Created an example for testing the bot and try to create freezing conditions.
This commit is contained in:
Filipi Fuchter
312
examples/freeze-test/freeze_test_bot.py
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312
examples/freeze-test/freeze_test_bot.py
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#
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# Copyright (c) 2024–2025, 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 argparse
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import asyncio
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import os
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import random
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from contextlib import asynccontextmanager
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from typing import Any, Dict
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import uvicorn
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from dotenv import load_dotenv
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from fastapi import FastAPI, Request, WebSocket
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import RedirectResponse
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from loguru import logger
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from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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CancelFrame,
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EndFrame,
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Frame,
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InterimTranscriptionFrame,
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LLMFullResponseEndFrame,
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LLMTextFrame,
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StartFrame,
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StartInterruptionFrame,
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StopFrame,
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StopInterruptionFrame,
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TranscriptionFrame,
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TTSTextFrame,
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UserStartedSpeakingFrame,
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UserStoppedSpeakingFrame,
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)
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from pipecat.observers.loggers.debug_log_observer import DebugLogObserver
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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 PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import (
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OpenAILLMContext,
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OpenAILLMContextFrame,
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor
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from pipecat.serializers.protobuf import ProtobufFrameSerializer
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.network.fastapi_websocket import (
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FastAPIWebsocketParams,
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FastAPIWebsocketTransport,
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)
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from pipecat.utils.time import time_now_iso8601
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load_dotenv(override=True)
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""Handles FastAPI startup and shutdown."""
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yield # Run app
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# Initialize FastAPI app with lifespan manager
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app = FastAPI(lifespan=lifespan)
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# Configure CORS to allow requests from any origin
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Mount the frontend at /
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app.mount("/client", SmallWebRTCPrebuiltUI)
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class SimulateFreezeInput(FrameProcessor):
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def __init__(
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self,
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**kwargs,
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):
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super().__init__(**kwargs)
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# Whether we have seen a StartFrame already.
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self._initialized = False
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self._send_frames_task = None
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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, StartFrame):
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# Push StartFrame before start(), because we want StartFrame to be
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# processed by every processor before any other frame is processed.
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await self.push_frame(frame, direction)
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await self._start(frame)
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elif isinstance(frame, CancelFrame):
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logger.info("SimulateFreezeInput: Received cancel frame")
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await self._stop()
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await self.push_frame(frame, direction)
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elif isinstance(frame, EndFrame):
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logger.info("SimulateFreezeInput: Received end frame")
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await self.push_frame(frame, direction)
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await self._stop()
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elif isinstance(frame, StopFrame):
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logger.info("SimulateFreezeInput: Received stop frame")
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await self.push_frame(frame, direction)
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await self._stop()
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async def _start(self, frame: StartFrame):
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if self._initialized:
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return
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logger.info(f"Starting SimulateFreezeInput")
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self._initialized = True
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if not self._send_frames_task:
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self._send_frames_task = self.create_task(self._send_frames())
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async def _stop(self):
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logger.info(f"Stopping SimulateFreezeInput")
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self._initialized = False
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if self._send_frames_task:
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await self.cancel_task(self._send_frames_task)
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self._send_frames_task = None
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async def _send_user_text(self, text: str):
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# Emulation as if the user has spoken and the stt transcribed
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await self.push_frame(UserStartedSpeakingFrame())
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await self.push_frame(StartInterruptionFrame())
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await self.push_frame(
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TranscriptionFrame(
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text,
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"",
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time_now_iso8601(),
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)
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)
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# Need to wait before sending the UserStoppedSpeakingFrame,
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# otherwise TranscriptionFrame will be processed
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# later than the UserStoppedSpeakingFrame
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await asyncio.sleep(0.1)
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await self.push_frame(UserStoppedSpeakingFrame())
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await self.push_frame(StopInterruptionFrame())
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async def _send_frames(self):
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try:
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i = 0
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while True:
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logger.debug("SimulateFreezeInput _send_frames")
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await self._send_user_text("Tell me a brief history of Brazil!")
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await asyncio.sleep(3)
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await self._send_user_text("")
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break
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# i += 1
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# if i >= 5:
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# break
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# sleeping 1s before interrupting
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# wait_time = random.uniform(1, 10)
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# await asyncio.sleep(wait_time)
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except Exception as e:
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logger.error(f"{self} exception receiving data: {e.__class__.__name__} ({e})")
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async def run_example(websocket_client):
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logger.info(f"Starting bot")
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# Create a transport using the WebRTC connection
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transport = FastAPIWebsocketTransport(
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websocket=websocket_client,
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params=FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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add_wav_header=False,
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vad_analyzer=SileroVADAnalyzer(),
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serializer=ProtobufFrameSerializer(),
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),
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)
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freeze = SimulateFreezeInput()
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. 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.",
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},
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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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ParallelPipeline(
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[
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freeze,
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],
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[
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transport.input(),
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stt,
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],
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),
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rtvi,
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context_aggregator.user(), # User responses
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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
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]
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)
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task = PipelineTask(
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pipeline,
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params=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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report_only_initial_ttfb=True,
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),
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idle_timeout_secs=120,
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observers=[
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DebugLogObserver(
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frame_types={
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InterimTranscriptionFrame: None,
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TranscriptionFrame: None,
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# TTSTextFrame: None,
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# LLMTextFrame: None,
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OpenAILLMContextFrame: None,
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LLMFullResponseEndFrame: None,
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},
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exclude_fields={
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"result",
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"metadata",
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"audio",
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"image",
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"images",
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},
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),
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],
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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@rtvi.event_handler("on_client_ready")
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async def on_client_ready(rtvi):
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logger.info(f"Client ready")
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await rtvi.set_bot_ready()
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# Kick off the conversation.
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# messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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# await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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@app.get("/", include_in_schema=False)
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async def root_redirect():
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return RedirectResponse(url="/client/")
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@app.websocket("/ws")
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async def websocket_endpoint(websocket: WebSocket):
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await websocket.accept()
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print("WebSocket connection accepted")
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try:
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await run_example(websocket)
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except Exception as e:
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print(f"Exception in run_bot: {e}")
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@app.post("/connect")
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async def bot_connect(request: Request) -> Dict[Any, Any]:
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server_mode = os.getenv("WEBSOCKET_SERVER", "fast_api")
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if server_mode == "websocket_server":
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ws_url = "ws://localhost:8765"
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else:
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ws_url = "ws://localhost:7860/ws"
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return {"ws_url": ws_url}
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
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parser.add_argument(
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"--host", default="localhost", help="Host for HTTP server (default: localhost)"
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)
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parser.add_argument(
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"--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
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)
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args = parser.parse_args()
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uvicorn.run(app, host=args.host, port=args.port)
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