renamed samples to examples

src/examples/foundational/01-say-one-thing.py

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+import asyncio
+import aiohttp
+import os
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+
+from examples.foundational.support.runner import configure
+
+async def main(room_url):
+    async with aiohttp.ClientSession() as session:
+        # create a transport service object using environment variables for
+        # the transport service's API key, room url, and any other configuration.
+        # services can all define and document the environment variables they use.
+        # services all also take an optional config object that is used instead of
+        # environment variables.
+        #
+        # the abstract transport service APIs presumably can map pretty closely
+        # to the daily-python basic API
+        meeting_duration_minutes = 5
+        transport = DailyTransportService(
+            room_url,
+            None,
+            "Say One Thing",
+            meeting_duration_minutes,
+            mic_enabled=True
+        )
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+
+        # Register an event handler so we can play the audio when the participant joins.
+        @transport.event_handler("on_participant_joined")
+        async def on_participant_joined(transport, participant):
+            if participant["info"]["isLocal"]:
+                return
+
+            await tts.say(
+                "Hello there, " + participant["info"]["userName"] + "!",
+                transport.send_queue,
+            )
+
+            # wait for the output queue to be empty, then leave the meeting
+            await transport.stop_when_done()
+
+        await transport.run()
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url))

src/examples/foundational/01a-local-transport.py

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+import asyncio
+import aiohttp
+import os
+
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.local_transport_service import LocalTransportService
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 1
+        transport = LocalTransportService(
+            duration_minutes=meeting_duration_minutes,
+            mic_enabled=True
+        )
+        tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
+
+        async def say_something():
+            await asyncio.sleep(1)
+            await tts.say(
+                "Hello there.",
+                transport.send_queue,
+            )
+            await transport.stop_when_done()
+
+        await asyncio.gather(transport.run(), say_something())
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/examples/foundational/02-llm-say-one-thing.py

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+import asyncio
+import os
+
+import aiohttp
+
+from dailyai.queue_frame import LLMMessagesQueueFrame
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.deepgram_ai_services import DeepgramTTSService
+from dailyai.services.open_ai_services import OpenAILLMService
+from examples.foundational.support.runner import configure
+
+async def main(room_url):
+    async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 1
+        transport = DailyTransportService(
+            room_url,
+            None,
+            "Say One Thing From an LLM",
+            duration_minutes=meeting_duration_minutes,
+            mic_enabled=True
+        )
+
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+        # tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+        # tts = DeepgramTTSService(aiohttp_session=session, api_key=os.getenv("DEEPGRAM_API_KEY"), voice=os.getenv("DEEPGRAM_VOICE"))
+
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        #llm = OpenAILLMService(api_key=os.getenv("OPENAI_CHATGPT_API_KEY"))
+        messages = [{
+            "role": "system",
+            "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world."
+        }]
+        tts_task = asyncio.create_task(
+            tts.run_to_queue(
+                transport.send_queue,
+                llm.run([LLMMessagesQueueFrame(messages)]),
+            )
+        )
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            await tts_task
+            await transport.stop_when_done()
+
+        await transport.run()
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url))

src/examples/foundational/03-still-frame.py

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+import asyncio
+import aiohttp
+import os
+
+from dailyai.queue_frame import TextQueueFrame
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.services.open_ai_services import OpenAIImageGenService
+from dailyai.services.azure_ai_services import AzureImageGenServiceREST
+
+from examples.foundational.support.runner import configure
+
+local_joined = False
+participant_joined = False
+
+
+async def main(room_url):
+    async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 1
+        transport = DailyTransportService(
+            room_url,
+            None,
+            "Show a still frame image",
+            duration_minutes=meeting_duration_minutes,
+            mic_enabled=False,
+            camera_enabled=True,
+            camera_width=1024,
+            camera_height=1024
+        )
+
+        imagegen = FalImageGenService(image_size="1024x1024", aiohttp_session=session, key_id=os.getenv("FAL_KEY_ID"), key_secret=os.getenv("FAL_KEY_SECRET"))
+        # imagegen = OpenAIImageGenService(aiohttp_session=session, api_key=os.getenv("OPENAI_DALLE_API_KEY"), image_size="1024x1024")
+        # imagegen = AzureImageGenServiceREST(image_size="1024x1024", aiohttp_session=session, api_key=os.getenv("AZURE_DALLE_API_KEY"), endpoint=os.getenv("AZURE_DALLE_ENDPOINT"), model=os.getenv("AZURE_DALLE_MODEL"))
+
+        image_task = asyncio.create_task(
+            imagegen.run_to_queue(
+                transport.send_queue, [
+                    TextQueueFrame("a cat in the style of picasso")]))
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            await image_task
+
+        await transport.run()
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url))

