
+            f"{self.api_base_url}{model}",
+            headers=self.headers,
+            json=input)
         return response.json()
 
     # https://developers.cloudflare.com/workers-ai/models/llm/
@@ -41,7 +44,8 @@ class CloudflareAIService(AIService):
 
     # https://developers.cloudflare.com/workers-ai/models/sentiment-analysis/
     def run_text_sentiment(self, sentence):
-        return self.run("@cf/huggingface/distilbert-sst-2-int8", {"text": sentence})
+        return self.run("@cf/huggingface/distilbert-sst-2-int8",
+                        {"text": sentence})
 
     # https://developers.cloudflare.com/workers-ai/models/image-classification/
     def run_image_classification(self, image_url):

src/dailyai/tests/integration/integration_azure_llm.py

@@ -10,7 +10,7 @@ from openai.types.chat import (
     ChatCompletionSystemMessageParam,
 )
 
-if __name__=="__main__":
+if __name__ == "__main__":
     async def test_chat():
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
@@ -19,8 +19,7 @@ if __name__=="__main__":
         )
         context = OpenAILLMContext()
         message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam(
-            content="Please tell the world hello.", name="system", role="system"
+            content="Please tell the world hello.", name="system", role="system")
-        )
         context.add_message(message)
         frame = OpenAILLMContextFrame(context)
         async for s in llm.process_frame(frame):

src/dailyai/tests/integration/integration_ollama_llm.py

@@ -9,13 +9,12 @@ from openai.types.chat import (
 )
 from dailyai.services.ollama_ai_services import OLLamaLLMService
 
-if __name__=="__main__":
+if __name__ == "__main__":
     async def test_chat():
         llm = OLLamaLLMService()
         context = OpenAILLMContext()
         message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam(
-            content="Please tell the world hello.", name="system", role="system"
+            content="Please tell the world hello.", name="system", role="system")
-        )
         context.add_message(message)
         frame = OpenAILLMContextFrame(context)
         async for s in llm.process_frame(frame):

src/dailyai/tests/integration/integration_openai_llm.py

@@ -18,7 +18,7 @@ if __name__ == "__main__":
         tools = [
             ChatCompletionToolParam(
                 type="function",
-                function= {
+                function={
                     "name": "get_current_weather",
                     "description": "Get the current weather",
                     "parameters": {
@@ -30,15 +30,17 @@ if __name__ == "__main__":
                             },
                             "format": {
                                 "type": "string",
-                                "enum": ["celsius", "fahrenheit"],
+                                "enum": [
+                                    "celsius",
+                                    "fahrenheit"],
                                 "description": "The temperature unit to use. Infer this from the users location.",
                             },
                         },
-                        "required": ["location", "format"],
+                        "required": [
+                            "location",
+                            "format"],
                     },
-                }
+                })]
-            )
-        ]
 
         api_key = os.getenv("OPENAI_API_KEY")
 
@@ -70,8 +72,7 @@ if __name__ == "__main__":
         )
         context = OpenAILLMContext()
         message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam(
-            content="Please tell the world hello.", name="system", role="system"
+            content="Please tell the world hello.", name="system", role="system")
-        )
         context.add_message(message)
         frame = OpenAILLMContextFrame(context)
         async for s in llm.process_frame(frame):

src/dailyai/tests/test_aggregators.py

@@ -45,10 +45,9 @@ class TestDailyFrameAggregators(unittest.IsolatedAsyncioTestCase):
 
     async def test_gated_accumulator(self):
         gated_aggregator = GatedAggregator(
-            gate_open_fn=lambda frame: isinstance(frame, ImageFrame),
+            gate_open_fn=lambda frame: isinstance(
-            gate_close_fn=lambda frame: isinstance(frame, LLMResponseStartFrame),
+                frame, ImageFrame), gate_close_fn=lambda frame: isinstance(
-            start_open=False,
+                frame, LLMResponseStartFrame), start_open=False, )
-        )
 
         frames = [
             LLMResponseStartFrame(),
@@ -76,12 +75,14 @@ class TestDailyFrameAggregators(unittest.IsolatedAsyncioTestCase):
 
     async def test_parallel_pipeline(self):
 
