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import gradio as gr
from huggingface_hub import InferenceClient
import whisper
import torch
import numpy as np
def transcribe_audio(audio):
"""Transcribe audio using Whisper model"""
if audio is None:
return ""
# Load Whisper model
model = whisper.load_model("base")
# Transcribe audio
result = model.transcribe(audio)
return result["text"]
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
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,
):
choices = message.choices
token = ""
if len(choices) and choices[0].delta.content:
token = choices[0].delta.content
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
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)",
),
],
chatbot=gr.Chatbot(height=500),
textbox=gr.Textbox(placeholder="Type your message here or use voice input...", container=False, scale=7),
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False),
examples=[
"Hello! How are you?",
"Can you help me with something?",
"Tell me a joke"
],
multimodal=True,
)
with gr.Blocks() as demo:
with gr.Sidebar():
gr.LoginButton()
gr.Markdown("## Voice Input Settings")
audio_input = gr.Audio(
sources=["microphone"],
type="filepath",
label="Record your message",
interactive=True
)
transcribe_btn = gr.Button("Transcribe Audio", variant="primary")
transcribed_text = gr.Textbox(label="Transcribed Text", interactive=False)
transcribe_btn.click(
fn=transcribe_audio,
inputs=audio_input,
outputs=transcribed_text
)
# Add transcribed text to chat
use_transcribed_btn = gr.Button("Send to Chat", variant="secondary")
use_transcribed_btn.click(
fn=lambda x: x,
inputs=transcribed_text,
outputs=chatbot.textbox
)
chatbot.render()
if __name__ == "__main__":
demo.launch() |