src/examples/foundational/03a-image-local.py

@@ -0,0 +1,50 @@
+import asyncio
+import aiohttp
+import os
+
+import tkinter as tk
+
+from dailyai.queue_frame import TextQueueFrame
+from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.services.local_transport_service import LocalTransportService
+
+local_joined = False
+participant_joined = False
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 2
+        tk_root = tk.Tk()
+        tk_root.title("Calendar")
+        transport = LocalTransportService(
+            tk_root=tk_root,
+            mic_enabled=True,
+            camera_enabled=True,
+            camera_width=1024,
+            camera_height=1024,
+            duration_minutes=meeting_duration_minutes,
+        )
+
+        imagegen = FalImageGenService(
+            image_size="1024x1024",
+            aiohttp_session=session,
+            key_id=os.getenv("FAL_KEY_ID"),
+            key_secret=os.getenv("FAL_KEY_SECRET"),
+        )
+        image_task = asyncio.create_task(
+            imagegen.run_to_queue(
+                transport.send_queue, [TextQueueFrame("a cat in the style of picasso")]
+            )
+        )
+
+        async def run_tk():
+            while not transport._stop_threads.is_set():
+                tk_root.update()
+                tk_root.update_idletasks()
+                await asyncio.sleep(0.1)
+
+        await asyncio.gather(transport.run(), image_task, run_tk())
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/examples/foundational/04-utterance-and-speech.py

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+import asyncio
+import os
+
+import aiohttp
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.queue_frame import EndStreamQueueFrame, LLMMessagesQueueFrame
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+
+from examples.foundational.support.runner import configure
+
+async def main(room_url: str):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransportService(
+            room_url,
+            None,
+            "Static And Dynamic Speech",
+            duration_minutes=1,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=False
+        )
+
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        azure_tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+        elevenlabs_tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+
+        messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
+
+        # Start a task to run the LLM to create a joke, and convert the LLM output to audio frames. This task
+        # will run in parallel with generating and speaking the audio for static text, so there's no delay to
+        # speak the LLM response.
+        buffer_queue = asyncio.Queue()
+        llm_response_task = asyncio.create_task(
+            elevenlabs_tts.run_to_queue(
+                buffer_queue,
+                llm.run([LLMMessagesQueueFrame(messages)]),
+                True,
+            )
+        )
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            await azure_tts.say("My friend the LLM is now going to tell a joke about llamas.", transport.send_queue)
+
+            async def buffer_to_send_queue():
+                while True:
+                    frame = await buffer_queue.get()
+                    await transport.send_queue.put(frame)
+                    buffer_queue.task_done()
+                    if isinstance(frame, EndStreamQueueFrame):
+                        break
+
+            await asyncio.gather(llm_response_task, buffer_to_send_queue())
+
+            await transport.stop_when_done()
+
+        await transport.run()
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url))

src/examples/foundational/05-sync-speech-and-image.py

@@ -0,0 +1,123 @@
+import asyncio
+import aiohttp
+import os
+
+from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
+from dailyai.services.azure_ai_services import AzureLLMService, AzureImageGenServiceREST, AzureTTSService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.services.open_ai_services import OpenAIImageGenService
+
+from examples.foundational.support.runner import configure
+
+async def main(room_url):
+    async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 5
+        transport = DailyTransportService(
+            room_url,
+            None,
+            "Month Narration Bot",
+            duration_minutes=meeting_duration_minutes,
+            mic_enabled=True,
+            camera_enabled=True,
+            mic_sample_rate=16000,
+            camera_width=1024,
+            camera_height=1024
+        )
+
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id="ErXwobaYiN019PkySvjV")
+        # tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+
+        dalle = FalImageGenService(image_size="1024x1024", aiohttp_session=session, key_id=os.getenv("FAL_KEY_ID"), key_secret=os.getenv("FAL_KEY_SECRET"))
+        # dalle = OpenAIImageGenService(aiohttp_session=session, api_key=os.getenv("OPENAI_DALLE_API_KEY"), image_size="1024x1024")
+        # dalle = AzureImageGenServiceREST(image_size="1024x1024", aiohttp_session=session, api_key=os.getenv("AZURE_DALLE_API_KEY"), endpoint=os.getenv("AZURE_DALLE_ENDPOINT"), model=os.getenv("AZURE_DALLE_MODEL"))
+
+        # Get a complete audio chunk from the given text. Splitting this into its own
+        # coroutine lets us ensure proper ordering of the audio chunks on the send queue.
+        async def get_all_audio(text):
+            all_audio = bytearray()
+            async for audio in tts.run_tts(text):
+                all_audio.extend(audio)
+
+            return all_audio
+
+        async def get_month_data(month):
+            messages = [
+                {
+                    "role": "system",
+                    "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.",
+                }
+            ]
+
+            image_description = await llm.run_llm(messages)
+            if not image_description:
+                return
+
+            to_speak = f"{month}: {image_description}"
+            audio_task = asyncio.create_task(get_all_audio(to_speak))
+            image_task = asyncio.create_task(dalle.run_image_gen(image_description))
+            print(f"about to gather tasks for {month}")
+            (audio, image_data) = await asyncio.gather(
+                audio_task, image_task
+            )
+            print(f"about to return from get_month_data for {month}")
+            return {
+                "month": month,
+                "text": image_description,
+                "image_url": image_data[0],
+                "image": image_data[1],
+                "audio": audio,
+            }
+
+        months: list[str] = [
+            "January",
+            "February",
+            "March",
+            "April",
+            "May",
+            "June"
+        ]
+        """
+        "February",
+        "March",
+        "April",
+        "May",
+        "June",
+        "July",
+        "August",
+        "September",
+        "October",
+        "November",
+        "December",
+        """
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            # This will play the months in the order they're completed. The benefit
+            # is we'll have as little delay as possible before the first month, and
+            # likely no delay between months, but the months won't display in order.
+            for month_data_task in asyncio.as_completed(month_tasks):
+                print(f"month_data_task: {month_data_task}")
+                try:
+                    data = await month_data_task
+                except Exception:
+                    print("OMG EXCEPTION!!!!")
+                if data:
+                    await transport.send_queue.put(
+                        [
+                            ImageQueueFrame(data["image_url"], data["image"]),
+                            AudioQueueFrame(data["audio"]),
+                        ]
+                    )
+
+            # wait for the output queue to be empty, then leave the meeting
+            await transport.stop_when_done()
+
+        month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
+
+        await transport.run()
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url))