-        async def slow_add(sleep_time:float, name:str, x: str):
+        async def slow_add(sleep_time: float, name: str, x: str):
             await asyncio.sleep(sleep_time)
             return ":".join([x, name])
 
-        pipe1_annotation = StatelessTextTransformer(functools.partial(slow_add, 0.1, 'pipe1'))
+        pipe1_annotation = StatelessTextTransformer(
-        pipe2_annotation = StatelessTextTransformer(functools.partial(slow_add, 0.2, 'pipe2'))
+            functools.partial(slow_add, 0.1, 'pipe1'))
+        pipe2_annotation = StatelessTextTransformer(
+            functools.partial(slow_add, 0.2, 'pipe2'))
         sentence_aggregator = SentenceAggregator()
         add_dots = StatelessTextTransformer(lambda x: x + ".")
 

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

@@ -32,7 +32,8 @@ async def main(room_url):
 
         pipeline = Pipeline([tts])
 
-        # Register an event handler so we can play the audio when the participant joins.
+        # Register an event handler so we can play the audio when the
+        # participant joins.
         @transport.event_handler("on_participant_joined")
         async def on_participant_joined(transport, participant):
             if participant["info"]["isLocal"]:

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

@@ -10,6 +10,7 @@ logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
 logger = logging.getLogger("dailyai")
 logger.setLevel(logging.DEBUG)
 
+
 async def main():
     async with aiohttp.ClientSession() as session:
         meeting_duration_minutes = 1

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

@@ -33,17 +33,16 @@ async def main(room_url):
         )
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
 
         messages = [
             {
                 "role": "system",
                 "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
-            }
+            }]
-        ]
 
-        pipeline= Pipeline([llm, tts])
+        pipeline = Pipeline([llm, tts])
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):

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

@@ -40,7 +40,8 @@ async def main(room_url):
         async def on_first_other_participant_joined(transport):
             # Note that we do not put an EndFrame() item in the pipeline for this demo.
             # This means that the bot will stay in the channel until it times out.
-            # An EndFrame() in the pipeline would cause the transport to shut down.
+            # An EndFrame() in the pipeline would cause the transport to shut
+            # down.
             await pipeline.queue_frames(
                 [TextFrame("a cat in the style of picasso")]
             )

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

@@ -39,9 +39,8 @@ async def main():
         )
         image_task = asyncio.create_task(
             imagegen.run_to_queue(
-                transport.send_queue, [TextFrame("a cat in the style of picasso")]
+                transport.send_queue, [
-            )
+                    TextFrame("a cat in the style of picasso")]))
-        )
 
         async def run_tk():
             while not transport._stop_threads.is_set():

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

@@ -49,7 +49,8 @@ async def main(room_url: str):
             voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
         )
 
-        messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
+        messages = [{"role": "system",
+                     "content": "tell the user a joke about llamas"}]
 
         # Start a task to run the LLM to create a joke, and convert the LLM output to audio frames. This task
         # will run in parallel with generating and speaking the audio for static text, so there's no delay to
@@ -65,7 +66,8 @@ async def main(room_url: str):
             ]
         )
 
-        merge_pipeline = SequentialMergePipeline([simple_tts_pipeline, llm_pipeline])
+        merge_pipeline = SequentialMergePipeline(
+            [simple_tts_pipeline, llm_pipeline])
 
         await asyncio.gather(
             transport.run(merge_pipeline),

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

@@ -79,8 +79,8 @@ async def main(room_url):
         )
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
 
         imagegen = FalImageGenService(
             image_size="square_hd",
@@ -90,10 +90,9 @@ async def main(room_url):
         )
 
         gated_aggregator = GatedAggregator(
-            gate_open_fn=lambda frame: isinstance(frame, ImageFrame),
+            gate_open_fn=lambda frame: isinstance(
-            gate_close_fn=lambda frame: isinstance(frame, LLMResponseStartFrame),
+                frame, ImageFrame), gate_close_fn=lambda frame: isinstance(
-            start_open=False,
+                frame, LLMResponseStartFrame), start_open=False, )
-        )
 
         sentence_aggregator = SentenceAggregator()
         month_prepender = MonthPrepender()

