fal.ai integration (#3)
* fal.ai image gen * some sample and readme updates * holy cow this is fast * basic image-gen working * starting audio prompt and reset * short confirmation words * moved fal module to pyproject.toml --------- Co-authored-by: Moishe Lettvin <moishel@gmail.com>
This commit is contained in:
@@ -15,7 +15,8 @@ dependencies = [
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"azure-cognitiveservices-speech",
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"azure-cognitiveservices-speech",
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"pyht",
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"pyht",
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"opentelemetry-sdk",
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"opentelemetry-sdk",
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"aiohttp"
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"aiohttp",
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"fal"
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]
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]
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[tool.setuptools.packages.find]
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[tool.setuptools.packages.find]
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@@ -1,2 +1,2 @@
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Pillow==10.1.0
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Pillow==10.1.0
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typing_extensions==4.9.0
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typing_extensions==4.9.0
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51
src/dailyai/services/fal_ai_services.py
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51
src/dailyai/services/fal_ai_services.py
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@@ -0,0 +1,51 @@
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import fal
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import aiohttp
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import asyncio
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import io
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import json
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from PIL import Image
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from dailyai.services.ai_services import LLMService, TTSService, ImageGenService
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# Fal expects FAL_KEY_ID and FAL_KEY_SECRET to be set in the env
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class FalImageGenService(ImageGenService):
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def __init__(self):
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super().__init__()
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async def run_image_gen(self, sentence, size) -> tuple[str, bytes]:
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def get_image_url(sentence, size):
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print("starting fal submit...")
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handler = fal.apps.submit(
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"110602490-fast-sdxl",
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arguments={
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"prompt": sentence
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},
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)
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print("past fal handler init, about to wait for iter_events...")
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for event in handler.iter_events():
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if isinstance(event, fal.apps.InProgress):
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print('Request in progress')
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print(event.logs)
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result = handler.get()
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image_url = result["images"][0]["url"] if result else None
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if not image_url:
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raise Exception("Image generation failed")
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return image_url
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print(f"fetching image url...")
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image_url = await asyncio.to_thread(get_image_url, sentence, size)
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print(f"got image url, downloading image...")
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# Load the image from the url
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async with aiohttp.ClientSession() as session:
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async with session.get(image_url) as response:
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print("got image response")
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image_stream = io.BytesIO(await response.content.read())
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print("read image stream")
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image = Image.open(image_stream)
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return (image_url, image.tobytes())
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# return (image_url, dalle_im.tobytes())
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113
src/samples/image-gen.py
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113
src/samples/image-gen.py
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@@ -0,0 +1,113 @@
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import argparse
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import asyncio
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import requests
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import time
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import urllib.parse
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import random
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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.queue_frame import QueueFrame, FrameType
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from dailyai.services.fal_ai_services import FalImageGenService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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async def main(room_url:str, token):
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global transport
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global llm
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global tts
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transport = DailyTransportService(
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room_url,
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token,
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"Imagebot",
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1,
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)
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transport.mic_enabled = True
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transport.camera_enabled = True
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transport.mic_sample_rate = 16000
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transport.camera_width = 1024
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transport.camera_height = 1024
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llm = AzureLLMService()
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tts = AzureTTSService()
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img = FalImageGenService()
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async def handle_transcriptions():
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print("handle_transcriptions got called")
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sentence = ""
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async for message in transport.get_transcriptions():
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print(f"transcription message: {message}")
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if message["session_id"] == transport.my_participant_id:
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continue
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finder = message["text"].find("start over")
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print(f"finder: {finder}")
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if finder >= 0:
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async for audio in tts.run_tts(f"Resetting."):
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transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
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sentence = ""
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continue
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# todo: we could differentiate between transcriptions from different participants
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sentence += f" {message['text']}"
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print(f"sentence is now: {sentence}")
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# TODO: Cache this audio
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phrase = random.choice(["OK.", "Got it.", "Sure.", "You bet.", "Sure thing."])