src/examples/foundational/05a-local-sync-speech-and-text.py

@@ -0,0 +1,134 @@
+import aiohttp
+import argparse
+import asyncio
+import tkinter as tk
+import os
+
+from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
+from dailyai.services.azure_ai_services import AzureLLMService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.services.local_transport_service import LocalTransportService
+
+
+async def main(room_url):
+    async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 5
+        tk_root = tk.Tk()
+        tk_root.title("Calendar")
+
+        transport = LocalTransportService(
+            mic_enabled=True,
+            camera_enabled=True,
+            camera_width=1024,
+            camera_height=1024,
+            duration_minutes=meeting_duration_minutes,
+            tk_root=tk_root,
+        )
+
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
+        tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id="ErXwobaYiN019PkySvjV",
+        )
+        dalle = FalImageGenService(
+            image_size="1024x1024",
+            aiohttp_session=session,
+            key_id=os.getenv("FAL_KEY_ID"),
+            key_secret=os.getenv("FAL_KEY_SECRET"),
+        )
+
+        # Get a complete audio chunk from the given text. Splitting this into its own
+        # coroutine lets us ensure proper ordering of the audio chunks on the send queue.
+        async def get_all_audio(text):
+            all_audio = bytearray()
+            async for audio in tts.run_tts(text):
+                all_audio.extend(audio)
+
+            return all_audio
+
+        async def get_month_data(month):
+            messages = [
+                {
+                    "role": "system",
+                    "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.",
+                }
+            ]
+
+            image_description = await llm.run_llm(messages)
+            if not image_description:
+                return
+
+            to_speak = f"{month}: {image_description}"
+            audio_task = asyncio.create_task(get_all_audio(to_speak))
+            image_task = asyncio.create_task(dalle.run_image_gen(image_description))
+            (audio, image_data) = await asyncio.gather(
+                audio_task, image_task
+            )
+
+            return {
+                "month": month,
+                "text": image_description,
+                "image_url": image_data[0],
+                "image": image_data[1],
+                "audio": audio,
+            }
+
+        months: list[str] = [
+            "January",
+            "February",
+            "March",
+            "April",
+            "May",
+            "June",
+            "July",
+            "August",
+            "September",
+            "October",
+            "November",
+            "December",
+        ]
+
+        async def show_images():
+            # This will play the months in the order they're completed. The benefit
+            # is we'll have as little delay as possible before the first month, and
+            # likely no delay between months, but the months won't display in order.
+            for month_data_task in asyncio.as_completed(month_tasks):
+                data = await month_data_task
+                if data:
+                    await transport.send_queue.put(
+                        [
+                            ImageQueueFrame(data["image_url"], data["image"]),
+                            AudioQueueFrame(data["audio"]),
+                        ]
+                    )
+
+            await asyncio.sleep(25)
+
+            # wait for the output queue to be empty, then leave the meeting
+            await transport.stop_when_done()
+
+        async def run_tk():
+            while not transport._stop_threads.is_set():
+                tk_root.update()
+                tk_root.update_idletasks()
+                await asyncio.sleep(0.1)
+
+        month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
+
+        await asyncio.gather(transport.run(), show_images(), run_tk())
+
+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"
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    asyncio.run(main(args.url))

src/examples/foundational/06-listen-and-respond.py

@@ -0,0 +1,55 @@
+import asyncio
+import os
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.queue_aggregators import LLMAssistantContextAggregator, LLMContextAggregator, LLMUserContextAggregator
+from examples.foundational.support.runner import configure
+
+async def main(room_url: str, token):
+    transport = DailyTransportService(
+        room_url,
+        token,
+        "Respond bot",
+        duration_minutes=5,
+        start_transcription=True,
+        mic_enabled=True,
+        mic_sample_rate=16000,
+        camera_enabled = False
+    )
+
+    llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+    tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+
+    @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)
+        await tts.run_to_queue(
+            transport.send_queue,
+            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__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/examples/foundational/06a-image-sync.py

@@ -0,0 +1,105 @@
+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
+
+from examples.foundational.support.runner import configure
+
+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 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(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+        img = FalImageGenService(image_size="1024x1024", aiohttp_session=session, key_id=os.getenv("FAL_KEY_ID"), key_secret=os.getenv("FAL_KEY_SECRET"))
+
+        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__), "assets", "speaking.png"),
+                os.path.join(os.path.dirname(__file__), "assets", "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__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/examples/foundational/07-interruptible.py

