147 lines
4.3 KiB
Python
147 lines
4.3 KiB
Python
import asyncio
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import aiohttp
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import os
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import logging
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from dataclasses import dataclass
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from typing import AsyncGenerator
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from dailyai.pipeline.aggregators import (
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GatedAggregator,
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LLMFullResponseAggregator,
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ParallelPipeline,
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SentenceAggregator,
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)
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from dailyai.pipeline.frames import (
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Frame,
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TextFrame,
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EndFrame,
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ImageFrame,
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LLMMessagesFrame,
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LLMResponseStartFrame,
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)
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from dailyai.pipeline.frame_processor import FrameProcessor
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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.services.open_ai_services import OpenAILLMService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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from dailyai.services.fal_ai_services import FalImageGenService
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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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@dataclass
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class MonthFrame(Frame):
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month: str
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class MonthPrepender(FrameProcessor):
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def __init__(self):
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self.most_recent_month = "Placeholder, month frame not yet received"
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self.prepend_to_next_text_frame = False
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, MonthFrame):
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self.most_recent_month = frame.month
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elif self.prepend_to_next_text_frame and isinstance(frame, TextFrame):
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yield TextFrame(f"{self.most_recent_month}: {frame.text}")
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self.prepend_to_next_text_frame = False
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elif isinstance(frame, LLMResponseStartFrame):
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self.prepend_to_next_text_frame = True
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yield frame
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else:
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yield frame
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async def main(room_url):
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async with aiohttp.ClientSession() as session:
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transport = DailyTransport(
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room_url,
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None,
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"Month Narration Bot",
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mic_enabled=True,
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camera_enabled=True,
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mic_sample_rate=16000,
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camera_width=1024,
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camera_height=1024,
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)
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tts = ElevenLabsTTSService(
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aiohttp_session=session,
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4-turbo-preview")
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imagegen = FalImageGenService(
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image_size="square_hd",
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aiohttp_session=session,
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key_id=os.getenv("FAL_KEY_ID"),
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key_secret=os.getenv("FAL_KEY_SECRET"),
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)
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gated_aggregator = GatedAggregator(
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gate_open_fn=lambda frame: isinstance(
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frame, ImageFrame), gate_close_fn=lambda frame: isinstance(
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frame, LLMResponseStartFrame), start_open=False, )
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sentence_aggregator = SentenceAggregator()
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month_prepender = MonthPrepender()
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llm_full_response_aggregator = LLMFullResponseAggregator()
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pipeline = Pipeline(
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processors=[
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llm,
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sentence_aggregator,
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ParallelPipeline(
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[[month_prepender, tts], [llm_full_response_aggregator, imagegen]]
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),
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gated_aggregator,
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],
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)
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frames = []
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for month in [
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"January",
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"February",
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"March",
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"April",
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"May",
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"June",
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"July",
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"August",
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"September",
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"October",
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"November",
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"December",
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]:
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messages = [
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{
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"role": "system",
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"content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
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}
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]
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frames.append(MonthFrame(month))
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frames.append(LLMMessagesFrame(messages))
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frames.append(EndFrame())
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await pipeline.queue_frames(frames)
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await transport.run(pipeline, override_pipeline_source_queue=False)
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if __name__ == "__main__":
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(url, token) = configure()
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asyncio.run(main(url))
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