move src/examples to examples

2024-03-18 11:51:06 -07:00
parent 2f4e31d1b2
commit 90c34d278f
78 changed files with 7 additions and 7 deletions
--- a/examples/foundational/05-sync-speech-and-image.py
+++ b/examples/foundational/05-sync-speech-and-image.py
@@ -0,0 +1,144 @@
+import asyncio
+from re import S
+import aiohttp
+import os
+import logging
+
+from dataclasses import dataclass
+from typing import AsyncGenerator
+
+from dailyai.pipeline.aggregators import (
+    GatedAggregator,
+    LLMFullResponseAggregator,
+    ParallelPipeline,
+    SentenceAggregator,
+)
+from dailyai.pipeline.frames import (
+    Frame,
+    TextFrame,
+    EndFrame,
+    ImageFrame,
+    LLMMessagesQueueFrame,
+    LLMResponseStartFrame,
+)
+from dailyai.pipeline.frame_processor import FrameProcessor
+
+from dailyai.pipeline.pipeline import Pipeline
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.fal_ai_services import FalImageGenService
+
+from examples.support.runner import configure
+
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
+
+
+@dataclass
+class MonthFrame(Frame):
+    month: str
+
+
+class MonthPrepender(FrameProcessor):
+    def __init__(self):
+        self.most_recent_month = "Placeholder, month frame not yet received"
+        self.prepend_to_next_text_frame = False
+
+    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+        if isinstance(frame, MonthFrame):
+            self.most_recent_month = frame.month
+        elif self.prepend_to_next_text_frame and isinstance(frame, TextFrame):
+            yield TextFrame(f"{self.most_recent_month}: {frame.text}")
+            self.prepend_to_next_text_frame = False
+        elif isinstance(frame, LLMResponseStartFrame):
+            self.prepend_to_next_text_frame = True
+            yield frame
+        else:
+            yield frame
+
+
+async def main(room_url):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransportService(
+            room_url,
+            None,
+            "Month Narration Bot",
+            mic_enabled=True,
+            camera_enabled=True,
+            mic_sample_rate=16000,
+            camera_width=1024,
+            camera_height=1024,
+        )
+
+        tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
+
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
+            model="gpt-4-turbo-preview")
+
+        imagegen = FalImageGenService(
+            image_size="square_hd",
+            aiohttp_session=session,
+            key_id=os.getenv("FAL_KEY_ID"),
+            key_secret=os.getenv("FAL_KEY_SECRET"),
+        )
+
+        gated_aggregator = GatedAggregator(
+            gate_open_fn=lambda frame: isinstance(
+                frame, ImageFrame), gate_close_fn=lambda frame: isinstance(
+                frame, LLMResponseStartFrame), start_open=False, )
+
+        sentence_aggregator = SentenceAggregator()
+        month_prepender = MonthPrepender()
+        llm_full_response_aggregator = LLMFullResponseAggregator()
+
+        pipeline = Pipeline(
+            processors=[
+                llm,
+                sentence_aggregator,
+                ParallelPipeline(
+                    [[month_prepender, tts], [llm_full_response_aggregator, imagegen]]
+                ),
+                gated_aggregator,
+            ],
+        )
+
+        frames = []
+        for month in [
+            "January",
+            "February",
+            "March",
+            "April",
+            "May",
+            "June",
+            "July",
+            "August",
+            "September",
+            "October",
+            "November",
+            "December",
+        ]:
+            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.",
+                }
+            ]
+            frames.append(MonthFrame(month))
+            frames.append(LLMMessagesQueueFrame(messages))
+
+        frames.append(EndFrame())
+        await pipeline.queue_frames(frames)
+
+        await transport.run(pipeline, override_pipeline_source_queue=False)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url))