@@ -0,0 +1,65 @@
+import asyncio
+import aiohttp
+import os
+from dailyai.conversation_wrappers import InterruptibleConversationWrapper
+
+from dailyai.queue_frame import StartStreamQueueFrame, TextQueueFrame
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+
+from examples.foundational.support.runner import configure
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransportService(
+            room_url,
+            token,
+            "Respond bot",
+            duration_minutes=5,
+            start_transcription=True,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=False,
+        )
+
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+
+        async def run_response(user_speech, tma_in, tma_out):
+            await tts.run_to_queue(
+                transport.send_queue,
+                tma_out.run(
+                    llm.run(
+                        tma_in.run(
+                            [StartStreamQueueFrame(), TextQueueFrame(user_speech)]
+                        )
+                    )
+                ),
+            )
+
+        @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 run_conversation():
+            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."},
+            ]
+
+            conversation_wrapper = InterruptibleConversationWrapper(
+                frame_generator=transport.get_receive_frames,
+                runner=run_response,
+                interrupt=transport.interrupt,
+                my_participant_id=transport._my_participant_id,
+                llm_messages=messages,
+            )
+            await conversation_wrapper.run_conversation()
+
+        transport.transcription_settings["extra"]["punctuate"] = False
+        await asyncio.gather(transport.run(), run_conversation())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/examples/foundational/08-bots-arguing.py

@@ -0,0 +1,100 @@
+import aiohttp
+import asyncio
+import os
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
+
+from examples.foundational.support.runner import configure
+
+async def main(room_url:str):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransportService(
+            room_url,
+            None,
+            "Respond bot",
+            duration_minutes=10,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=True,
+            camera_width=1024,
+            camera_height=1024
+        )
+
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts1 = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+        tts2 = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id="jBpfuIE2acCO8z3wKNLl")
+        dalle = FalImageGenService(image_size="1024x1024", aiohttp_session=session, key_id=os.getenv("FAL_KEY_ID"), key_secret=os.getenv("FAL_KEY_SECRET"))
+
+        bot1_messages = [
+            {"role": "system", "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long."},
+        ]
+        bot2_messages = [
+            {"role": "system", "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich."},
+        ]
+
+        async def get_bot1_statement():
+            # Run the LLMs synchronously for the back-and-forth
+            bot1_msg = await llm.run_llm(bot1_messages)
+            print(f"bot1_msg: {bot1_msg}")
+            if bot1_msg:
+                bot1_messages.append({"role": "assistant", "content": bot1_msg})
+                bot2_messages.append({"role": "user", "content": bot1_msg})
+
+            all_audio = bytearray()
+            async for audio in tts1.run_tts(bot1_msg):
+                all_audio.extend(audio)
+
+            return all_audio
+
+        async def get_bot2_statement():
+            # Run the LLMs synchronously for the back-and-forth
+            bot2_msg = await llm.run_llm(bot2_messages)
+            print(f"bot2_msg: {bot2_msg}")
+            if bot2_msg:
+                bot2_messages.append({"role": "assistant", "content": bot2_msg})
+                bot1_messages.append({"role": "user", "content": bot2_msg})
+
+            all_audio = bytearray()
+            async for audio in tts2.run_tts(bot2_msg):
+                all_audio.extend(audio)
+
+            return all_audio
+
+        async def argue():
+            for i in range(100):
+                print(f"In iteration {i}")
+
+                bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
+
+                (audio1, image_data1) = await asyncio.gather(
+                    get_bot1_statement(), dalle.run_image_gen(bot1_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageQueueFrame(None, image_data1[1]),
+                        AudioQueueFrame(audio1),
+                    ]
+                )
+
+                bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
+
+                (audio2, image_data2) = await asyncio.gather(
+                    get_bot2_statement(), dalle.run_image_gen(bot2_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageQueueFrame(None, image_data2[1]),
+                        AudioQueueFrame(audio2),
+                    ]
+                )
+
+        await asyncio.gather(transport.run(), argue())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url))

