Spaces:
Sleeping
Sleeping
File size: 1,398 Bytes
abbab7a 404886a abbab7a 404886a 6161aaf 404886a abbab7a 404886a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
import os
import gradio as gr
from huggingface_hub import InferenceClient
MODEL_ID = "unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF"
token = os.environ.get("HF_TOKEN")
client = InferenceClient(model=MODEL_ID, token=token)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Monta o prompt tipo chat manualmente
prompt = f"{system_message}\n\n"
for user_msg, bot_msg in history:
if user_msg:
prompt += f"User: {user_msg}\n"
if bot_msg:
prompt += f"Assistant: {bot_msg}\n"
prompt += f"User: {message}\nAssistant:"
# Chama o endpoint de geração de texto normal, sem streaming
response = client.text_generation(
prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
# A resposta vem como string simples
yield response
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()
|