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import gradio as gr
from huggingface_hub import InferenceClient
# Hugging Face inference client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
for user_msg, bot_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
response = ""
for msg in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = msg.choices[0].delta.content
response += token
yield response
# Enhanced UI with ChatGPT-like design
with gr.Blocks(theme=gr.themes.Default()) as demo:
gr.Markdown("""
<h1 style='text-align: center;'>💬 AI Chat Assistant</h1>
<p style='text-align: center;'>A sleek and interactive chatbot experience powered by AI.</p>
""")
with gr.Row():
system_message = gr.Textbox(
value="You are a helpful and intelligent assistant.",
label="System Message",
interactive=True,
show_label=False,
elem_id="system-message",
)
with gr.Row():
max_tokens = gr.Slider(
minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"
)
temperature = gr.Slider(
minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"
)
top_p = gr.Slider(
minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"
)
with gr.Box():
chatbot = gr.ChatInterface(
respond,
additional_inputs=[system_message, max_tokens, temperature, top_p],
bubble_colors=("#007AFF", "#E5E5EA"),
show_copy_button=True,
elem_id="chat-container"
)
gr.Markdown("""
<style>
#chat-container {
max-width: 800px;
margin: auto;
border-radius: 10px;
background: #f7f7f8;
padding: 20px;
}
#system-message input {
font-size: 16px;
border-radius: 8px;
}
</style>
""")
demo.launch()
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