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import os | |
import time | |
import requests | |
import gradio as gr | |
import numpy as np | |
from dotenv import load_dotenv | |
from elevenlabs import ElevenLabs | |
from fastapi import FastAPI | |
from fastrtc import ( | |
AdditionalOutputs, | |
ReplyOnPause, | |
Stream, | |
get_stt_model, | |
get_twilio_turn_credentials, | |
) | |
from gradio.utils import get_space | |
from numpy.typing import NDArray | |
# Load environment variables | |
load_dotenv() | |
# Initialize DeepSeek client | |
class DeepSeekAPI: | |
def __init__(self, api_key): | |
self.api_key = api_key | |
def chat_completion(self, messages, temperature=0.7, max_tokens=512): | |
url = "https://api.deepseek.com/v1/chat/completions" | |
headers = { | |
"Content-Type": "application/json", | |
"Authorization": f"Bearer {self.api_key}" | |
} | |
payload = { | |
"model": "deepseek-chat", | |
"messages": messages, | |
"temperature": temperature, | |
"max_tokens": max_tokens | |
} | |
response = requests.post(url, json=payload, headers=headers) | |
# Check for error response | |
if response.status_code != 200: | |
print(f"DeepSeek API error: {response.status_code} - {response.text}") | |
return {"choices": [{"message": {"content": "I'm sorry, I encountered an error processing your request."}}]} | |
return response.json() | |
# Initialize clients | |
deepseek_client = DeepSeekAPI(api_key=os.getenv("DEEPSEEK_API_KEY")) | |
tts_client = ElevenLabs(api_key=os.getenv("ELEVENLABS_API_KEY")) | |
stt_model = get_stt_model() | |
# Set up Twilio credentials for WebRTC | |
# The function doesn't accept keyword arguments, it reads from env vars directly | |
twilio_credentials = get_twilio_turn_credentials() | |
# Log Twilio status | |
if twilio_credentials: | |
print("Twilio TURN credentials successfully configured") | |
else: | |
print("No Twilio credentials found or invalid credentials") | |
# Handler function for voice conversation | |
def response( | |
audio: tuple[int, NDArray[np.int16 | np.float32]], | |
chatbot: list[dict] | None = None, | |
): | |
chatbot = chatbot or [] | |
messages = [{"role": d["role"], "content": d["content"]} for d in chatbot] | |
start = time.time() | |
text = stt_model.stt(audio) | |
print("transcription", time.time() - start) | |
print("prompt", text) | |
chatbot.append({"role": "user", "content": text}) | |
yield AdditionalOutputs(chatbot) | |
messages.append({"role": "user", "content": text}) | |
# Replace Groq LLM with DeepSeek | |
response_data = deepseek_client.chat_completion( | |
messages=messages, | |
max_tokens=512 | |
) | |
response_text = response_data["choices"][0]["message"]["content"] | |
chatbot.append({"role": "assistant", "content": response_text}) | |
for chunk in tts_client.text_to_speech.convert_as_stream( | |
text=response_text, | |
voice_id="JBFqnCBsd6RMkjVDRZzb", | |
model_id="eleven_multilingual_v2", | |
output_format="pcm_24000", | |
): | |
audio_array = np.frombuffer(chunk, dtype=np.int16).reshape(1, -1) | |
yield (24000, audio_array) | |
yield AdditionalOutputs(chatbot) | |
# Create the chatbot and Stream components | |
chatbot = gr.Chatbot(type="messages") | |
stream = Stream( | |
modality="audio", | |
mode="send-receive", | |
handler=ReplyOnPause(response, input_sample_rate=16000), | |
additional_outputs_handler=lambda a, b: b, | |
additional_inputs=[chatbot], | |
additional_outputs=[chatbot], | |
rtc_configuration=twilio_credentials, # Always use Twilio credentials | |
concurrency_limit=5 if get_space() else None, | |
time_limit=90 if get_space() else None, | |
ui_args={"title": "LLM Voice Chat (Powered by DeepSeek, ElevenLabs, and WebRTC ⚡️)"}, | |
) | |
# Mount the STREAM UI to the FastAPI app | |
app = FastAPI() | |
app = gr.mount_gradio_app(app, stream.ui, path="/") | |
if __name__ == "__main__": | |
import os | |
os.environ["GRADIO_SSR_MODE"] = "false" | |
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: | |
stream.ui.launch(server_port=7860) | |