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import os
import gradio as gr
from huggingface_hub import InferenceClient
MODEL_ID = "unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF"
# Pega o token do secret HF_TOKEN que você adicionou no Space
token = os.environ.get("HF_TOKEN")
# Inicializa o cliente; se token for None, InferenceClient tentará usar o token local/config.
client = InferenceClient(model=MODEL_ID, token=token)
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})
# MODE: escolha "stream_mode = True" para token por token, ou False para resposta completa de uma vez
stream_mode = True
if stream_mode:
response = ""
# stream=True entrega chunks — iteramos e extraímos 'content' do delta
for chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
# chunk pode ser dataclass/obj ou dict-like; tentamos extrair o texto com segurança
token_piece = ""
try:
delta = chunk.choices[0].delta
if isinstance(delta, dict):
token_piece = delta.get("content", "") or ""
else:
# objeto dataclass-like
token_piece = getattr(delta, "content", "") or ""
except Exception:
# fallback genérico (caso a API retorne formato diferente)
token_piece = str(chunk)
response += token_piece
yield response
else:
# Sem streaming: recupera a resposta completa
completion = client.chat_completion(
messages,
max_tokens=max_tokens,
stream=False,
temperature=temperature,
top_p=top_p,
)
# conforme docs, a resposta completa aparece em:
text = completion.choices[0].message.content
yield text
demo = gr.ChatInterface(
respond,
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)"),
],
)
if __name__ == "__main__":
demo.launch()
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