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

@@ -38,8 +38,8 @@ async def main(room_url):
         )
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
 
         dalle = FalImageGenService(
             image_size="1024x1024",
@@ -49,7 +49,8 @@ async def main(room_url):
         )
 
         # 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.
+        # coroutine lets us ensure proper ordering of the audio chunks on the
+        # send queue.
         async def get_all_audio(text):
             all_audio = bytearray()
             async for audio in tts.run_tts(text):
@@ -71,7 +72,8 @@ 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))
+            image_task = asyncio.create_task(
+                dalle.run_image_gen(image_description))
             (audio, image_data) = await asyncio.gather(audio_task, image_task)
 
             return {
@@ -100,7 +102,8 @@ async def main(room_url):
         async def show_images():
             # 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.
+            # 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
                 if data:
@@ -122,7 +125,9 @@ async def main(room_url):
                 tk_root.update_idletasks()
                 await asyncio.sleep(0.1)
 
-        month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
+        month_tasks = [
+            asyncio.create_task(
+                get_month_data(month)) for month in months]
 
         await asyncio.gather(transport.run(), show_images(), run_tk())
 
@@ -130,8 +135,11 @@ async def main(room_url):
 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"
+        "-u",
-    )
+        "--url",
+        type=str,
+        required=True,
+        help="URL of the Daily room to join")
 
     args, unknown = parser.parse_known_args()
 

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

@@ -41,8 +41,8 @@ async def main(room_url: str, token):
         )
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
         fl = FrameLogger("Inner")
         fl2 = FrameLogger("Outer")
         messages = [
@@ -52,7 +52,8 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+        tma_in = LLMUserContextAggregator(
+            messages, transport._my_participant_id)
         tma_out = LLMAssistantContextAggregator(
             messages, transport._my_participant_id
         )
@@ -70,7 +71,8 @@ async def main(room_url: str, token):
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
             # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            messages.append(
+                {"role": "system", "content": "Please introduce yourself to the user."})
             await pipeline.queue_frames([LLMMessagesQueueFrame(messages)])
 
         transport.transcription_settings["extra"]["endpointing"] = True

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

@@ -61,8 +61,8 @@ async def main(room_url: str, token):
         )
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
 
         img = FalImageGenService(
             image_size="1024x1024",
@@ -97,14 +97,15 @@ async def main(room_url: str, token):
                 },
             ]
 
-            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+            tma_in = LLMUserContextAggregator(
+                messages, transport._my_participant_id)
             tma_out = LLMAssistantContextAggregator(
                 messages, transport._my_participant_id
             )
             image_sync_aggregator = ImageSyncAggregator(
-                os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
+                os.path.join(
-                os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
+                    os.path.dirname(__file__), "assets", "speaking.png"), os.path.join(
-            )
+                    os.path.dirname(__file__), "assets", "waiting.png"), )
             await tts.run_to_queue(
                 transport.send_queue,
                 image_sync_aggregator.run(

src/examples/foundational/07-interruptible.py

@@ -42,8 +42,8 @@ async def main(room_url: str, token):
         )
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
 
         pipeline = Pipeline([FrameLogger(), llm, FrameLogger(), tts])
 

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

@@ -56,7 +56,8 @@ talking_list = [sprites["sc-default.png"], sprites["sc-talk.png"]]
 talking = [random.choice(talking_list) for x in range(30)]
 talking_frame = SpriteFrame(images=talking)
 
-# TODO: Support "thinking" as soon as we get a valid transcript, while LLM is processing
+# TODO: Support "thinking" as soon as we get a valid transcript, while LLM
+# is processing
 thinking_list = [
     sprites["sc-think-1.png"],
     sprites["sc-think-2.png"],
@@ -130,8 +131,8 @@ async def main(room_url: str, token):
         transport._camera_height = 1280
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
 