src/examples/foundational/10-wake-word.py

@@ -0,0 +1,173 @@
+import aiohttp
+import asyncio
+import os
+import random
+from typing import AsyncGenerator
+
+from PIL import Image
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.queue_aggregators import LLMUserContextAggregator, LLMAssistantContextAggregator
+from dailyai.queue_frame import (
+    QueueFrame,
+    TextQueueFrame,
+    ImageQueueFrame,
+    SpriteQueueFrame,
+    TranscriptionQueueFrame,
+)
+from dailyai.services.ai_services import AIService
+
+from examples.foundational.support.runner import configure
+
+
+sprites = {}
+image_files = [
+    'sc-default.png',
+    'sc-talk.png',
+    'sc-listen-1.png',
+    'sc-think-1.png',
+    'sc-think-2.png',
+    'sc-think-3.png',
+    'sc-think-4.png'
+]
+
+script_dir = os.path.dirname(__file__)
+
+for file in image_files:
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, "assets", file)
+    # Get the filename without the extension to use as the dictionary key
+    filename = os.path.splitext(os.path.basename(full_path))[0]
+    # Open the image and convert it to bytes
+    with Image.open(full_path) as img:
+        sprites[file] = img.tobytes()
+
+# When the bot isn't talking, show a static image of the cat listening
+quiet_frame = ImageQueueFrame("", sprites["sc-listen-1.png"])
+# When the bot is talking, build an animation from two sprites
+talking_list = [sprites['sc-default.png'], sprites['sc-talk.png']]
+talking = [random.choice(talking_list) for x in range(30)]
+talking_frame = SpriteQueueFrame(images=talking)
+
+# TODO: Support "thinking" as soon as we get a valid transcript, while LLM is processing
+thinking_list = [
+    sprites['sc-think-1.png'],
+    sprites['sc-think-2.png'],
+    sprites['sc-think-3.png'],
+    sprites['sc-think-4.png']]
+thinking_frame = SpriteQueueFrame(images=thinking_list)
+
+
+class TranscriptFilter(AIService):
+    def __init__(self, bot_participant_id=None):
+        self.bot_participant_id = bot_participant_id
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, TranscriptionQueueFrame):
+            if frame.participantId != self.bot_participant_id:
+                yield frame
+
+
+class NameCheckFilter(AIService):
+    def __init__(self, names:list[str]):
+        self.names = names
+        self.sentence = ""
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        content: str = ""
+
+        # TODO: split up transcription by participant
+        if isinstance(frame, TextQueueFrame):
+            content = frame.text
+
+        self.sentence += content
+        if self.sentence.endswith((".", "?", "!")):
+            if any(name in self.sentence for name in self.names):
+                out = self.sentence
+                self.sentence = ""
+                yield TextQueueFrame(out)
+            else:
+                out = self.sentence
+                self.sentence = ""
+
+
+class ImageSyncAggregator(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        yield talking_frame
+        yield frame
+        yield quiet_frame
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransportService(
+            room_url,
+            token,
+            "Santa Cat",
+            duration_minutes=3,
+            start_transcription=True,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=True,
+            camera_width=720,
+            camera_height=1280
+        )
+        transport._mic_enabled = True
+        transport._mic_sample_rate = 16000
+        transport._camera_enabled = True
+        transport._camera_width = 720
+        transport._camera_height = 1280
+
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id="jBpfuIE2acCO8z3wKNLl")
+        isa = ImageSyncAggregator()
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            await tts.say("Hi! If you want to talk to me, just say 'hey Santa Cat'.", transport.send_queue)
+
+        async def handle_transcriptions():
+            messages = [
+                {"role": "system", "content": "You are Santa Cat, a cat that lives in Santa's workshop at the North Pole. You should be clever, and a bit sarcastic. You should also tell jokes every once in a while.  Your responses should only be a few sentences long."},
+            ]
+
+            tma_in = LLMUserContextAggregator(
+                messages, transport._my_participant_id
+            )
+            tma_out = LLMAssistantContextAggregator(
+                messages, transport._my_participant_id
+            )
+            tf = TranscriptFilter(transport._my_participant_id)
+            ncf = NameCheckFilter(["Santa Cat", "Santa"])
+            await tts.run_to_queue(
+                transport.send_queue,
+                isa.run(
+                    tma_out.run(
+                        llm.run(
+                            tma_in.run(
+                                ncf.run(
+                                    tf.run(
+                                        transport.get_receive_frames()
+                                    )
+                                )
+                            )
+                        )
+                    )
+                )
+            )
+
+        async def starting_image():
+            await transport.send_queue.put(quiet_frame)
+
+        transport.transcription_settings["extra"]["punctuate"] = True
+        await asyncio.gather(transport.run(), handle_transcriptions(), starting_image())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/examples/foundational/11-sound-effects.py

@@ -0,0 +1,127 @@
+import aiohttp
+import asyncio
+import logging
+import os
+import wave
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.queue_aggregators import LLMContextAggregator, LLMUserContextAggregator, LLMAssistantContextAggregator
+from dailyai.services.ai_services import AIService, FrameLogger
+from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
+from typing import AsyncGenerator
+
+from examples.foundational.support.runner import configure
+
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s") # or whatever
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
+
+sounds = {}
+sound_files = [
+    'ding1.wav',
+    'ding2.wav'
+]
+
+script_dir = os.path.dirname(__file__)
+
+for file in sound_files:
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, "assets", file)
+    # Get the filename without the extension to use as the dictionary key
+    filename = os.path.splitext(os.path.basename(full_path))[0]
+    # Open the image and convert it to bytes
+    with wave.open(full_path) as audio_file:
+        sounds[file] = audio_file.readframes(-1)
+
+
+
+
+class OutboundSoundEffectWrapper(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMResponseEndQueueFrame):
+            yield AudioQueueFrame(sounds["ding1.wav"])
+            # In case anything else up the stack needs it
+            yield frame
+        else:
+            yield frame
+
+class InboundSoundEffectWrapper(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMMessagesQueueFrame):
+            yield AudioQueueFrame(sounds["ding2.wav"])
+            # In case anything else up the stack needs it
+            yield frame
+        else:
+            yield frame
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransportService(
+            room_url,
+            token,
+            "Respond bot",
+            duration_minutes=5,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=False
+        )
+
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id="ErXwobaYiN019PkySvjV")
+
+
+        @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)
+            await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
+        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
+            )
+            out_sound = OutboundSoundEffectWrapper()
+            in_sound = InboundSoundEffectWrapper()
+            fl = FrameLogger("LLM Out")
+            fl2 = FrameLogger("Transcription In")
+            await out_sound.run_to_queue(
+                transport.send_queue,
+                tts.run(
+                    fl.run(
+                        tma_out.run(
+                            llm.run(
+                                fl2.run(
+                                    in_sound.run(
+                                        tma_in.run(
+                                            transport.get_receive_frames()
+                                        )
+                                    )
+                                )
+                            )
+                        )
+                    )
+                )
+            )
+
+
+        transport.transcription_settings["extra"]["punctuate"] = True
+        await asyncio.gather(transport.run(), handle_transcriptions())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/examples/foundational/13-whisper-transcription.py

