195 lines
6.5 KiB
Python
195 lines
6.5 KiB
Python
#
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# Copyright (c) 2024, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import base64
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import io
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import os
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import sys
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from collections import deque
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from PIL import Image
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import Frame, ImageRawFrame, TranscriptionFrame
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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 PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.anthropic import AnthropicLLMContext, AnthropicLLMService
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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MAX_FRAMES = 5 # Constant to control number of frames to keep
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video_participant_id = None
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anthropic_context = None
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recent_image_frames = deque(maxlen=MAX_FRAMES)
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class ImageFrameCatcher(FrameProcessor):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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global recent_image_frames
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await super().process_frame(frame, direction)
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if isinstance(frame, ImageRawFrame):
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# logger.debug(f"ImageLogger: {frame}")
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recent_image_frames.append(frame)
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else:
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await self.push_frame(frame, direction)
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class TranscriptFrameCatcher(FrameProcessor):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, TranscriptionFrame):
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logger.debug(f"TranscriptLogger: {frame}")
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async def main():
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global llm
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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model="claude-3-5-sonnet-20240620",
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enable_prompt_caching_beta=True,
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)
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# todo: test with very short initial user message
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system_prompt = """\
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You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions. Keep
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your answers brief unless explicitly asked for more information.
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Your response will be turned into speech so use only simple words and punctuation.
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"""
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messages = [
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{
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"role": "system",
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"content": [
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{
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"type": "text",
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"text": system_prompt,
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}
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],
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},
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{"role": "user", "content": "Start the conversation by saying 'hello'."},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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ImageFrameCatcher(),
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context_aggregator.user(), # User speech to text
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses and tool context
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
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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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global video_participant_id
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video_participant_id = participant["id"]
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await transport.capture_participant_transcription(video_participant_id)
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await transport.capture_participant_video(
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video_participant_id, framerate=1, video_source="screenVideo"
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)
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# Kick off the conversation.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_app_message")
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async def on_app_message(transport, message, sender):
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global anthropic_context
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anthropic_context = AnthropicLLMContext.upgrade_to_anthropic(context)
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logger.debug(f"Received app message: {message} - {context}")
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if not recent_image_frames:
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logger.debug("No image frames to send")
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return
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add_message_with_images(
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anthropic_context, message["message"], frames=list(recent_image_frames)
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)
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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runner = PipelineRunner()
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await runner.run(task)
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def add_message_with_images(c, message, frames=None):
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if frames is None:
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frames = list(recent_image_frames)
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if not frames:
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logger.debug("No image frames to send")
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return
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# Create content list starting with all images
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content = []
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for frame in frames:
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buffer = io.BytesIO()
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Image.frombytes(frame.format, frame.size, frame.image).save(buffer, format="JPEG")
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encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
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content.append(
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": "image/jpeg",
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"data": encoded_image,
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},
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}
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)
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# Add text message at the end if provided
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if message:
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content.append({"type": "text", "text": message})
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c.add_message({"role": "user", "content": content})
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if __name__ == "__main__":
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asyncio.run(main())
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