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
@@ -155,7 +156,8 @@ async def main(room_url: str, token):
                 },
             ]
 
-            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+            tma_in = LLMUserContextAggregator(
+                messages, transport._my_participant_id)
             tma_out = LLMAssistantContextAggregator(
                 messages, transport._my_participant_id
             )

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

@@ -81,8 +81,8 @@ async def main(room_url: str, token):
         )
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
 
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
@@ -103,7 +103,8 @@ async def main(room_url: str, token):
                 },
             ]
 
-            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+            tma_in = LLMUserContextAggregator(
+                messages, transport._my_participant_id)
             tma_out = LLMAssistantContextAggregator(
                 messages, transport._my_participant_id
             )

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

@@ -57,8 +57,11 @@ async def main(room_url: str):
 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"
+        "-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/examples/image-gen.py

@@ -45,14 +45,17 @@ async def main(room_url: str, token):
             print(f"finder: {finder}")
             if finder >= 0:
                 async for audio in tts.run_tts(f"Resetting."):
-                    transport.output_queue.put(Frame(FrameType.AUDIO_FRAME, audio))
+                    transport.output_queue.put(
+                        Frame(FrameType.AUDIO_FRAME, audio))
                 sentence = ""
                 continue
-            # todo: we could differentiate between transcriptions from different participants
+            # todo: we could differentiate between transcriptions from
+            # different participants
             sentence += f" {message['text']}"
             print(f"sentence is now: {sentence}")
             # TODO: Cache this audio
-            phrase = random.choice(["OK.", "Got it.", "Sure.", "You bet.", "Sure thing."])
+            phrase = random.choice(
+                ["OK.", "Got it.", "Sure.", "You bet.", "Sure thing."])
             async for audio in tts.run_tts(phrase):
                 transport.output_queue.put(Frame(FrameType.AUDIO_FRAME, audio))
             img_result = img.run_image_gen(sentence, "1024x1024")
@@ -82,8 +85,11 @@ async def main(room_url: str, token):
 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"
+        "-u",
-    )
+        "--url",
+        type=str,
+        required=True,
+        help="URL of the Daily room to join")
     parser.add_argument(
         "-k",
         "--apikey",
@@ -94,20 +100,25 @@ if __name__ == "__main__":
 
     args, unknown = parser.parse_known_args()
 
-    # Create a meeting token for the given room with an expiration 1 hour in the future.
+    # 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}"},
+        headers={
+            "Authorization": f"Bearer {args.apikey}"},
         json={
-            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
+            "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}")
+        raise Exception(
+            f"Failed to create meeting token: {res.status_code} {res.text}")
 
     token: str = res.json()["token"]
 

src/examples/server/auth.py

@@ -24,7 +24,8 @@ def get_meeting_token(room_name, daily_api_key, token_expiry):
                 'is_owner': True,
                 'exp': token_expiry}})
     if res.status_code != 200:
-        return jsonify({'error': 'Unable to create meeting token', 'detail': res.text}), 500
+        return jsonify(
+            {'error': 'Unable to create meeting token', 'detail': res.text}), 500
     meeting_token = res.json()['token']
     return meeting_token
 

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

@@ -14,14 +14,16 @@ load_dotenv()
 app = Flask(__name__)
 CORS(app)
 
-print(f"I loaded an environment, and my FAL_KEY_ID is {os.getenv('FAL_KEY_ID')}")
+print(
+    f"I loaded an environment, and my FAL_KEY_ID is {os.getenv('FAL_KEY_ID')}")
 
 
 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("DAILY_ROOM_TIMEOUT") or os.getenv("DAILY_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",
@@ -59,14 +61,13 @@ def start_bot(bot_path, args=None):
         extra_args = ""
 
     proc = subprocess.Popen(
-        [
+        [f"python {bot_path} -u {room_url} -t {meeting_token} -k {daily_api_key} {extra_args}"],
-            f"python {bot_path} -u {room_url} -t {meeting_token} -k {daily_api_key} {extra_args}"
-        ],
         shell=True,
         bufsize=1,
     )
 
