mirror of
https://github.com/HumanAIGC-Engineering/gradio-webrtc.git
synced 2026-02-05 18:09:23 +08:00
837330dcd8
* Turn integration * Add code: * type hint * Fix typehint * add code * format * WIP * trickle ice * bump version * Better docs * Modify * code * Mute icon for whisper * Add code * llama 4 demo * code * OpenAI interruptions * fix docs
121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
import json
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import os
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from pathlib import Path
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import gradio as gr
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import numpy as np
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from dotenv import load_dotenv
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from fastapi import FastAPI
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from fastapi.responses import HTMLResponse, StreamingResponse
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from fastrtc import (
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AdditionalOutputs,
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ReplyOnPause,
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Stream,
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audio_to_bytes,
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get_cloudflare_turn_credentials_async,
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get_current_context,
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get_tts_model,
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)
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from groq import Groq
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from numpy.typing import NDArray
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curr_dir = Path(__file__).parent
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load_dotenv()
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tts_model = get_tts_model()
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groq = Groq(api_key=os.getenv("GROQ_API_KEY"))
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conversations: dict[str, list[dict[str, str]]] = {}
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def response(user_audio: tuple[int, NDArray[np.int16]]):
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context = get_current_context()
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if context.webrtc_id not in conversations:
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conversations[context.webrtc_id] = [
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{
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"role": "system",
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"content": (
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"You are a helpful assistant that can answer questions and help with tasks."
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'Please return a short (that will be converted to audio using a text-to-speech model) response and long response to this question. They can be the same if appropriate. Please return in JSON format\n\n{"short":, "long"}\n\n'
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),
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}
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]
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messages = conversations[context.webrtc_id]
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transcription = groq.audio.transcriptions.create(
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file=("audio.wav", audio_to_bytes(user_audio)),
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model="distil-whisper-large-v3-en",
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response_format="verbose_json",
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)
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print(transcription.text)
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messages.append({"role": "user", "content": transcription.text})
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completion = groq.chat.completions.create( # type: ignore
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model="meta-llama/llama-4-scout-17b-16e-instruct",
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messages=messages, # type: ignore
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temperature=1,
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max_completion_tokens=1024,
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top_p=1,
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stream=False,
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response_format={"type": "json_object"},
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stop=None,
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)
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response = completion.choices[0].message.content
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response = json.loads(response)
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short_response = response["short"]
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long_response = response["long"]
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messages.append({"role": "assistant", "content": long_response})
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conversations[context.webrtc_id] = messages
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yield from tts_model.stream_tts_sync(short_response)
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yield AdditionalOutputs(messages)
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stream = Stream(
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ReplyOnPause(response),
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modality="audio",
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mode="send-receive",
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additional_outputs=[gr.Chatbot(type="messages")],
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additional_outputs_handler=lambda old, new: new,
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rtc_configuration=get_cloudflare_turn_credentials_async,
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)
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app = FastAPI()
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stream.mount(app)
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@app.get("/")
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async def _():
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rtc_config = await get_cloudflare_turn_credentials_async()
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html_content = (curr_dir / "index.html").read_text()
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html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
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return HTMLResponse(content=html_content)
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@app.get("/outputs")
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async def _(webrtc_id: str):
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async def output_stream():
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async for output in stream.output_stream(webrtc_id):
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state = output.args[0]
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for msg in state[-2:]:
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data = {
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"message": msg,
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}
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yield f"event: output\ndata: {json.dumps(data)}\n\n"
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return StreamingResponse(output_stream(), media_type="text/event-stream")
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if __name__ == "__main__":
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import os
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if (mode := os.getenv("MODE")) == "UI":
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stream.ui.launch(server_port=7860)
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elif mode == "PHONE":
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raise ValueError("Phone mode not supported")
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else:
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=7860)
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