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import gradio as gr
from huggingface_hub import InferenceClient
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Background effect from CodePen
background_html = """
<p class="codepen" data-height="300" data-theme-id="dark" data-default-tab="html,result" data-slug-hash="LYwLqVv" data-pen-title="WebGL Eye Pattern" data-user="bxck75" style="height: 300px; box-sizing: border-box; display: flex; align-items: center; justify-content: center; border: 2px solid; margin: 1em 0; padding: 1em;">
<span>See the Pen <a href="https://codepen.io/bxck75/pen/LYwLqVv">
WebGL Eye Pattern</a> by boudewijn (<a href="https://codepen.io/bxck75">@bxck75</a>)
on <a href="https://codepen.io">CodePen</a>.</span>
</p>
<script async src="https://cpwebassets.codepen.io/assets/embed/ei.js"></script>
"""
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:
gr.HTML(background_html) # Insert the background effect
gr.ChatInterface(
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)",
),
],
)
if __name__ == "__main__":
demo.launch()
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