-    # Don't return until the bot has joined the room, but wait for at most 2 seconds.
+    # Don't return until the bot has joined the room, but wait for at most 2
+    # seconds.
     attempts = 0
     while attempts < 20:
         time.sleep(0.1)
@@ -82,11 +83,13 @@ 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("DAILY_CLIENT_VAD_TIMEOUT_SEC"))
+        config['vad_timeout_sec'] = float(
+            os.getenv("DAILY_CLIENT_VAD_TIMEOUT_SEC"))
     else:
         config['vad_timeout_sec'] = 1.5
 
-    # return jsonify({"room_url": room_url, "token": meeting_token, "config": config}), 200
+    # return jsonify({"room_url": room_url, "token": meeting_token, "config":
+    # config}), 200
     return redirect(room_url, code=301)
 
 

src/examples/starter-apps/chatbot.py

@@ -109,8 +109,8 @@ async def main(room_url: str, token):
         )
 
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
 
         ta = TalkingAnimation()
         ai = AnimationInitializer()

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

@@ -61,161 +61,101 @@ for file in sound_files:
         sounds[file] = audio_file.readframes(-1)
 
 
-steps = [
+steps = [{"prompt": "Start by introducing yourself. Then, ask the user to confirm their identity by telling you their birthday, including the year. When they answer with their birthday, call the verify_birthday function.",
-    {
+          "run_async": False,
-        "prompt": "Start by introducing yourself. Then, ask the user to confirm their identity by telling you their birthday, including the year. When they answer with their birthday, call the verify_birthday function.",
+          "failed": "The user provided an incorrect birthday. Ask them for their birthday again. When they answer, call the verify_birthday function.",
-        "run_async": False,
+          "tools": [{"type": "function",
-        "failed": "The user provided an incorrect birthday. Ask them for their birthday again. When they answer, call the verify_birthday function.",
+                     "function": {"name": "verify_birthday",
-        "tools": [
+                                  "description": "Use this function to verify the user has provided their correct birthday.",
-            {
+                                  "parameters": {"type": "object",
-                "type": "function",
+                                                 "properties": {"birthday": {"type": "string",
-                "function": {
+                                                                             "description": "The user's birthdate, including the year. The user can provide it in any format, but convert it to YYYY-MM-DD format to call this function.",
-                    "name": "verify_birthday",
+                                                                             }},
-                    "description": "Use this function to verify the user has provided their correct birthday.",
+                                                 },
-                    "parameters": {
+                                  },
-                        "type": "object",
+                     }],
-                        "properties": {
+          },
-                            "birthday": {
+         {"prompt": "Next, thank the user for confirming their identity, then ask the user to list their current prescriptions. Each prescription needs to have a medication name and a dosage. Do not call the list_prescriptions function with any unknown dosages.",
-                                "type": "string",
+          "run_async": True,
-                                "description": "The user's birthdate, including the year. The user can provide it in any format, but convert it to YYYY-MM-DD format to call this function.",
+          "tools": [{"type": "function",
-                            }
+                     "function": {"name": "list_prescriptions",
-                        },
+                                  "description": "Once the user has provided a list of their prescription medications, call this function.",
-                    },
+                                  "parameters": {"type": "object",
-                },
+                                                 "properties": {"prescriptions": {"type": "array",
-            }
+                                                                                  "items": {"type": "object",
-        ],
+                                                                                            "properties": {"medication": {"type": "string",
-    },
+                                                                                                                          "description": "The medication's name",
-    {
+                                                                                                                          },
-        "prompt": "Next, thank the user for confirming their identity, then ask the user to list their current prescriptions. Each prescription needs to have a medication name and a dosage. Do not call the list_prescriptions function with any unknown dosages.",
+                                                                                                           "dosage": {"type": "string",
-        "run_async": True,
+                                                                                                                      "description": "The prescription's dosage",
-        "tools": [
+                                                                                                                      },
-            {
+                                                                                                           },
-                "type": "function",
+                                                                                            },
-                "function": {
+                                                                                  }},
-                    "name": "list_prescriptions",
+                                                 },
-                    "description": "Once the user has provided a list of their prescription medications, call this function.",
+                                  },
-                    "parameters": {
+                     }],
-                        "type": "object",
+          },
-                        "properties": {
+         {"prompt": "Next, ask the user if they have any allergies. Once they have listed their allergies or confirmed they don't have any, call the list_allergies function.",
-                            "prescriptions": {
+          "run_async": True,
-                                "type": "array",
+          "tools": [{"type": "function",
-                                "items": {
+                     "function": {"name": "list_allergies",
-                                    "type": "object",
+                                  "description": "Once the user has provided a list of their allergies, call this function.",
-                                    "properties": {
+                                  "parameters": {"type": "object",
-                                        "medication": {
+                                                 "properties": {"allergies": {"type": "array",
-                                            "type": "string",
+                                                                              "items": {"type": "object",
-                                            "description": "The medication's name",
+                                                                                        "properties": {"name": {"type": "string",
-                                        },