@@ -0,0 +1,38 @@
+import asyncio
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.whisper_ai_services import WhisperSTTService
+
+from examples.foundational.support.runner import configure
+
+async def main(room_url: str):
+    transport = DailyTransportService(
+        room_url,
+        None,
+        "Transcription bot",
+        start_transcription=True,
+        mic_enabled=False,
+        camera_enabled=False,
+        speaker_enabled=True
+    )
+
+    stt = WhisperSTTService()
+    transcription_output_queue = asyncio.Queue()
+
+    async def handle_transcription():
+        print("`````````TRANSCRIPTION`````````")
+        while True:
+            item = await transcription_output_queue.get()
+            print(item.text)
+
+    async def handle_speaker():
+        await stt.run_to_queue(
+            transcription_output_queue,
+            transport.get_receive_frames()
+        )
+    await asyncio.gather(transport.run(), handle_speaker(), handle_transcription())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url))

src/examples/foundational/13a-whisper-local.py

@@ -0,0 +1,59 @@
+import argparse
+import asyncio
+import wave
+from dailyai.queue_frame import EndStreamQueueFrame, TranscriptionQueueFrame
+
+from dailyai.services.local_transport_service import LocalTransportService
+from dailyai.services.whisper_ai_services import WhisperSTTService
+
+
+async def main(room_url: str):
+    global transport
+    global stt
+
+    meeting_duration_minutes = 1
+    transport = LocalTransportService(
+        mic_enabled=True,
+        camera_enabled=False,
+        speaker_enabled=True,
+        duration_minutes=meeting_duration_minutes,
+        start_transcription = True
+    )
+    stt = WhisperSTTService()
+    transcription_output_queue = asyncio.Queue()
+    transport_done = asyncio.Event()
+
+    async def handle_transcription():
+        print("`````````TRANSCRIPTION`````````")
+        while not transport_done.is_set():
+            item = await transcription_output_queue.get()
+            print("got item from queue", item)
+            if isinstance(item, TranscriptionQueueFrame):
+                print(item.text)
+            elif isinstance(item, EndStreamQueueFrame):
+                break
+        print("handle_transcription done")
+
+    async def handle_speaker():
+        await stt.run_to_queue(
+            transcription_output_queue, transport.get_receive_frames()
+        )
+        await transcription_output_queue.put(EndStreamQueueFrame())
+        print("handle speaker done.")
+
+    async def run_until_done():
+        await transport.run()
+        transport_done.set()
+        print("run_until_done done")
+
+    await asyncio.gather(run_until_done(), handle_speaker(), handle_transcription())
+
+
+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"
+    )
+
+    args, unknown = parser.parse_known_args()
+    asyncio.run(main(args.url))

src/examples/foundational/support/runner.py

@@ -0,0 +1,52 @@
+import argparse
+import os
+import time
+import urllib
+import requests
+
+from dotenv import load_dotenv
+load_dotenv()
+
+def configure():
+    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
+    parser.add_argument(
+        "-k",
+        "--apikey",
+        type=str,
+        required=False,
+        help="Daily API Key (needed to create an owner token for the room)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    key = args.apikey or os.getenv("DAILY_API_KEY")
+
+    if not url:
+        raise Exception("No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL.")
+    
+    if not key:
+        raise Exception("No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers.")
+    
+
+    # Create a meeting token for the given room with an expiration 1 hour in the future.
+    room_name: str = urllib.parse.urlparse(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 {key}"},
+        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"]
+
+    return (url, token)

src/examples/image-gen.py

@@ -0,0 +1,114 @@
+import argparse
+import asyncio
+import requests
+import time
+import urllib.parse
+import random
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.queue_frame import QueueFrame, FrameType
+from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+
+
+async def main(room_url: str, token):
+    global transport
+    global llm
+    global tts
+
+    transport = DailyTransportService(
+        room_url,
+        token,
+        "Imagebot",
+        1,
+    )
+    transport._mic_enabled = True
+    transport._camera_enabled = True
+    transport._mic_sample_rate = 16000
+    transport._camera_width = 1024
+    transport._camera_height = 1024
+
+    llm = AzureLLMService()
+    tts = AzureTTSService()
+    img = FalImageGenService()
+
+    async def handle_transcriptions():
+        print("handle_transcriptions got called")
+
+        sentence = ""
+        async for message in transport.get_transcriptions():
+            print(f"transcription message: {message}")
+            if message["session_id"] == transport._my_participant_id:
+                continue
+            finder = message["text"].find("start over")
+            print(f"finder: {finder}")
+            if finder >= 0:
+                async for audio in tts.run_tts(f"Resetting."):
+                    transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
+                sentence = ""
+                continue
+            # todo: we could differentiate between transcriptions from different participants
+            sentence += f" {message['text']}"
+            print(f"sentence is now: {sentence}")
+            # TODO: Cache this audio
+            phrase = random.choice(["OK.", "Got it.", "Sure.", "You bet.", "Sure thing."])
+            async for audio in tts.run_tts(phrase):
+                transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
+            img_result = img.run_image_gen(sentence, "1024x1024")
+            awaited_img = await asyncio.gather(img_result)
+            transport.output_queue.put(
+                [
+                    QueueFrame(FrameType.IMAGE_FRAME, awaited_img[0][1]),
+                ]
+            )
+
+    @transport.event_handler("on_participant_joined")
+    async def on_participant_joined(transport, participant):
+        print(f"participant joined: {participant['info']['userName']}")
+        if participant["info"]["isLocal"]:
+            return
+        async for audio in tts.run_tts("Describe an image, and I'll create it."):
+            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'.")
+            async for audio in audio_generator:
+                transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
+
+    transport.transcription_settings["extra"]["punctuate"] = False
+    transport.transcription_settings["extra"]["endpointing"] = False
+    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))

