Storybot and Chatbot examples (#58)
* storybot * storybot * added pipeline.queue_frames * fixup
This commit is contained in:
chadbailey59
291
src/examples/starter-apps/storybot.py
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291
src/examples/starter-apps/storybot.py
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import aiohttp
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import asyncio
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import json
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import random
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import logging
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import os
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import re
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import wave
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from typing import AsyncGenerator
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from PIL import Image
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from dailyai.pipeline.pipeline import Pipeline
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from dailyai.pipeline.frame_processor import FrameProcessor
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from dailyai.services.daily_transport_service import DailyTransportService
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from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
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from dailyai.services.fal_ai_services import FalImageGenService
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from dailyai.services.open_ai_services import OpenAILLMService
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from dailyai.services.deepgram_ai_services import DeepgramTTSService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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from dailyai.pipeline.aggregators import (
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LLMAssistantContextAggregator,
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LLMContextAggregator,
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LLMUserContextAggregator,
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ParallelPipeline,
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UserResponseAggregator,
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LLMResponseAggregator,
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)
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from examples.support.runner import configure
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from dailyai.pipeline.frames import (
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LLMMessagesQueueFrame,
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TranscriptionQueueFrame,
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Frame,
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TextFrame,
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LLMFunctionCallFrame,
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LLMFunctionStartFrame,
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LLMResponseEndFrame,
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StartFrame,
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AudioFrame,
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SpriteFrame,
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ImageFrame,
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UserStoppedSpeakingFrame,
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)
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from dailyai.services.ai_services import FrameLogger, AIService
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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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sounds = {}
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images = {}
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sound_files = ["talking.wav", "listening.wav", "ding3.wav"]
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image_files = ["grandma-writing.png", "grandma-listening.png"]
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script_dir = os.path.dirname(__file__)
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for file in sound_files:
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# Build the full path to the sound file
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full_path = os.path.join(script_dir, "assets", file)
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# Get the filename without the extension to use as the dictionary key
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filename = os.path.splitext(os.path.basename(full_path))[0]
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# Open the sound and convert it to bytes
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with wave.open(full_path) as audio_file:
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sounds[file] = audio_file.readframes(-1)
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for file in image_files:
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# Build the full path to the image file
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full_path = os.path.join(script_dir, "assets", file)
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# Get the filename without the extension to use as the dictionary key
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filename = os.path.splitext(os.path.basename(full_path))[0]
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# Open the image and convert it to bytes
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with Image.open(full_path) as img:
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images[file] = img.tobytes()
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class StoryStartFrame(TextFrame):
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pass
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class StoryPageFrame(TextFrame):
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pass
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class StoryPromptFrame(TextFrame):
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pass
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class StoryProcessor(FrameProcessor):
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def __init__(self, messages, story):
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self._messages = messages
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self._text = ""
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self._story = story
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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"""
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The response from the LLM service looks like:
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A comment about the user's choice
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[start] (when the cat starts telling parts of the story)
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A sentence of the story
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[break] (between each sentence/'page' of the story)
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[prompt] (when the cat asks the user to make a decision)
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Question about the next part of the story
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1. Catch the frames that are generated by the LLM service
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"""
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if isinstance(frame, UserStoppedSpeakingFrame):
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yield ImageFrame(None, images["grandma-writing.png"])
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yield AudioFrame(sounds["talking.wav"])
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elif isinstance(frame, TextFrame):
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self._text += frame.text
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if re.findall(r".*\[[sS]tart\].*", self._text):
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# Then we have the intro. Send it to speech ASAP
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self._text = self._text.replace("[Start]", "")
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self._text = self._text.replace("[start]", "")
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self._text = self._text.replace("\n", " ")
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if len(self._text) > 2:
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yield ImageFrame(None, images["grandma-writing.png"])
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yield StoryStartFrame(self._text)
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yield AudioFrame(sounds["ding3.wav"])
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self._text = ""
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elif re.findall(r".*\[[bB]reak\].*", self._text):
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# Then it's a page of the story. Get an image too
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self._text = self._text.replace("[Break]", "")
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self._text = self._text.replace("[break]", "")
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self._text = self._text.replace("\n", " ")
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if len(self._text) > 2:
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self._story.append(self._text)
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yield StoryPageFrame(self._text)
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yield AudioFrame(sounds["ding3.wav"])
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self._text = ""
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elif re.findall(r".*\[[pP]rompt\].*", self._text):
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# Then it's question time. Flush any
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# text here as a story page, then set
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# the var to get to prompt mode
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# cb: trying scene now
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# self.handle_chunk(self._text)
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self._text = self._text.replace("[Prompt]", "")
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self._text = self._text.replace("[prompt]", "")
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self._text = self._text.replace("\n", " ")
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if len(self._text) > 2:
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self._story.append(self._text)
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yield StoryPageFrame(self._text)
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else:
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# After the prompt thing, we'll catch an LLM end to get the last bit
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pass
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elif isinstance(frame, LLMResponseEndFrame):
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yield ImageFrame(None, images["grandma-writing.png"])
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yield StoryPromptFrame(self._text)
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self._text = ""
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yield frame
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yield ImageFrame(None, images["grandma-listening.png"])
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yield AudioFrame(sounds["listening.wav"])
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else:
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# pass through everything that's not a TextFrame
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yield frame
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class StoryImageGenerator(FrameProcessor):
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def __init__(self, story, llm, img):
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self._story = story
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self._llm = llm
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self._img = img
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, StoryPageFrame):
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if len(self._story) == 1:
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prompt = f'You are an illustrator for a children\'s story book. Generate a prompt for DALL-E to create an illustration for the first page of the book, which reads: "{self._story[0]}"\n\n Your response should start with the phrase "Children\'s book illustration of".'