+                                                                                                                "description": "What the user is allergic to",
-                                        "dosage": {
+                                                                                                                }},
-                                            "type": "string",
+                                                                                        },
-                                            "description": "The prescription's dosage",
+                                                                              }},
-                                        },
+                                                 },
-                                    },
+                                  },
-                                },
+                     }],
-                            }
+          },
-                        },
+         {"prompt": "Now ask the user if they have any medical conditions the doctor should know about. Once they've answered the question, call the list_conditions function.",
-                    },
+          "run_async": True,
-                },
+          "tools": [{"type": "function",
-            }
+                     "function": {"name": "list_conditions",
-        ],
+                                  "description": "Once the user has provided a list of their medical conditions, call this function.",
-    },
+                                  "parameters": {"type": "object",
-    {
+                                                 "properties": {"conditions": {"type": "array",
-        "prompt": "Next, ask the user if they have any allergies. Once they have listed their allergies or confirmed they don't have any, call the list_allergies function.",
+                                                                               "items": {"type": "object",
-        "run_async": True,
+                                                                                         "properties": {"name": {"type": "string",
-        "tools": [
+                                                                                                                 "description": "The user's medical condition",
-            {
+                                                                                                                 }},
-                "type": "function",
+                                                                                         },
-                "function": {
+                                                                               }},
-                    "name": "list_allergies",
+                                                 },
-                    "description": "Once the user has provided a list of their allergies, call this function.",
+                                  },
-                    "parameters": {
+                     },
-                        "type": "object",
+                    ],
-                        "properties": {
+          },
-                            "allergies": {
+         {"prompt": "Finally, ask the user the reason for their doctor visit today. Once they answer, call the list_visit_reasons function.",
-                                "type": "array",
+          "run_async": True,
-                                "items": {
+          "tools": [{"type": "function",
-                                    "type": "object",
+                     "function": {"name": "list_visit_reasons",
-                                    "properties": {
+                                  "description": "Once the user has provided a list of the reasons they are visiting a doctor today, call this function.",
-                                        "name": {
+                                  "parameters": {"type": "object",
-                                            "type": "string",
+                                                 "properties": {"visit_reasons": {"type": "array",
-                                            "description": "What the user is allergic to",
+                                                                                  "items": {"type": "object",
-                                        }
+                                                                                            "properties": {"name": {"type": "string",
-                                    },
+                                                                                                                    "description": "The user's reason for visiting the doctor",
-                                },
+                                                                                                                    }},
-                            }
+                                                                                            },
-                        },
+                                                                                  }},
-                    },
+                                                 },
-                },
+                                  },
-            }
+                     }],
-        ],
+          },
-    },
+         {"prompt": "Now, thank the user and end the conversation.",
-    {
+          "run_async": True,
-        "prompt": "Now ask the user if they have any medical conditions the doctor should know about. Once they've answered the question, call the list_conditions function.",
+          "tools": [],
-        "run_async": True,
+          },
-        "tools": [
+         {"prompt": "",
-            {
+          "run_async": True,
-                "type": "function",
+          "tools": []},
-                "function": {
+         ]
-                    "name": "list_conditions",
-                    "description": "Once the user has provided a list of their medical conditions, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "conditions": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "name": {
-                                            "type": "string",
-                                            "description": "The user's medical condition",
-                                        }
-                                    },
-                                },
-                            }
-                        },
-                    },
-                },
-            },
-        ],
-    },
-    {
-        "prompt": "Finally, ask the user the reason for their doctor visit today. Once they answer, call the list_visit_reasons function.",
-        "run_async": True,
-        "tools": [
-            {
-                "type": "function",
-                "function": {
-                    "name": "list_visit_reasons",
-                    "description": "Once the user has provided a list of the reasons they are visiting a doctor today, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "visit_reasons": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "name": {
-                                            "type": "string",
-                                            "description": "The user's reason for visiting the doctor",
-                                        }
-                                    },
-                                },
-                            }
-                        },
-                    },
-                },
-            }
-        ],
-    },
-    {
-        "prompt": "Now, thank the user and end the conversation.",
-        "run_async": True,
-        "tools": [],
-    },
-    {"prompt": "", "run_async": True, "tools": []},
-]
 current_step = 0
 