src/examples/internal/11a-dial-out.py

@@ -0,0 +1,135 @@
+import aiohttp
+import asyncio
+import os
+import wave
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.queue_aggregators import LLMContextAggregator
+from dailyai.services.ai_services import AIService, FrameLogger
+from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
+from typing import AsyncGenerator
+
+from examples.foundational.support.runner import configure
+
+sounds = {}
+sound_files = [
+    'ding1.wav',
+    'ding2.wav'
+]
+
+script_dir = os.path.dirname(__file__)
+
+for file in sound_files:
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, "assets", file)
+    # Get the filename without the extension to use as the dictionary key
+    filename = os.path.splitext(os.path.basename(full_path))[0]
+    # Open the image and convert it to bytes
+    with wave.open(full_path) as audio_file:
+        sounds[file] = audio_file.readframes(-1)
+
+
+
+
+class OutboundSoundEffectWrapper(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMResponseEndQueueFrame):
+            yield AudioQueueFrame(sounds["ding1.wav"])
+            # In case anything else up the stack needs it
+            yield frame
+        else:
+            yield frame
+
+class InboundSoundEffectWrapper(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMMessagesQueueFrame):
+            yield AudioQueueFrame(sounds["ding2.wav"])
+            # In case anything else up the stack needs it
+            yield frame
+        else:
+            yield frame
+
+
+async def main(room_url: str, token, phone):
+    async with aiohttp.ClientSession() as session:
+
+        global transport
+        global llm
+        global tts
+
+        transport = DailyTransportService(
+            room_url,
+            token,
+            "Respond bot",
+            300,
+        )
+        transport._mic_enabled = True
+        transport._mic_sample_rate = 16000
+        transport._camera_enabled = False
+
+        llm = AzureLLMService()
+        tts = AzureTTSService()
+
+        @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)
+            await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
+        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 = LLMContextAggregator(
+                messages, "user", transport._my_participant_id
+            )
+            tma_out = LLMContextAggregator(
+                messages, "assistant", transport._my_participant_id
+            )
+            out_sound = OutboundSoundEffectWrapper()
+            in_sound = InboundSoundEffectWrapper()
+            fl = FrameLogger("LLM Out")
+            fl2 = FrameLogger("Transcription In")
+            await out_sound.run_to_queue(
+                transport.send_queue,
+                tts.run(
+                    tma_out.run(
+                        llm.run(
+                            fl2.run(
+                                in_sound.run(
+                                    tma_in.run(
+                                        transport.get_receive_frames()
+                                    )
+                                )
+                            )
+                        )
+                    )
+                )
+            )
+
+        @transport.event_handler("on_participant_joined")
+        async def pax_joined(transport, pax):
+            print(f"PARTICIPANT JOINED: {pax}")
+
+        @transport.event_handler("on_call_state_updated")
+        async def on_call_state_updated(transport, state):
+            if (state == "joined"):
+                if (phone):
+                    transport.start_recording()
+                    transport.dialout(phone)
+
+
+        transport.transcription_settings["extra"]["punctuate"] = True
+
+        await asyncio.gather(transport.run(), handle_transcriptions())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/examples/server/Dockerfile

@@ -0,0 +1,39 @@
+# setup
+FROM python:3.11.5
+
+WORKDIR /app
+COPY requirements.txt /app
+COPY *.py /app
+COPY pyproject.toml /app
+
+COPY src/ /app/src/
+
+WORKDIR /app
+RUN ls --recursive /app/
+RUN pip3 install --upgrade -r requirements.txt
+RUN python -m build .
+RUN pip3 install .
+
+# If running on Ubuntu, Azure TTS requires some extra config
+# https://learn.microsoft.com/en-us/azure/ai-services/speech-service/quickstarts/setup-platform?pivots=programming-language-python&tabs=linux%2Cubuntu%2Cdotnetcli%2Cdotnet%2Cjre%2Cmaven%2Cnodejs%2Cmac%2Cpypi
+
+RUN wget -O - https://www.openssl.org/source/openssl-1.1.1w.tar.gz | tar zxf -
+WORKDIR openssl-1.1.1w
+RUN ./config --prefix=/usr/local
+RUN make -j $(nproc)
+RUN make install_sw install_ssldirs
+RUN ldconfig -v
+ENV SSL_CERT_DIR=/etc/ssl/certs
+
+#ENV LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
+RUN apt clean
+RUN apt-get update
+RUN apt-get -y install build-essential libssl-dev ca-certificates libasound2 wget
+
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+EXPOSE 8000
+# run
+CMD ["gunicorn", "--workers=2", "--log-level", "debug", "--capture-output", "daily-bot-manager:app", "--bind=0.0.0.0:8000"]

