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import gradio as gr
from huggingface_hub import InferenceClient
import torch
from TTS.api import TTS
import soundfile as sf
# Load TTS Model
tts_model = TTS("tts_models/en/ljspeech/tacotron2-DDC").to("cuda" if torch.cuda.is_available() else "cpu")
# Hugging Face LLM client (DeepSeek R1 7B)
client = InferenceClient("deepseek-ai/deepseek-r1-7b")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response, None # Yielding text response first
# Generate speech from text response
output_audio_path = "response.wav"
tts_model.tts_to_file(text=response, file_path=output_audio_path)
yield response, output_audio_path # Yielding audio response
# Using gr.Blocks() instead of ChatInterface
with gr.Blocks() as demo:
gr.Markdown("## DeepSeek R1 7B Chatbot with TTS")
chatbot = gr.Chatbot()
msg = gr.Textbox(label="User Input")
system_msg = gr.Textbox(value="You are a friendly Chatbot.", label="System Message")
max_tokens = gr.Slider(1, 2048, value=512, step=1, label="Max Tokens")
temperature = gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature")
top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p (Nucleus Sampling)")
tts_audio = gr.Audio(type="filepath", label="TTS Output")
def chat_fn(message, history):
return respond(message, history, system_msg.value, max_tokens.value, temperature.value, top_p.value)
msg.submit(chat_fn, inputs=[msg, chatbot], outputs=[chatbot, tts_audio])
demo.launch()
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