added examples back

examples/storytelling-chatbot/src/bot.py

@@ -0,0 +1,172 @@
+import asyncio
+import aiohttp
+import logging
+import os
+import argparse
+
+from dailyai.pipeline.pipeline import Pipeline
+from dailyai.pipeline.frames import (
+    AudioFrame,
+    ImageFrame,
+    EndPipeFrame,
+    LLMMessagesFrame,
+    SendAppMessageFrame
+)
+from dailyai.pipeline.aggregators import (
+    LLMUserResponseAggregator,
+    LLMAssistantResponseAggregator,
+)
+from dailyai.transports.daily_transport import DailyTransport
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.services.fal_ai_services import FalImageGenService
+
+from processors import StoryProcessor, StoryImageProcessor
+from prompts import LLM_BASE_PROMPT, LLM_INTRO_PROMPT, CUE_USER_TURN
+from utils.helpers import load_sounds, load_images
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logging.basicConfig(format=f"[STORYBOT] %(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.INFO)
+
+
+sounds = load_sounds(["listening.wav"])
+images = load_images(["book1.png", "book2.png"])
+
+
+async def main(room_url, token=None):
+    async with aiohttp.ClientSession() as session:
+
+        # -------------- Transport --------------- #
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Storytelling Bot",
+            duration_minutes=5,
+            start_transcription=True,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            vad_enabled=True,
+            camera_framerate=30,
+            camera_bitrate=680000,
+            camera_enabled=True,
+            camera_width=768,
+            camera_height=768,
+        )
+
+        logger.debug("Transport created for room:" + room_url)
+
+        # -------------- Services --------------- #
+
+        llm_service = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4-turbo"
+        )
+
+        tts_service = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
+
+        fal_service_params = FalImageGenService.InputParams(
+            image_size={
+                "width": 768,
+                "height": 768
+            }
+        )
+
+        fal_service = FalImageGenService(
+            aiohttp_session=session,
+            model="fal-ai/fast-lightning-sdxl",
+            params=fal_service_params,
+            key=os.getenv("FAL_KEY"),
+        )
+
+        # --------------- Setup ----------------- #
+
+        message_history = [LLM_BASE_PROMPT]
+        story_pages = []
+
+        # We need aggregators to keep track of user and LLM responses
+        llm_responses = LLMAssistantResponseAggregator(message_history)
+        user_responses = LLMUserResponseAggregator(message_history)
+
+        # -------------- Processors ------------- #
+
+        story_processor = StoryProcessor(message_history, story_pages)
+        image_processor = StoryImageProcessor(fal_service)
+
+        # -------------- Story Loop ------------- #
+
+        logger.debug("Waiting for participant...")
+
+        start_storytime_event = asyncio.Event()
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport, participant):
+            logger.debug("Participant joined, storytime commence!")
+            start_storytime_event.set()
+
+        # The storytime coroutine will wait for the start_storytime_event
+        # to be set before starting the storytime pipeline
+        async def storytime():
+            await start_storytime_event.wait()
+
+            # The intro pipeline is used to start
+            # the story (as per LLM_INTRO_PROMPT)
+            intro_pipeline = Pipeline(processors=[
+                llm_service,
+                tts_service,
+            ], sink=transport.send_queue)
+
+            await intro_pipeline.queue_frames(
+                [
+                    ImageFrame(images['book1'], (768, 768)),
+                    LLMMessagesFrame([LLM_INTRO_PROMPT]),
+                    SendAppMessageFrame(CUE_USER_TURN, None),
+                    AudioFrame(sounds["listening"]),
+                    ImageFrame(images['book2'], (768, 768)),
+                    EndPipeFrame(),
+                ]
+            )
+
+            # We start the pipeline as soon as the user joins
+            await intro_pipeline.run_pipeline()
+
+            # The main story pipeline is used to continue the
+            # story based on user input
+            pipeline = Pipeline(processors=[
+                user_responses,
+                llm_service,
+                story_processor,
+                image_processor,
+                tts_service,
+                llm_responses,
+            ])
+
+            await transport.run_pipeline(pipeline)
+
+        transport.transcription_settings["extra"]["endpointing"] = True
+        transport.transcription_settings["extra"]["punctuate"] = True
+
+        try:
+            await asyncio.gather(transport.run(), storytime())
+        except (asyncio.CancelledError, KeyboardInterrupt):
+            transport.stop()
+
+        logger.debug("Pipeline finished. Exiting.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Daily Storyteller Bot")
+    parser.add_argument("-u", type=str, help="Room URL")
+    parser.add_argument("-t", type=str, help="Token")
+    config = parser.parse_args()
+
+    asyncio.run(main(config.u, config.t))