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import gradio as gr
from huggingface_hub import InferenceClient
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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})
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
yield response
with gr.Blocks() as demo:
system_message = gr.Textbox(
label="System Message",
value="You are a helpful assistant.",
lines=2,
)
chat_history = gr.State([])
with gr.Row():
with gr.Column(scale=0.8):
chatbot = gr.Chatbot()
with gr.Column(scale=0.2):
max_tokens = gr.Slider(
minimum=1, maximum=512, step=1, value=128, label="Max Tokens"
)
temperature = gr.Slider(
minimum=0, maximum=1, step=0.01, value=0.7, label="Temperature"
)
top_p = gr.Slider(
minimum=0, maximum=1, step=0.01, value=1, label="Top-p"
)
user_input = gr.Textbox(show_label=False, placeholder="Type your message here...")
def user_interaction(message, history, system_message, max_tokens, temperature, top_p):
bot_message = next(respond(message, history, system_message, max_tokens, temperature, top_p))
history.append((message, bot_message))
return history, history
user_input.submit(
user_interaction,
inputs=[user_input, chat_history, system_message, max_tokens, temperature, top_p],
outputs=[chatbot, chat_history],
)
if __name__ == "__main__":
demo.launch()
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