examples: fix simple-chatbot

examples/simple-chatbot/bot.py

@@ -1,37 +1,36 @@
 import asyncio
 import aiohttp
-import logging
 import os
-from PIL import Image
-from typing import AsyncGenerator
+import sys
 
-from dailyai.pipeline.aggregators import (
-    LLMAssistantResponseAggregator,
-    LLMUserResponseAggregator,
-)
-from dailyai.pipeline.frames import (
-    ImageFrame,
+from PIL import Image
+
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator
+from pipecat.frames.frames import (
+    AudioRawFrame,
+    ImageRawFrame,
     SpriteFrame,
     Frame,
     LLMMessagesFrame,
-    AudioFrame,
-    PipelineStartedFrame,
-    TTSEndFrame,
+    TTSStoppedFrame
 )
-from dailyai.services.ai_services import AIService
-from dailyai.pipeline.pipeline import Pipeline
-from dailyai.transports.daily_transport import DailyTransport
-from dailyai.services.open_ai_services import OpenAILLMService
-from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 from runner import configure
 
+from loguru import logger
+
 from dotenv import load_dotenv
 load_dotenv(override=True)
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
 sprites = []
 
@@ -43,17 +42,17 @@ for i in range(1, 26):
     # Get the filename without the extension to use as the dictionary key
     # Open the image and convert it to bytes
     with Image.open(full_path) as img:
-        sprites.append(img.tobytes())
+        sprites.append(ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
 
 flipped = sprites[::-1]
 sprites.extend(flipped)
 
 # When the bot isn't talking, show a static image of the cat listening
-quiet_frame = ImageFrame(sprites[0], (1024, 576))
+quiet_frame = sprites[0]
 talking_frame = SpriteFrame(images=sprites)
 
 
-class TalkingAnimation(AIService):
+class TalkingAnimation(FrameProcessor):
     """
     This class starts a talking animation when it receives an first AudioFrame,
     and then returns to a "quiet" sprite when it sees a LLMResponseEndFrame.
@@ -63,32 +62,16 @@ class TalkingAnimation(AIService):
         super().__init__()
         self._is_talking = False
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, AudioFrame):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        if isinstance(frame, AudioRawFrame):
             if not self._is_talking:
-                yield talking_frame
-                yield frame
+                await self.push_frame(talking_frame)
                 self._is_talking = True
-            else:
-                yield frame
-        elif isinstance(frame, TTSEndFrame):
-            yield quiet_frame
-            yield frame
+        elif isinstance(frame, TTSStoppedFrame):
+            await self.push_frame(quiet_frame)
             self._is_talking = False
-        else:
-            yield frame
 
-
-class AnimationInitializer(AIService):
-    def __init__(self):
-        super().__init__()
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, PipelineStartedFrame):
-            yield quiet_frame
-            yield frame
-        else:
-            yield frame
+        await self.push_frame(frame)
 
 
 async def main(room_url: str, token):
@@ -97,14 +80,14 @@ async def main(room_url: str, token):
             room_url,
             token,
             "Chatbot",
-            duration_minutes=5,
-            start_transcription=True,
-            mic_enabled=True,
-            mic_sample_rate=16000,
-            camera_enabled=True,
-            camera_width=1024,
-            camera_height=576,
-            vad_enabled=True,
+            DailyParams(
+                audio_out_enabled=True,
+                camera_out_enabled=True,
+                camera_out_width=1024,
+                camera_out_height=576,
+                transcription_enabled=True,
+                vad_enabled=True
+            )
         )
 
         tts = ElevenLabsTTSService(
@@ -117,9 +100,6 @@ async def main(room_url: str, token):
             api_key=os.getenv("OPENAI_API_KEY"),
             model="gpt-4-turbo-preview")
 
-        ta = TalkingAnimation()
-        ai = AnimationInitializer()
-        pipeline = Pipeline([ai, llm, tts, ta])
         messages = [
             {
                 "role": "system",
@@ -127,22 +107,23 @@ async def main(room_url: str, token):
             },
         ]
 
-        @transport.event_handler("on_first_other_participant_joined")
-        async def on_first_other_participant_joined(transport, participant):
-            print(f"!!! in here, pipeline.source is {pipeline.source}")
-            await pipeline.queue_frames([LLMMessagesFrame(messages)])
+        user_response = LLMUserResponseAggregator()
 
-        async def run_conversation():
+        ta = TalkingAnimation()
 
-            await transport.run_interruptible_pipeline(
-                pipeline,
-                post_processor=LLMAssistantResponseAggregator(messages),
-                pre_processor=LLMUserResponseAggregator(messages),
-            )
+        pipeline = Pipeline([transport.input(), user_response, llm, tts, ta, transport.output()])
 
-        transport.transcription_settings["extra"]["endpointing"] = True
-        transport.transcription_settings["extra"]["punctuate"] = True
-        await asyncio.gather(transport.run(), run_conversation())
+        task = PipelineTask(pipeline)
+        await task.queue_frame(quiet_frame)
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":