From 23385ca3d2d1645d0c6e4710916c35d11e7d8112 Mon Sep 17 00:00:00 2001 From: vipyne Date: Tue, 21 Oct 2025 11:43:47 -0500 Subject: [PATCH] replace foundational example 08-bots-arguing.py with 08-custom-frame-processor.py --- examples/foundational/08-bots-arguing.py | 147 ------------------ ...cessor.py => 08-custom-frame-processor.py} | 13 +- 2 files changed, 5 insertions(+), 155 deletions(-) delete mode 100644 examples/foundational/08-bots-arguing.py rename examples/foundational/{47-custom-frame-processor.py => 08-custom-frame-processor.py} (97%) diff --git a/examples/foundational/08-bots-arguing.py b/examples/foundational/08-bots-arguing.py deleted file mode 100644 index b84e945c3..000000000 --- a/examples/foundational/08-bots-arguing.py +++ /dev/null @@ -1,147 +0,0 @@ -import asyncio -import logging -import os -from typing import Tuple - -import aiohttp -from dotenv import load_dotenv - -from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMContextFrame, TextFrame -from pipecat.pipeline.pipeline import Pipeline -from pipecat.processors.aggregators import SentenceAggregator -from pipecat.processors.aggregators.llm_context import LLMContext -from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair -from pipecat.runner.daily import configure -from pipecat.services.azure import AzureLLMService, AzureTTSService -from pipecat.services.elevenlabs import ElevenLabsTTSService -from pipecat.services.fal import FalImageGenService -from pipecat.transports.daily.transport import DailyTransport - -load_dotenv(override=True) - -logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s") -logger = logging.getLogger("pipecat") -logger.setLevel(logging.DEBUG) - - -async def main(): - async with aiohttp.ClientSession() as session: - (room_url, _) = await configure(session) - - transport = DailyTransport( - room_url, - None, - "Respond bot", - duration_minutes=10, - mic_enabled=True, - mic_sample_rate=16000, - camera_enabled=True, - camera_width=1024, - camera_height=1024, - ) - - llm = AzureLLMService( - api_key=os.getenv("AZURE_CHATGPT_API_KEY"), - endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), - model=os.getenv("AZURE_CHATGPT_MODEL"), - ) - tts1 = AzureTTSService( - api_key=os.getenv("AZURE_SPEECH_API_KEY"), - region=os.getenv("AZURE_SPEECH_REGION"), - ) - tts2 = ElevenLabsTTSService( - api_key=os.getenv("ELEVENLABS_API_KEY"), - voice_id="jBpfuIE2acCO8z3wKNLl", - ) - dalle = FalImageGenService( - params=FalImageGenService.InputParams(image_size="1024x1024"), - aiohttp_session=session, - key=os.getenv("FAL_KEY"), - ) - - bot1_messages = [ - { - "role": "system", - "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long.", - }, - ] - bot2_messages = [ - { - "role": "system", - "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich.", - }, - ] - - async def get_text_and_audio(messages) -> Tuple[str, bytearray]: - """This function streams text from the LLM and uses the TTS service to convert - that text to speech as it's received. - """ - source_queue = asyncio.Queue() - sink_queue = asyncio.Queue() - sentence_aggregator = SentenceAggregator() - pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue) - - await source_queue.put(LLMContextFrame(LLMContext(messages))) - await source_queue.put(EndFrame()) - await pipeline.run_pipeline() - - message = "" - all_audio = bytearray() - while sink_queue.qsize(): - frame = sink_queue.get_nowait() - if isinstance(frame, TextFrame): - message += frame.text - elif isinstance(frame, AudioFrame): - all_audio.extend(frame.audio) - - return (message, all_audio) - - async def get_bot1_statement(): - message, audio = await get_text_and_audio(bot1_messages) - - bot1_messages.append({"role": "assistant", "content": message}) - bot2_messages.append({"role": "user", "content": message}) - - return audio - - async def get_bot2_statement(): - message, audio = await get_text_and_audio(bot2_messages) - - bot2_messages.append({"role": "assistant", "content": message}) - bot1_messages.append({"role": "user", "content": message}) - - return audio - - async def argue(): - for i in range(100): - print(f"In iteration {i}") - - bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed" - - (audio1, image_data1) = await asyncio.gather( - get_bot1_statement(), dalle.run_image_gen(bot1_description) - ) - await transport.send_queue.put( - [ - ImageFrame(image_data1[1], image_data1[2]), - AudioFrame(audio1), - ] - ) - - bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed" - - (audio2, image_data2) = await asyncio.gather( - get_bot2_statement(), dalle.run_image_gen(bot2_description) - ) - await transport.send_queue.put( - [ - ImageFrame(image_data2[1], image_data2[2]), - AudioFrame(audio2), - ] - ) - - await asyncio.gather(transport.run(), argue()) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/foundational/47-custom-frame-processor.py b/examples/foundational/08-custom-frame-processor.py similarity index 97% rename from examples/foundational/47-custom-frame-processor.py rename to examples/foundational/08-custom-frame-processor.py index 26fe86391..20da4f876 100644 --- a/examples/foundational/47-custom-frame-processor.py +++ b/examples/foundational/08-custom-frame-processor.py @@ -57,13 +57,10 @@ def format_metrics(metrics, indent=0): class MetricsFrameLogger(FrameProcessor): - """MetricsFrameLogger logs all MetericsFrames. + """MetricsFrameLogger formats and logs all MetericsFrames""" - AND it Always pushes all frames. - """ - - def __init__(self): - super().__init__() + def __init__(self, **kwargs): + super().__init__(**kwargs) async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) @@ -110,8 +107,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) - metrics_frame_processor = MetricsFrameLogger() - messages = [ { "role": "system", @@ -122,6 +117,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): context = LLMContext(messages) context_aggregator = LLMContextAggregatorPair(context) + metrics_frame_processor = MetricsFrameLogger() + pipeline = Pipeline( [ transport.input(),