# # Copyright (c) 2024–2025, Daily # # SPDX-License-Identifier: BSD 2-Clause License # import os from dotenv import load_dotenv from loguru import logger from PIL import Image from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.audio.vad.vad_analyzer import VADParams from pipecat.frames.frames import UserImageRawFrame from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.runner.types import RunnerArguments from pipecat.runner.utils import create_transport from pipecat.services.cartesia.tts import CartesiaTTSService from pipecat.services.moondream.vision import MoondreamService from pipecat.transports.base_transport import BaseTransport, TransportParams from pipecat.transports.daily.transport import DailyParams load_dotenv(override=True) # We store functions so objects (e.g. SileroVADAnalyzer) don't get # instantiated. The function will be called when the desired transport gets # selected. transport_params = { "daily": lambda: DailyParams( audio_out_enabled=True, vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)), ), "webrtc": lambda: TransportParams( audio_out_enabled=True, vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)), ), } async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): logger.info(f"Starting bot") tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady ) vision = MoondreamService() pipeline = Pipeline( [ vision, # Vision tts, # TTS transport.output(), # Transport bot output ] ) task = PipelineTask( pipeline, params=PipelineParams( enable_metrics=True, enable_usage_metrics=True, ), idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, ) @transport.event_handler("on_client_connected") async def on_client_connected(transport, client): logger.info(f"Client connected") if not runner_args.body: script_dir = os.path.dirname(__file__) runner_args.body = { "image_path": os.path.join(script_dir, "assets", "cat.jpg"), "question": "Describe this image", } image_path = runner_args.body["image_path"] question = runner_args.body["question"] # Describe the image. image = Image.open(image_path) await task.queue_frames( [ UserImageRawFrame( image=image.tobytes(), format="RGB", size=image.size, text=question, ) ] ) @transport.event_handler("on_client_disconnected") async def on_client_disconnected(transport, client): logger.info(f"Client disconnected") await task.cancel() runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) await runner.run(task) async def bot(runner_args: RunnerArguments): """Main bot entry point compatible with Pipecat Cloud.""" transport = await create_transport(runner_args, transport_params) await run_bot(transport, runner_args) if __name__ == "__main__": from pipecat.runner.run import main main()