 
@@ -299,10 +239,12 @@ class ChecklistProcessor(AIService):
         elif isinstance(frame, LLMFunctionCallFrame):
 
             if frame.function_name and frame.arguments:
-                print(f"--> Calling function: {frame.function_name} with arguments:")
+                print(
+                    f"--> Calling function: {frame.function_name} with arguments:")
                 pretty_json = re.sub(
-                    "\n", "\n    ", json.dumps(json.loads(frame.arguments), indent=2)
+                    "\n", "\n    ", json.dumps(
-                )
+                        json.loads(
+                            frame.arguments), indent=2))
                 print(f"--> {pretty_json}\n")
                 if frame.function_name not in self._functions:
                     raise Exception(

src/examples/starter-apps/storybot.py

@@ -146,7 +146,8 @@ class StoryProcessor(FrameProcessor):
                     self._story.append(self._text)
                     yield StoryPageFrame(self._text)
             else:
-                # After the prompt thing, we'll catch an LLM end to get the last bit
+                # After the prompt thing, we'll catch an LLM end to get the
+                # last bit
                 pass
         elif isinstance(frame, LLMResponseEndFrame):
             yield ImageFrame(None, images["grandma-writing.png"])
@@ -251,7 +252,8 @@ async def main(room_url: str, token):
                 }
             ]
             lca = LLMAssistantContextAggregator(messages)
-            local_pipeline = Pipeline([llm, lca, tts], sink=transport.send_queue)
+            local_pipeline = Pipeline(
+                [llm, lca, tts], sink=transport.send_queue)
             await local_pipeline.queue_frames(
                 [
                     ImageFrame(None, images["grandma-listening.png"]),

src/examples/starter-apps/translator.py

@@ -30,7 +30,8 @@ It also isn't saving what the user or bot says into the context object for use i
 """
 
 
-# We need to use a custom service here to yield LLM frames without saving any context
+# We need to use a custom service here to yield LLM frames without saving
+# any context
 class TranslationProcessor(FrameProcessor):
     def __init__(self, language):
         self._language = language
@@ -68,8 +69,8 @@ async def main(room_url: str, token):
             voice="es-ES-AlvaroNeural",
         )
         llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-        )
+            model="gpt-4-turbo-preview")
         sa = SentenceAggregator()
         tp = TranslationProcessor("Spanish")
         pipeline = Pipeline([sa, tp, llm, tts])

src/examples/support/runner.py

@@ -11,8 +11,11 @@ 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"
+        "-u",
-    )
+        "--url",
+        type=str,
+        required=False,
+        help="URL of the Daily room to join")
     parser.add_argument(
         "-k",
         "--apikey",
@@ -33,20 +36,25 @@ def configure():
     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.
+    # 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}"},
+        headers={
+            "Authorization": f"Bearer {key}"},
         json={
-            "properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
+            "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}")
+        raise Exception(
+            f"Failed to create meeting token: {res.status_code} {res.text}")
 
     token: str = res.json()["token"]