src/examples/server/README.md

@@ -0,0 +1,13 @@
+# Server Example
+
+This is an example server based on [Santa Cat](https://santacat.ai). You can run the server with this command:
+
+```
+flask --app daily-bot-manager.py --debug run
+```
+
+Once the server is started, you can load `http://127.0.0.1:5000/spin-up-kitty` in a browser, and the server will do the following:
+
+- Create a new, randomly-named Daily room with `DAILY_API_KEY` from your .env file or environment
+- Start the `10-wake-word.py` example and connect it to that room
+- 301 redirect your browser to the room

src/examples/server/auth.py

@@ -0,0 +1,33 @@
+import time
+import urllib
+
+from dotenv import load_dotenv
+import requests
+from flask import jsonify
+import os
+
+load_dotenv()
+
+
+def get_meeting_token(room_name, daily_api_key, token_expiry):
+    api_path = os.getenv('DAILY_API_PATH') or 'https://api.daily.co/v1'
+
+    if not token_expiry:
+        token_expiry = time.time() + 600
+    res = requests.post(
+        f'{api_path}/meeting-tokens',
+        headers={
+            'Authorization': f'Bearer {daily_api_key}'},
+        json={
+            'properties': {
+                'room_name': room_name,
+                'is_owner': True,
+                'exp': token_expiry}})
+    if res.status_code != 200:
+        return jsonify({'error': 'Unable to create meeting token', 'detail': res.text}), 500
+    meeting_token = res.json()['token']
+    return meeting_token
+
+
+def get_room_name(room_url):
+    return urllib.parse.urlparse(room_url).path[1:]

src/examples/server/daily-bot-manager.py

@@ -0,0 +1,100 @@
+import os
+import requests
+import subprocess
+import time
+
+from flask import Flask, jsonify, request, redirect
+from flask_cors import CORS
+from examples.server.auth import get_meeting_token
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+app = Flask(__name__)
+CORS(app)
+
+print(f"I loaded an environment, and my FAL_KEY_ID is {os.getenv('FAL_KEY_ID')}")
+
+
+def start_bot(bot_path, args=None):
+    daily_api_key = os.getenv("DAILY_API_KEY")
+    api_path = os.getenv("DAILY_API_PATH") or "https://api.daily.co/v1"
+
+    timeout = int(os.getenv("DAILY_ROOM_TIMEOUT") or os.getenv("DAILY_BOT_MAX_DURATION") or 300)
+    exp = time.time() + timeout
+    res = requests.post(
+        f"{api_path}/rooms",
+        headers={"Authorization": f"Bearer {daily_api_key}"},
+        json={
+            "properties": {
+                "exp": exp,
+                "enable_chat": True,
+                "enable_emoji_reactions": True,
+                "eject_at_room_exp": True,
+                "enable_prejoin_ui": False,
+                "enable_recording": "cloud"
+            }
+        },
+    )
+    if res.status_code != 200:
+        return (
+            jsonify(
+                {
+                    "error": "Unable to create room",
+                    "status_code": res.status_code,
+                    "text": res.text,
+                }
+            ),
+            500,
+        )
+    room_url = res.json()["url"]
+    room_name = res.json()["name"]
+
+    meeting_token = get_meeting_token(room_name, daily_api_key, exp)
+
+    if args:
+        extra_args = " ".join([f'-{x[0]} "{x[1]}"' for x in args])
+    else:
+        extra_args = ""
+
+    proc = subprocess.Popen(
+        [
+            f"python {bot_path} -u {room_url} -t {meeting_token} -k {daily_api_key} {extra_args}"
+        ],
+        shell=True,
+        bufsize=1,
+    )
+
+    # Don't return until the bot has joined the room, but wait for at most 2 seconds.
+    attempts = 0
+    while attempts < 20:
+        time.sleep(0.1)
+        attempts += 1
+        res = requests.get(
+            f"{api_path}/rooms/{room_name}/get-session-data",
+            headers={"Authorization": f"Bearer {daily_api_key}"},
+        )
+        if res.status_code == 200:
+            break
+    print(f"Took {attempts} attempts to join room {room_name}")
+
+    # Additional client config
+    config = {}
+    if os.getenv("CLIENT_VAD_TIMEOUT_SEC"):
+        config['vad_timeout_sec'] = float(os.getenv("DAILY_CLIENT_VAD_TIMEOUT_SEC"))
+    else:
+        config['vad_timeout_sec'] = 1.5
+
+    # return jsonify({"room_url": room_url, "token": meeting_token, "config": config}), 200
+    return redirect(room_url, code=301)
+
+
+@app.route("/spin-up-kitty", methods=["GET", "POST"])
+def spin_up_kitty():
+    return start_bot("./src/examples/foundational/10-wake-word.py")
+
+
+@app.route("/healthz")
+def health_check():
+    return "ok", 200