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import torch
import gradio as gr
from dataclasses import asdict
from smolagents import CodeAgent, TransformersModel, InferenceClientModel, stream_to_gradio
from transformers import BitsAndBytesConfig
from tools import get_weather, CurrencyConverterTool
model_path = "Qwen/Qwen3-4B-Instruct-2507"
cuda = torch.cuda.is_available()
if cuda:
print("\nRunning on Local GPU\n")
else:
print("\nRunning on Hugging Face Ecosystem\n")
def interact_with_agent(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
if cuda:
quantization = BitsAndBytesConfig(load_in_8bit=True)
model = TransformersModel(
model_id=model_path,
max_new_tokens=1024,
temperature=temperature,
hf_token=hf_token.token,
top_p=top_p,
max_tokens=max_tokens,
system_message=system_message,
model_kwargs={
"quantization_config": quantization
})
else:
model = InferenceClientModel(
token=hf_token.token,
model_id=model_path
)
agent = CodeAgent(
tools=[
get_weather,
CurrencyConverterTool()
],
model=model,
max_steps=8,
verbosity_level=2,
add_base_tools=True
)
messages = []
yield messages
for msg in stream_to_gradio(agent, message):
messages.append(asdict(msg))
yield messages
yield messages
chatbot = gr.ChatInterface(
interact_with_agent,
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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