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import asyncio
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import aiohttp
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import logging
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import os
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import sys
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from PIL import Image
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from typing import AsyncGenerator
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from dailyai.pipeline.aggregators import (
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LLMUserResponseAggregator,
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ParallelPipeline,
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VisionImageFrameAggregator,
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SentenceAggregator
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)
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from dailyai.pipeline.frames import (
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ImageFrame,
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from pipecat.frames.frames import (
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ImageRawFrame,
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SpriteFrame,
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Frame,
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LLMMessagesFrame,
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AudioFrame,
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PipelineStartedFrame,
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TTSEndFrame,
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AudioRawFrame,
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TTSStoppedFrame,
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TextFrame,
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UserImageFrame,
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UserImageRawFrame,
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UserImageRequestFrame,
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)
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from dailyai.services.moondream_ai_service import MoondreamService
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from dailyai.pipeline.pipeline import FrameProcessor, Pipeline
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from dailyai.transports.daily_transport import DailyTransport
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from dailyai.services.open_ai_services import OpenAILLMService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineTask
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from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator
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from pipecat.processors.aggregators.sentence import SentenceAggregator
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from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.moondream import MoondreamService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.vad.silero import SileroVAD
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
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logger = logging.getLogger("dailyai")
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logger.setLevel(logging.DEBUG)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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user_request_answer = "Let me take a look."
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@@ -51,13 +54,13 @@ for i in range(1, 26):
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# Get the filename without the extension to use as the dictionary key
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# Open the image and convert it to bytes
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with Image.open(full_path) as img:
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sprites.append(img.tobytes())
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sprites.append(ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
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flipped = sprites[::-1]
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sprites.extend(flipped)
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# When the bot isn't talking, show a static image of the cat listening
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quiet_frame = ImageFrame(sprites[0], (1024, 576))
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quiet_frame = sprites[0]
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talking_frame = SpriteFrame(images=sprites)
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@@ -71,37 +74,18 @@ class TalkingAnimation(FrameProcessor):
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super().__init__()
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self._is_talking = False
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, AudioFrame):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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if isinstance(frame, AudioRawFrame):
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if not self._is_talking:
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yield talking_frame
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yield frame
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await self.push_frame(talking_frame)
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self._is_talking = True
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else:
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yield frame
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elif isinstance(frame, TTSEndFrame):
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yield quiet_frame
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yield frame
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elif isinstance(frame, TTSStoppedFrame):
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await self.push_frame(quiet_frame)
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self._is_talking = False
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else:
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yield frame
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class AnimationInitializer(FrameProcessor):
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def __init__(self):
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super().__init__()
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, PipelineStartedFrame):
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yield quiet_frame
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yield frame
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else:
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yield frame
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await self.push_frame(frame)
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class UserImageRequester(FrameProcessor):
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participant_id: str | None
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def __init__(self):
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super().__init__()
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self.participant_id = None
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@@ -109,33 +93,32 @@ class UserImageRequester(FrameProcessor):
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def set_participant_id(self, participant_id: str):
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self.participant_id = participant_id
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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if self.participant_id and isinstance(frame, TextFrame):
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if frame.text == user_request_answer:
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yield UserImageRequestFrame(self.participant_id)
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yield TextFrame("Describe the image in a short sentence.")
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elif isinstance(frame, UserImageFrame):
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yield frame
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await self.push_frame(UserImageRequestFrame(self.participant_id), FrameDirection.UPSTREAM)
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await self.push_frame(TextFrame("Describe the image in a short sentence."))
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elif isinstance(frame, UserImageRawFrame):
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await self.push_frame(frame)
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class TextFilterProcessor(FrameProcessor):
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text: str
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def __init__(self, text: str):
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super().__init__()
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self.text = text
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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if isinstance(frame, TextFrame):
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if frame.text != self.text:
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yield frame
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await self.push_frame(frame)
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else:
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yield frame
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await self.push_frame(frame)
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class ImageFilterProcessor(FrameProcessor):
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if not isinstance(frame, ImageFrame):
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yield frame
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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if not isinstance(frame, ImageRawFrame):
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await self.push_frame(frame)
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async def main(room_url: str, token):
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@@ -144,17 +127,18 @@ async def main(room_url: str, token):
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room_url,
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token,
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"Chatbot",
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duration_minutes=5,
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start_transcription=True,
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mic_enabled=True,
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mic_sample_rate=16000,
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camera_enabled=True,
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camera_width=1024,
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camera_height=576,
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vad_enabled=True,
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video_rendering_enabled=True
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DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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camera_out_enabled=True,
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camera_out_width=1024,
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camera_out_height=576,
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transcription_enabled=True
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)
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)
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vad = SileroVAD()
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tts = ElevenLabsTTSService(
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aiohttp_session=session,
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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@@ -166,11 +150,11 @@ async def main(room_url: str, token):
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model="gpt-4-turbo-preview")
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ta = TalkingAnimation()
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ai = AnimationInitializer()
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sa = SentenceAggregator()
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ir = UserImageRequester()
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va = VisionImageFrameAggregator()
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# If you run into weird description, try with use_cpu=True
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moondream = MoondreamService()
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@@ -186,23 +170,25 @@ async def main(room_url: str, token):
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ura = LLMUserResponseAggregator(messages)
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pipeline = Pipeline([
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ai, ura, llm, ParallelPipeline(
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[[sa, ir, va, moondream], [tf, imgf]]
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),
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tts, ta
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])
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pipeline = Pipeline([transport.input(), vad, ura, llm,
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ParallelPipeline(
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[sa, ir, va, moondream],
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[tf, imgf]),
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tts, ta, transport.output()])
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@transport.event_handler("on_first_other_participant_joined")
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async def on_first_other_participant_joined(transport, participant):
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transport.render_participant_video(participant["id"], framerate=0)
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task = PipelineTask(pipeline)
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await task.queue_frame(quiet_frame)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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transport.capture_participant_transcription(participant["id"])
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transport.capture_participant_video(participant["id"], framerate=0)
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ir.set_participant_id(participant["id"])
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await pipeline.queue_frames([LLMMessagesFrame(messages)])
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await task.queue_frames([LLMMessagesFrame(messages)])
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transport.transcription_settings["extra"]["endpointing"] = True
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transport.transcription_settings["extra"]["punctuate"] = True
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runner = PipelineRunner()
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await asyncio.gather(transport.run(pipeline))
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await runner.run(task)
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if __name__ == "__main__":
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