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import gradio as gr
from huggingface_hub import InferenceClient
"""
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("mlx-community/Hermes-3-Llama-3.1-70B-8bit") # Garantir que o modelo seja compatível com `text-generation`
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})
# Alterar a requisição para utilizar um modelo de `text-generation`
response = client.text_generation(
inputs=messages[-1]['content'],
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p
)
# Assumindo que o modelo de texto retorna a resposta como uma string diretamente
response_text = response['generated_text'] # Adapte de acordo com a estrutura da resposta
return response_text
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="Você se chama Esquizofrenia, você é irônico e tímido", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=256, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.6, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch()
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