72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
import asyncio
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import logging
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import tkinter as tk
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from typing import AsyncGenerator
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from dailyai.pipeline.aggregators import FrameProcessor
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from dailyai.pipeline.frames import ImageFrame, Frame, UserImageFrame
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from dailyai.pipeline.pipeline import Pipeline
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from dailyai.transports.daily_transport import DailyTransport
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from dailyai.transports.local_transport import LocalTransport
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from runner import configure
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
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logger = logging.getLogger("dailyai")
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logger.setLevel(logging.DEBUG)
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class UserImageProcessor(FrameProcessor):
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, UserImageFrame):
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yield ImageFrame(frame.image, frame.size)
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else:
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yield frame
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async def main(room_url: str, token):
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tk_root = tk.Tk()
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tk_root.title("dailyai")
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local_transport = LocalTransport(
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tk_root=tk_root,
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camera_enabled=True,
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camera_width=1280,
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camera_height=720
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)
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transport = DailyTransport(
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room_url,
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token,
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"Render participant video",
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video_rendering_enabled=True
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)
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@transport.event_handler("on_first_other_participant_joined")
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async def on_first_other_participant_joined(transport, participant):
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transport.render_participant_video(participant["id"])
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async def run_tk():
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while not transport._stop_threads.is_set():
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tk_root.update()
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tk_root.update_idletasks()
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await asyncio.sleep(0.1)
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local_pipeline = Pipeline([UserImageProcessor()], source=transport.receive_queue)
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await asyncio.gather(
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transport.run(),
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local_transport.run(local_pipeline, override_pipeline_source_queue=False),
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run_tk()
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)
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if __name__ == "__main__":
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(url, token) = configure()
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asyncio.run(main(url, token))
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