Delete app.py

app.py

@@ -1,151 +0,0 @@
-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"},
-                    "system_instruction": "당신은 MOUSE 챗이라는 AI 어시스턴트입니다. 사용자의 질문에 친절하고 명확하게 답변해주세요. 한국어로 대화를 나누며, 필요한 경우 정보를 상세히 제공합니다. 당신의 이름은 'MOUSE 챗'입니다."
-                }
-            )
-            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
-        try:
-            _, 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
-        except Exception as e:
-            # 연결이 이미 닫혔거나 다른 오류가 발생한 경우 로그만 남기고 계속 진행
-            print(f"Error in receive: {e}")
-            # 연결이 끊어진 것 같으면 connection을 None으로 설정
-            if "ConnectionClosed" in str(e):
-                self.connection = None
-
-    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)