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import gradio as gr
from huggingface_hub import InferenceClient
import os
from gtts import gTTS
import whisper
import io
from tempfile import NamedTemporaryFile
api = os.getenv("HF_API_TOKEN")
client = InferenceClient("meta-llama/Meta-Llama-3.1-70B-Instruct", token=f"{api}")
# Load Whisper model
model = whisper.load_model("base") # or use 'small', 'medium', 'large', depending on your needs
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
def text_to_speech(text):
tts = gTTS(text=text, lang='en')
with NamedTemporaryFile(delete=True) as tmpfile:
tts.save(tmpfile.name)
with open(tmpfile.name, "rb") as f:
return f.read()
def speech_to_text(audio):
# Load audio data into a temporary file
with NamedTemporaryFile(delete=True, suffix=".wav") as tmpfile:
tmpfile.write(audio)
tmpfile.flush()
# Transcribe audio with Whisper
result = model.transcribe(tmpfile.name)
return result['text']
def process_audio(audio, system_message, max_tokens, temperature, top_p):
text = speech_to_text(audio)
response_gen = respond(
message=text,
history=[],
system_message=system_message,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
response_text = next(response_gen)
audio_response = text_to_speech(response_text)
return audio_response
demo = gr.Interface(
fn=process_audio,
inputs=[
gr.Audio(source="microphone", type="bytes"),
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
outputs=gr.Audio(type="bytes"),
)
if __name__ == "__main__":
demo.launch()
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