go back to using @dataclass since they can be inspected

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

@@ -9,7 +9,7 @@ import aiohttp
 import os
 import sys
 
-import daily
+from dataclasses import dataclass
 
 from pipecat.frames.frames import (
     AppFrame,
@@ -44,20 +44,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
+@dataclass
 class MonthFrame(AppFrame):
-    def __init__(self, month):
-        super().__init__()
-        self.metadata["month"] = month
-
-    @ property
-    def month(self) -> str:
-        return self.metadata["month"]
+    month: str
 
     def __str__(self):
         return f"{self.name}(month: {self.month})"
 
-    month: str
-
 
 class MonthPrepender(FrameProcessor):
     def __init__(self):
@@ -69,7 +62,7 @@ class MonthPrepender(FrameProcessor):
         if isinstance(frame, MonthFrame):
             self.most_recent_month = frame.month
         elif self.prepend_to_next_text_frame and isinstance(frame, TextFrame):
-            await self.push_frame(TextFrame(f"{self.most_recent_month}: {frame.data}"))
+            await self.push_frame(TextFrame(f"{self.most_recent_month}: {frame.text}"))
             self.prepend_to_next_text_frame = False
         elif isinstance(frame, LLMResponseStartFrame):
             self.prepend_to_next_text_frame = True
@@ -152,7 +145,7 @@ async def main(room_url):
                     "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
                 }
             ]
-            frames.append(MonthFrame(month))
+            frames.append(MonthFrame(month=month))
             frames.append(LLMMessagesFrame(messages))
 
         frames.append(EndFrame())

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

@@ -61,7 +61,7 @@ async def main():
 
                 async def process_frame(self, frame: Frame, direction: FrameDirection):
                     if isinstance(frame, AudioRawFrame):
-                        self.audio.extend(frame.data)
+                        self.audio.extend(frame.audio)
                         self.frame = AudioRawFrame(
                             bytes(self.audio), frame.sample_rate, frame.num_channels)
 

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

@@ -49,9 +49,9 @@ class ImageSyncAggregator(FrameProcessor):
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         if not isinstance(frame, SystemFrame):
-            await self.push_frame(ImageRawFrame(self._speaking_image_bytes, (1024, 1024), self._speaking_image_format))
+            await self.push_frame(ImageRawFrame(image=self._speaking_image_bytes, size=(1024, 1024), format=self._speaking_image_format))
             await self.push_frame(frame)
-            await self.push_frame(ImageRawFrame(self._waiting_image_bytes, (1024, 1024), self._waiting_image_format))
+            await self.push_frame(ImageRawFrame(image=self._waiting_image_bytes, size=(1024, 1024), format=self._waiting_image_format))
         else:
             await self.push_frame(frame)
 

examples/foundational/08-bots-arguing.py

@@ -92,7 +92,7 @@ async def main(room_url: str):
                 if isinstance(frame, TextFrame):
                     message += frame.text
                 elif isinstance(frame, AudioFrame):
-                    all_audio.extend(frame.data)
+                    all_audio.extend(frame.audio)
 
             return (message, all_audio)
 

examples/foundational/10-wake-word.py

@@ -63,7 +63,7 @@ for file in image_files:
     filename = os.path.splitext(os.path.basename(full_path))[0]
     # Open the image and convert it to bytes
     with Image.open(full_path) as img:
-        sprites[file] = ImageRawFrame(img.tobytes(), img.size, img.format)
+        sprites[file] = ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format)
 
 # When the bot isn't talking, show a static image of the cat listening
 quiet_frame = sprites["sc-listen-1.png"]
@@ -99,7 +99,7 @@ class NameCheckFilter(FrameProcessor):
 
         # TODO: split up transcription by participant
         if isinstance(frame, TranscriptionFrame):
-            content = frame.data
+            content = frame.text
             self._sentence += content
             if self._sentence.endswith((".", "?", "!")):
                 if any(name in self._sentence for name in self._names):