added examples back

examples/translation-chatbot/bot.py

@@ -0,0 +1,109 @@
+import asyncio
+import aiohttp
+import logging
+import os
+from typing import AsyncGenerator
+
+from dailyai.pipeline.aggregators import (
+    SentenceAggregator,
+)
+from dailyai.pipeline.frames import (
+    Frame,
+    LLMMessagesFrame,
+    TextFrame,
+    SendAppMessageFrame,
+)
+from dailyai.pipeline.frame_processor import FrameProcessor
+from dailyai.pipeline.pipeline import Pipeline
+from dailyai.transports.daily_transport import DailyTransport
+from dailyai.services.azure_ai_services import AzureTTSService
+from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.pipeline.aggregators import LLMFullResponseAggregator
+
+from runner import configure
+
+from dotenv import load_dotenv
+
+
+load_dotenv(override=True)
+
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
+
+"""
+This example looks a bit different than the chatbot example, because it isn't waiting on the user to stop talking to start translating.
+It also isn't saving what the user or bot says into the context object for use in subsequent interactions.
+"""
+
+
+# We need to use a custom service here to yield LLM frames without saving
+# any context
+class TranslationProcessor(FrameProcessor):
+    def __init__(self, language):
+        self._language = language
+
+    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+        if isinstance(frame, TextFrame):
+            context = [
+                {
+                    "role": "system",
+                    "content": f"You will be provided with a sentence in English, and your task is to translate it into {self._language}.",
+                },
+                {"role": "user", "content": frame.text},
+            ]
+            yield LLMMessagesFrame(context)
+        else:
+            yield frame
+
+
+class TranslationSubtitles(FrameProcessor):
+    def __init__(self, language):
+        self._language = language
+
+    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+        if isinstance(frame, TextFrame):
+            app_message = {
+                "language": self._language,
+                "text": frame.text
+            }
+            yield SendAppMessageFrame(app_message, None)
+            yield frame
+        else:
+            yield frame
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Translator",
+            duration_minutes=5,
+            start_transcription=True,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=False,
+        )
+        tts = AzureTTSService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
+            voice="es-ES-AlvaroNeural",
+        )
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4-turbo-preview"
+        )
+        sa = SentenceAggregator()
+        tp = TranslationProcessor("Spanish")
+        lfra = LLMFullResponseAggregator()
+        ts = TranslationSubtitles("spanish")
+        pipeline = Pipeline([sa, tp, llm, lfra, ts, tts])
+
+        transport.transcription_settings["extra"]["endpointing"] = True
+        transport.transcription_settings["extra"]["punctuate"] = True
+        await transport.run(pipeline)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))