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import gradio as gr
from smolagents import Agent # adjust the import to your actual smolagents module
# Step 1: Initialize your smolagents agent.
# Replace "zephyr-7b-beta" with your desired model name/configuration.
agent = Agent(model="zephyr-7b-beta")
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
"""
This function builds the conversation history, calls the smolagents agent,
and streams the response back to Gradio.
"""
# Build the conversation messages list, starting with the system prompt.
messages = [{"role": "system", "content": system_message}]
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
# Add the latest user input.
messages.append({"role": "user", "content": message})
# Initialize an empty response.
complete_response = ""
# Step 2: Call the agent's chat_completion method.
# If your smolagents agent supports streaming (i.e. yielding tokens as they are generated),
# this loop will yield partial responses to update the UI.
# If streaming is not supported, you can simply do:
# complete_response = agent.chat_completion(messages, max_tokens, temperature, top_p)
# yield complete_response
for token in agent.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True # set to False if your agent does not support streaming
):
complete_response += token
yield complete_response
# Step 3: Create the Gradio ChatInterface.
demo = gr.ChatInterface(
fn=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)"
),
],
)
# Step 4: Launch the Gradio app.
if __name__ == "__main__":
demo.launch()
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