Files
pipecat/src/samples/foundational/06a-image-sync.py
2024-01-29 14:29:32 -05:00

135 lines
4.8 KiB
Python

import argparse
import asyncio
import os
from typing import AsyncGenerator
import aiohttp
import requests
import time
import urllib.parse
from PIL import Image
from dailyai.queue_frame import ImageQueueFrame, QueueFrame
from dailyai.services.daily_transport_service import DailyTransportService
from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
from dailyai.services.ai_services import AIService
from dailyai.queue_aggregators import LLMAssistantContextAggregator, LLMUserContextAggregator
from dailyai.services.fal_ai_services import FalImageGenService
class ImageSyncAggregator(AIService):
def __init__(self, speaking_path: str, waiting_path: str):
self._speaking_image = Image.open(speaking_path)
self._speaking_image_bytes = self._speaking_image.tobytes()
self._waiting_image = Image.open(waiting_path)
self._waiting_image_bytes = self._waiting_image.tobytes()
async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
yield ImageQueueFrame(None, self._speaking_image_bytes)
yield frame
yield ImageQueueFrame(None, self._waiting_image_bytes)
async def main(room_url: str, token):
async with aiohttp.ClientSession() as aiohttp_session:
transport = DailyTransportService(
room_url,
token,
"Respond bot",
5,
)
transport.camera_enabled = True
transport.camera_width = 1024
transport.camera_height = 1024
transport.mic_enabled = True
transport.mic_sample_rate = 16000
llm = AzureLLMService()
tts = AzureTTSService()
img = FalImageGenService(image_size="1024x1024", aiohttp_session=aiohttp_session)
async def get_images():
get_speaking_task = asyncio.create_task(
img.run_image_gen("An image of a cat speaking")
)
get_waiting_task = asyncio.create_task(
img.run_image_gen("An image of a cat waiting")
)
(speaking_data, waiting_data) = await asyncio.gather(
get_speaking_task, get_waiting_task
)
return speaking_data, waiting_data
@transport.event_handler("on_first_other_participant_joined")
async def on_first_other_participant_joined(transport):
await tts.say("Hi, I'm listening!", transport.send_queue)
async def handle_transcriptions():
messages = [
{"role": "system", "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way."},
]
tma_in = LLMUserContextAggregator(
messages, transport.my_participant_id
)
tma_out = LLMAssistantContextAggregator(
messages, transport.my_participant_id
)
image_sync_aggregator = ImageSyncAggregator(
os.path.join(os.path.dirname(__file__), "images", "speaking.png"),
os.path.join(os.path.dirname(__file__), "images", "waiting.png"),
)
await tts.run_to_queue(
transport.send_queue,
image_sync_aggregator.run(
tma_out.run(
llm.run(
tma_in.run(
transport.get_receive_frames()
)
)
)
)
)
transport.transcription_settings["extra"]["punctuate"] = True
await asyncio.gather(transport.run(), handle_transcriptions())
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
parser.add_argument(
"-u", "--url", type=str, required=True, help="URL of the Daily room to join"
)
parser.add_argument(
"-k",
"--apikey",
type=str,
required=True,
help="Daily API Key (needed to create token)",
)
args, unknown = parser.parse_known_args()
# Create a meeting token for the given room with an expiration 1 hour in the future.
room_name: str = urllib.parse.urlparse(args.url).path[1:]
expiration: float = time.time() + 60 * 60
res: requests.Response = requests.post(
f"https://api.daily.co/v1/meeting-tokens",
headers={"Authorization": f"Bearer {args.apikey}"},
json={
"properties": {"room_name": room_name, "is_owner": True, "exp": expiration}
},
)
if res.status_code != 200:
raise Exception(f"Failed to create meeting token: {res.status_code} {res.text}")
token: str = res.json()["token"]
asyncio.run(main(args.url, token))