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import json
import os
import time
from pathlib import Path
import anthropic
import gradio as gr
import numpy as np
from dotenv import load_dotenv
from elevenlabs import ElevenLabs
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import (
AdditionalOutputs,
ReplyOnPause,
Stream,
WebRTCError,
get_tts_model,
get_twilio_turn_credentials,
)
from fastrtc.utils import audio_to_bytes
from gradio.utils import get_space
from groq import Groq
from pydantic import BaseModel
load_dotenv()
groq_client = Groq()
claude_client = anthropic.Anthropic()
tts_client = ElevenLabs(api_key=os.environ["ELEVENLABS_API_KEY"])
curr_dir = Path(__file__).parent
tts_model = get_tts_model()
def response(
audio: tuple[int, np.ndarray],
chatbot: list[dict] | None = None,
):
try:
chatbot = chatbot or []
messages = [{"role": d["role"], "content": d["content"]} for d in chatbot]
prompt = groq_client.audio.transcriptions.create(
file=("audio-file.mp3", audio_to_bytes(audio)),
model="whisper-large-v3-turbo",
response_format="verbose_json",
).text
print("prompt", prompt)
chatbot.append({"role": "user", "content": prompt})
yield AdditionalOutputs(chatbot)
messages.append({"role": "user", "content": prompt})
response = claude_client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=512,
messages=messages, # type: ignore
)
response_text = " ".join(
block.text # type: ignore
for block in response.content
if getattr(block, "type", None) == "text"
)
chatbot.append({"role": "assistant", "content": response_text})
start = time.time()
print("starting tts", start)
for i, chunk in enumerate(tts_model.stream_tts_sync(response_text)):
print("chunk", i, time.time() - start)
yield chunk
print("finished tts", time.time() - start)
yield AdditionalOutputs(chatbot)
except Exception as e:
raise WebRTCError(str(e))
chatbot = gr.Chatbot(type="messages")
stream = Stream(
modality="audio",
mode="send-receive",
handler=ReplyOnPause(response),
additional_outputs_handler=lambda a, b: b,
additional_inputs=[chatbot],
additional_outputs=[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,
)
class Message(BaseModel):
role: str
content: str
class InputData(BaseModel):
webrtc_id: str
chatbot: list[Message]
app = FastAPI()
stream.mount(app)
@app.get("/")
async def _():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = (curr_dir / "index.html").read_text()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content, status_code=200)
@app.post("/input_hook")
async def _(body: InputData):
stream.set_input(body.webrtc_id, body.model_dump()["chatbot"])
return {"status": "ok"}
@app.get("/outputs")
def _(webrtc_id: str):
async def output_stream():
async for output in stream.output_stream(webrtc_id):
chatbot = output.args[0]
yield f"event: output\ndata: {json.dumps(chatbot[-1])}\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, server_name="0.0.0.0")
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)
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