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async for audio in tts.run_tts(phrase):
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transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
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img_result = img.run_image_gen(sentence, "1024x1024")
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awaited_img = await asyncio.gather(img_result)
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transport.output_queue.put(
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[
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QueueFrame(FrameType.IMAGE_FRAME, awaited_img[0][1]),
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]
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)
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@transport.event_handler("on_participant_joined")
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async def on_participant_joined(transport, participant):
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print(f"participant joined: {participant['info']['userName']}")
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if participant["info"]["isLocal"]:
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return
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async for audio in tts.run_tts("Describe an image, and I'll create it."):
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audio_generator = tts.run_tts(f"Hello, {participant['info']['userName']}! Describe an image and I'll create it. To start over, just say 'start over'.")
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async for audio in audio_generator:
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transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
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transport.transcription_settings["extra"]["punctuate"] = False
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transport.transcription_settings["extra"]["endpointing"] = False
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await asyncio.gather(transport.run(), handle_transcriptions())
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
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parser.add_argument(
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"-u", "--url", type=str, required=True, help="URL of the Daily room to join"
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)
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parser.add_argument(
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"-k",
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"--apikey",
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type=str,
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required=True,
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help="Daily API Key (needed to create token)",
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)
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args, unknown = parser.parse_known_args()
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# Create a meeting token for the given room with an expiration 1 hour in the future.
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room_name: str = urllib.parse.urlparse(args.url).path[1:]
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expiration: float = time.time() + 60 * 60
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res: requests.Response = requests.post(
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f"https://api.daily.co/v1/meeting-tokens",
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headers={"Authorization": f"Bearer {args.apikey}"},
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json={
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"properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
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},
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)
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if res.status_code != 200:
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raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
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token: str = res.json()["token"]
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asyncio.run(main(args.url, token))
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@@ -9,6 +9,7 @@ from dailyai.services.azure_ai_services import AzureLLMService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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from dailyai.services.open_ai_services import OpenAIImageGenService
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from dailyai.services.open_ai_services import OpenAIImageGenService
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from dailyai.services.daily_transport_service import DailyTransportService
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from dailyai.services.daily_transport_service import DailyTransportService
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from dailyai.services.fal_ai_services import FalImageGenService
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async def main(room_url):
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async def main(room_url):
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meeting_duration_minutes = 5
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meeting_duration_minutes = 5
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@@ -25,8 +26,10 @@ async def main(room_url):
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transport.camera_height = 1024
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transport.camera_height = 1024
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llm = AzureLLMService()
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llm = AzureLLMService()
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tts = ElevenLabsTTSService(voice_id="ErXwobaYiN019PkySvjV")
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#tts = ElevenLabsTTSService(voice_id="ErXwobaYiN019PkySvjV")
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dalle = OpenAIImageGenService()
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tts = ElevenLabsTTSService()
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dalle = FalImageGenService()
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# dalle = OpenAIImageGenService()
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# Get a complete audio chunk from the given text. Splitting this into its own
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# Get a complete audio chunk from the given text. Splitting this into its own
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# coroutine lets us ensure proper ordering of the audio chunks on the output queue.
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# coroutine lets us ensure proper ordering of the audio chunks on the output queue.
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@@ -77,7 +80,8 @@ async def main(room_url):
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months: list[str] = [
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months: list[str] = [
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"January",
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"January",
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"February",
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"February",
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"March",
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"March"]
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"""
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"April",
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"April",
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"May",
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"May",
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"June",
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"June",
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@@ -88,6 +92,7 @@ async def main(room_url):
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"November",
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"November",
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"December",
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"December",
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]
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]
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"""
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@transport.event_handler("on_first_other_participant_joined")
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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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async def on_first_other_participant_joined(transport):
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@@ -96,6 +101,7 @@ async def main(room_url):
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# likely no delay between months, but the months won't display in order.
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# likely no delay between months, but the months won't display in order.
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for month_data_task in asyncio.as_completed(month_tasks):
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for month_data_task in asyncio.as_completed(month_tasks):
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data = await month_data_task
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data = await month_data_task
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print(f"got data, queueing frames...")
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transport.output_queue.put(
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transport.output_queue.put(
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[
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[
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QueueFrame(FrameType.IMAGE, data["image"]),
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QueueFrame(FrameType.IMAGE, data["image"]),
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