Remove Queue in frame names

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

@@ -11,10 +11,10 @@ from dailyai.services.azure_ai_services import AzureLLMService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.pipeline.aggregators import LLMUserContextAggregator, LLMAssistantContextAggregator
 from dailyai.pipeline.frames import (
-    QueueFrame,
-    TextQueueFrame,
-    ImageQueueFrame,
-    SpriteQueueFrame,
+    Frame,
+    TextFrame,
+    ImageFrame,
+    SpriteFrame,
     TranscriptionQueueFrame,
 )
 from dailyai.services.ai_services import AIService
@@ -45,11 +45,11 @@ for file in image_files:
         sprites[file] = img.tobytes()
 
 # When the bot isn't talking, show a static image of the cat listening
-quiet_frame = ImageQueueFrame("", sprites["sc-listen-1.png"])
+quiet_frame = ImageFrame("", sprites["sc-listen-1.png"])
 # When the bot is talking, build an animation from two sprites
 talking_list = [sprites['sc-default.png'], sprites['sc-talk.png']]
 talking = [random.choice(talking_list) for x in range(30)]
-talking_frame = SpriteQueueFrame(images=talking)
+talking_frame = SpriteFrame(images=talking)
 
 # TODO: Support "thinking" as soon as we get a valid transcript, while LLM is processing
 thinking_list = [
@@ -57,14 +57,14 @@ thinking_list = [
     sprites['sc-think-2.png'],
     sprites['sc-think-3.png'],
     sprites['sc-think-4.png']]
-thinking_frame = SpriteQueueFrame(images=thinking_list)
+thinking_frame = SpriteFrame(images=thinking_list)
 
 
 class TranscriptFilter(AIService):
     def __init__(self, bot_participant_id=None):
         self.bot_participant_id = bot_participant_id
 
-    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
         if isinstance(frame, TranscriptionQueueFrame):
             if frame.participantId != self.bot_participant_id:
                 yield frame
@@ -75,11 +75,11 @@ class NameCheckFilter(AIService):
         self.names = names
         self.sentence = ""
 
-    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
         content: str = ""
 
         # TODO: split up transcription by participant
-        if isinstance(frame, TextQueueFrame):
+        if isinstance(frame, TextFrame):
             content = frame.text
 
         self.sentence += content
@@ -87,7 +87,7 @@ class NameCheckFilter(AIService):
             if any(name in self.sentence for name in self.names):
                 out = self.sentence
                 self.sentence = ""
-                yield TextQueueFrame(out)
+                yield TextFrame(out)
             else:
                 out = self.sentence
                 self.sentence = ""
@@ -97,7 +97,7 @@ class ImageSyncAggregator(AIService):
     def __init__(self):
         pass
 
-    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
         yield talking_frame
         yield frame
         yield quiet_frame