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else:
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prompt = f"You are an illustrator for a children's story book. Here is the story so far:\n\n\"{' '.join(self._story[:-1])}\"\n\nGenerate a prompt for DALL-E to create an illustration for the next page. Here's the sentence for the next page:\n\n\"{self._story[-1:][0]}\"\n\n Your response should start with the phrase \"Children's book illustration of\"."
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msgs = [{"role": "system", "content": prompt}]
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image_prompt = ""
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async for f in self._llm.process_frame(LLMMessagesQueueFrame(msgs)):
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if isinstance(f, TextFrame):
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image_prompt += f.text
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async for f in self._img.process_frame(TextFrame(image_prompt)):
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yield f
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# Yield the original StoryPageFrame for basic image/audio sync
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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: str, token):
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async with aiohttp.ClientSession() as session:
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global transport
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global llm
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global tts
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messages = [
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{
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"role": "system",
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"content": "You are a storytelling grandma who loves to make up fantastic, fun, and educational stories for children between the ages of 5 and 10 years old. Your stories are full of friendly, magical creatures. Your stories are never scary. Each sentence of your story will become a page in a storybook. Stop after 3-4 sentences and give the child a choice to make that will influence the next part of the story. Once the child responds, start by saying something nice about the choice they made, then include [start] in your response. Include [break] after each sentence of the story. Include [prompt] between the story and the prompt.",
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}
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]
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story = []
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
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model="gpt-4-1106-preview",
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) # gpt-4-1106-preview
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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="Xb7hH8MSUJpSbSDYk0k2",
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) # matilda
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img = FalImageGenService(
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image_size="1024x1024",
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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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lra = LLMResponseAggregator(messages)
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ura = UserResponseAggregator(messages)
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sp = StoryProcessor(messages, story)
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sig = StoryImageGenerator(story, llm, img)
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transport = DailyTransportService(
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room_url,
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token,
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"Storybot",
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5,
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mic_enabled=True,
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mic_sample_rate=16000,
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camera_enabled=True,
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camera_width=1024,
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camera_height=1024,
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start_transcription=True,
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vad_enabled=True,
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vad_stop_s=1.5,
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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):
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# We're being a bit tricky here by using a special system prompt to
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# ask the user for a story topic. After their intial response, we'll
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# use a different system prompt to create story pages.
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intro_messages = [
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{
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"role": "system",
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"content": "You are a storytelling grandma who loves to make up fantastic, fun, and educational stories for children between the ages of 5 and 10 years old. Your stories are full of friendly, magical creatures. Your stories are never scary. Begin by asking what a child wants you to tell a story about. Keep your reponse to only a few sentences.",
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}
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]
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lca = LLMAssistantContextAggregator(messages)
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await tts.run_to_queue(
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transport.send_queue,
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lca.run(
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llm.run(
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[
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ImageFrame(None, images["grandma-listening.png"]),
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LLMMessagesQueueFrame(intro_messages),
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AudioFrame(sounds["listening.wav"]),
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]
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),
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),
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)
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async def storytime():
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fl = FrameLogger("### After Image Generation")
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pipeline = Pipeline(
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processors=[
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ura,
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llm,
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sp,
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sig,
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fl,
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tts,
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lra,
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]
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)
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await transport.run_uninterruptible_pipeline(
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pipeline,
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)
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transport.transcription_settings["extra"]["endpointing"] = True
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transport.transcription_settings["extra"]["punctuate"] = True
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try:
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await asyncio.gather(transport.run(), storytime())
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except (asyncio.CancelledError, KeyboardInterrupt):
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print("whoops")
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transport.stop()
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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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