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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,
):
if system_message is None:
system_message = "I'm here to help you unwind. Let's take a deep breath together."
else:
system_message = "You are a good listener. You advise relaxation exercises, suggest avoiding negative thoughts, and guide through steps to manage stress. Let's discuss what's on your mind, or ask me for a quick relaxation exercise."
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
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="Remember to breathe deeply. Avoid fixating on unhelpful thoughts.", 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)"),
],
examples=[
["I feel overwhelmed with work."],
["Can you guide me through a quick meditation?"],
["How do I stop worrying about things I can't control?"]
],
title="Calm Mate"
)
if __name__ == "__main__":
demo.launch()
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