Rename example files to prepend parent folder name, preventing package shadowing
Example files like openai.py shadow installed packages when Python adds the script directory to sys.path. Prepend the parent folder name to each example file (e.g. openai.py -> function-calling-openai.py). Also split thinking-and-mcp/ into separate mcp/ and thinking/ directories.
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108
examples/video-processing/video-processing-mirror.py
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108
examples/video-processing/video-processing-mirror.py
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.frames.frames import (
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Frame,
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InputAudioRawFrame,
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InputImageRawFrame,
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OutputAudioRawFrame,
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OutputImageRawFrame,
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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.frame_processor import FrameDirection, FrameProcessor
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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load_dotenv(override=True)
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class MirrorProcessor(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, InputAudioRawFrame):
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await self.push_frame(
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OutputAudioRawFrame(
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audio=frame.audio,
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sample_rate=frame.sample_rate,
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num_channels=frame.num_channels,
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)
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)
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elif isinstance(frame, InputImageRawFrame):
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await self.push_frame(
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OutputImageRawFrame(image=frame.image, size=frame.size, format=frame.format)
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)
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else:
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await self.push_frame(frame, direction)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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video_in_enabled=True,
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video_out_enabled=True,
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video_out_is_live=True,
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video_out_width=1280,
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video_out_height=720,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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video_in_enabled=True,
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video_out_enabled=True,
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video_out_is_live=True,
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video_out_width=1280,
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video_out_height=720,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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pipeline = Pipeline([transport.input(), MirrorProcessor(), transport.output()])
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task = PipelineTask(
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pipeline,
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params=PipelineParams(),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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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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@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=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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