161 lines
5.0 KiB
Python
161 lines
5.0 KiB
Python
#
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# Copyright (c) 2024, 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 asyncio
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import aiohttp
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import os
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import sys
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from dataclasses import dataclass
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from pipecat.frames.frames import (
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DataFrame,
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Frame,
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LLMFullResponseStartFrame,
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LLMMessagesFrame,
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TextFrame,
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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.sync_parallel_pipeline import SyncParallelPipeline
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from pipecat.pipeline.task import PipelineTask
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.processors.aggregators.sentence import SentenceAggregator
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from pipecat.services.cartesia import CartesiaHttpTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.services.fal import FalImageGenService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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@dataclass
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class MonthFrame(DataFrame):
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month: str
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def __str__(self):
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return f"{self.name}(month: {self.month})"
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class MonthPrepender(FrameProcessor):
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def __init__(self):
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super().__init__()
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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, direction: FrameDirection):
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await super().process_frame(frame, direction)
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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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await self.push_frame(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, LLMFullResponseStartFrame):
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self.prepend_to_next_text_frame = True
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await self.push_frame(frame)
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else:
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await self.push_frame(frame, direction)
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(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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DailyParams(
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audio_out_enabled=True,
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camera_out_enabled=True,
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camera_out_width=1024,
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camera_out_height=1024,
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),
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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tts = CartesiaHttpTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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imagegen = FalImageGenService(
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params=FalImageGenService.InputParams(image_size="square_hd"),
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aiohttp_session=session,
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key=os.getenv("FAL_KEY"),
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)
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sentence_aggregator = SentenceAggregator()
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month_prepender = MonthPrepender()
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# With `SyncParallelPipeline` we synchronize audio and images by pushing
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# them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2 I3 A3). To do
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# that, each pipeline runs concurrently and `SyncParallelPipeline` will
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# wait for the input frame to be processed.
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#
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# Note that `SyncParallelPipeline` requires the last processor in each
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# of the pipelines to be synchronous. In this case, we use
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# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
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# requests and wait for the response.
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pipeline = Pipeline(
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[
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llm, # LLM
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sentence_aggregator, # Aggregates LLM output into full sentences
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SyncParallelPipeline( # Run pipelines in parallel aggregating the result
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[month_prepender, tts], # Create "Month: sentence" and output audio
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[imagegen], # Generate image
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),
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transport.output(), # Transport output
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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=month))
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frames.append(LLMMessagesFrame(messages))
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runner = PipelineRunner()
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task = PipelineTask(pipeline)
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await task.queue_frames(frames)
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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