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import gradio as gr
from huggingface_hub import InferenceClient
# Connect to Hugging Face model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Define the chat response function
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
# Gradio interface layout
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
],
css=".gradio-container {background-color: #ffeef8;} .gr-button {background-color: #ff88cc; color: white; border-radius: 12px;} .gr-input {border: 2px solid #ff66b2; border-radius: 10px;} .gr-output {border: 2px solid #ff66b2; border-radius: 10px; padding: 10px;} .gr-button:hover {background-color: #ff66b2;} .gr-textbox {background-color: #fff1f5;}",
theme="huggingface", # Hugging Face's default theme
title="Cuddly Chatbot 🐾", # Title for the app
description="Welcome to the most adorable chatbot! 💖",
)
if __name__ == "__main__":
demo.launch()
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