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import gradio as gr
from huggingface_hub import InferenceClient
from gtts import gTTS
import os
# Inicializando o cliente da Hugging Face com o modelo de linguagem
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Função para gerar a resposta e o áudio
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_text = ""
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_text += token
# Convertendo o texto em fala
tts = gTTS(response_text, lang='pt')
audio_file = "response.mp3"
tts.save(audio_file)
return response_text, audio_file
# Interface do Gradio usando 'gr.Interface' para múltiplos tipos de saída
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(label="User Input", placeholder="Digite sua mensagem aqui..."),
gr.State([]), # Histórico da conversa
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.Textbox(label="Chatbot Response"),
gr.Audio(label="Response in Audio")
],
title="Chatbot com TTS",
description="Digite uma mensagem e o chatbot responderá com texto e voz."
)
if __name__ == "__main__":
demo.launch()
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