257 lines
8.3 KiB
Python
257 lines
8.3 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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"""OpenAI Bot Implementation.
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This module implements a chatbot using OpenAI's GPT-4 model for natural language
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processing. It includes:
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- Real-time audio/video interaction through Daily
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- Animated robot avatar
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- Text-to-speech using ElevenLabs
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- Support for both English and Spanish
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The bot runs as part of a pipeline that processes audio/video frames and manages
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the conversation flow.
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"""
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import asyncio
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import os
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import sys
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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 (
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BotStartedSpeakingFrame,
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BotStoppedSpeakingFrame,
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EndFrame,
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Frame,
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LLMMessagesFrame,
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OutputImageRawFrame,
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SpriteFrame,
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)
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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.processors.frameworks.rtvi import (
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RTVIBotTranscriptionProcessor,
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RTVIConfig,
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RTVIMetricsProcessor,
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RTVIProcessor,
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RTVISpeakingProcessor,
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RTVIUserTranscriptionProcessor,
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)
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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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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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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sprites = []
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script_dir = os.path.dirname(__file__)
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# Load sequential animation frames
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for i in range(1, 26):
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# Build the full path to the image file
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full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
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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(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
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# Create a smooth animation by adding reversed frames
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flipped = sprites[::-1]
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sprites.extend(flipped)
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# Define static and animated states
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quiet_frame = sprites[0] # Static frame for when bot is listening
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talking_frame = SpriteFrame(images=sprites) # Animation sequence for when bot is talking
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class TalkingAnimation(FrameProcessor):
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"""Manages the bot's visual animation states.
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Switches between static (listening) and animated (talking) states based on
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the bot's current speaking status.
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"""
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def __init__(self):
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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, direction: FrameDirection):
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"""Process incoming frames and update animation state.
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Args:
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frame: The incoming frame to process
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direction: The direction of frame flow in the pipeline
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"""
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await super().process_frame(frame, direction)
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# Switch to talking animation when bot starts speaking
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if isinstance(frame, BotStartedSpeakingFrame):
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if not self._is_talking:
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await self.push_frame(talking_frame)
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self._is_talking = True
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# Return to static frame when bot stops speaking
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elif isinstance(frame, BotStoppedSpeakingFrame):
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await self.push_frame(quiet_frame)
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self._is_talking = False
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await self.push_frame(frame, direction)
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async def main():
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"""Main bot execution function.
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Sets up and runs the bot pipeline including:
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- Daily video transport
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- Speech-to-text and text-to-speech services
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- Language model integration
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- Animation processing
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- RTVI event handling
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"""
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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# Set up Daily transport with video/audio parameters
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transport = DailyTransport(
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room_url,
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token,
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"Chatbot",
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DailyParams(
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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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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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transcription_enabled=True,
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#
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# Spanish
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#
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# transcription_settings=DailyTranscriptionSettings(
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# language="es",
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# tier="nova",
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# model="2-general"
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# )
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),
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)
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# Initialize text-to-speech service
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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#
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# English
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#
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voice_id="pNInz6obpgDQGcFmaJgB",
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#
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# Spanish
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#
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# model="eleven_multilingual_v2",
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# voice_id="gD1IexrzCvsXPHUuT0s3",
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)
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# Initialize LLM service
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = [
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{
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"role": "system",
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#
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# English
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#
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"content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself.",
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#
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# Spanish
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#
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# "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
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},
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]
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# Set up conversation context and management
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# The context_aggregator will automatically collect conversation context
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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ta = TalkingAnimation()
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#
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# RTVI events for Pipecat client UI
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#
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# This will send `user-*-speaking` and `bot-*-speaking` messages.
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rtvi_speaking = RTVISpeakingProcessor()
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# This will emit UserTranscript events.
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rtvi_user_transcription = RTVIUserTranscriptionProcessor()
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# This will emit BotTranscript events.
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rtvi_bot_transcription = RTVIBotTranscriptionProcessor()
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# This will send `metrics` messages.
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rtvi_metrics = RTVIMetricsProcessor()
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# Handles RTVI messages from the client
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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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pipeline = Pipeline(
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[
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transport.input(),
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rtvi,
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rtvi_speaking,
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rtvi_user_transcription,
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context_aggregator.user(),
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llm,
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rtvi_bot_transcription,
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tts,
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ta,
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rtvi_metrics,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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await task.queue_frame(quiet_frame)
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@rtvi.event_handler("on_client_ready")
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async def on_client_ready(rtvi):
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await rtvi.set_bot_ready()
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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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await transport.capture_participant_transcription(participant["id"])
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await task.queue_frames([LLMMessagesFrame(messages)])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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print(f"Participant left: {participant}")
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await task.queue_frame(EndFrame())
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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