Create app-backup.py

app-backup.py

@@ -0,0 +1,141 @@
+import asyncio
+import base64
+import json
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+import openai
+from dotenv import load_dotenv
+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse, StreamingResponse
+from fastrtc import (
+    AdditionalOutputs,
+    AsyncStreamHandler,
+    Stream,
+    get_twilio_turn_credentials,
+    wait_for_item,
+)
+from gradio.utils import get_space
+from openai.types.beta.realtime import ResponseAudioTranscriptDoneEvent
+
+load_dotenv()
+
+cur_dir = Path(__file__).parent
+
+SAMPLE_RATE = 24000
+
+
+class OpenAIHandler(AsyncStreamHandler):
+    def __init__(
+        self,
+    ) -> None:
+        super().__init__(
+            expected_layout="mono",
+            output_sample_rate=SAMPLE_RATE,
+            output_frame_size=480,
+            input_sample_rate=SAMPLE_RATE,
+        )
+        self.connection = None
+        self.output_queue = asyncio.Queue()
+
+    def copy(self):
+        return OpenAIHandler()
+
+    async def start_up(
+        self,
+    ):
+        """Connect to realtime API. Run forever in separate thread to keep connection open."""
+        self.client = openai.AsyncOpenAI()
+        async with self.client.beta.realtime.connect(
+            model="gpt-4o-mini-realtime-preview-2024-12-17"
+        ) as conn:
+            await conn.session.update(
+                session={"turn_detection": {"type": "server_vad"}}
+            )
+            self.connection = conn
+            async for event in self.connection:
+                if event.type == "response.audio_transcript.done":
+                    await self.output_queue.put(AdditionalOutputs(event))
+                if event.type == "response.audio.delta":
+                    await self.output_queue.put(
+                        (
+                            self.output_sample_rate,
+                            np.frombuffer(
+                                base64.b64decode(event.delta), dtype=np.int16
+                            ).reshape(1, -1),
+                        ),
+                    )
+
+    async def receive(self, frame: tuple[int, np.ndarray]) -> None:
+        if not self.connection:
+            return
+        _, array = frame
+        array = array.squeeze()
+        audio_message = base64.b64encode(array.tobytes()).decode("utf-8")
+        await self.connection.input_audio_buffer.append(audio=audio_message)  # type: ignore
+
+    async def emit(self) -> tuple[int, np.ndarray] | AdditionalOutputs | None:
+        return await wait_for_item(self.output_queue)
+
+    async def shutdown(self) -> None:
+        if self.connection:
+            await self.connection.close()
+            self.connection = None
+
+
+def update_chatbot(chatbot: list[dict], response: ResponseAudioTranscriptDoneEvent):
+    chatbot.append({"role": "assistant", "content": response.transcript})
+    return chatbot
+
+
+chatbot = gr.Chatbot(type="messages")
+latest_message = gr.Textbox(type="text", visible=False)
+stream = Stream(
+    OpenAIHandler(),
+    mode="send-receive",
+    modality="audio",
+    additional_inputs=[chatbot],
+    additional_outputs=[chatbot],
+    additional_outputs_handler=update_chatbot,
+    rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
+    concurrency_limit=5 if get_space() else None,
+    time_limit=90 if get_space() else None,
+)
+
+app = FastAPI()
+
+stream.mount(app)
+
+
+@app.get("/")
+async def _():
+    rtc_config = get_twilio_turn_credentials() if get_space() else None
+    html_content = (cur_dir / "index.html").read_text()
+    html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
+    return HTMLResponse(content=html_content)
+
+
+@app.get("/outputs")
+def _(webrtc_id: str):
+    async def output_stream():
+        import json
+
+        async for output in stream.output_stream(webrtc_id):
+            s = json.dumps({"role": "assistant", "content": output.args[0].transcript})
+            yield f"event: output\ndata: {s}\n\n"
+
+    return StreamingResponse(output_stream(), media_type="text/event-stream")
+
+
+if __name__ == "__main__":
+    import os
+
+    if (mode := os.getenv("MODE")) == "UI":
+        stream.ui.launch(server_port=7860)
+    elif mode == "PHONE":
+        stream.fastphone(host="0.0.0.0", port=7860)
+    else:
+        import uvicorn
+
+        uvicorn.run(app, host="0.0.0.0", port=7860)