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import gradio as gr
from huggingface_hub import InferenceClient
# Set up the client for Hugging Face Inference
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Define the respond function
def respond(message, history, 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})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
return response
# Gradio interface function for the chatbot
def gradio_interface(message, history, system_message, max_tokens, temperature, top_p):
return respond(message, history, system_message, max_tokens, temperature, top_p)
# Define the Gradio interface
demo = gr.Interface(
fn=gradio_interface,
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)"),
gr.Chatbot(label="Chat History"),
],
outputs=gr.Textbox(),
)
demo.launch(share=True)
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