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import gradio as gr
from huggingface_hub import InferenceClient
def respond(
message,
history,
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
"""
Função de resposta usando Hugging Face Inference API.
"""
# Use o token diretamente se estiver testando localmente
client = InferenceClient(token=hf_token.token, model="apple/FastVLM-7B")
messages = [{"role": "system", "content": system_message}]
if history:
for h in history:
if isinstance(h, tuple) and len(h) == 2:
user_msg, bot_msg = h
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
response = ""
try:
for message_chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
if hasattr(message_chunk, "choices") and message_chunk.choices:
delta = message_chunk.choices[0].delta
if delta and hasattr(delta, "content"):
response += delta.content
yield response
except Exception as e:
yield f"Erro durante a execução: {str(e)}"
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)"),
],
)
with gr.Blocks() as demo:
with gr.Sidebar():
gr.LoginButton()
chatbot.render()
if __name__ == "__main__":
demo.launch()
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