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import gradio as gr
from huggingface_hub import InferenceClient
import os
import base64
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Base64 encode image
def image_to_base64(image_path):
try:
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
except:
return ""
# Header HTML
def get_header_html():
if os.path.exists("static/aivn_logo.png"):
logo_base64 = image_to_base64("static/aivn_logo.png")
logo_html = f"""
<img src="data:image/png;base64,{logo_base64}"
alt="Logo"
style="height: 120px; width: auto; margin-right: 20px; margin-bottom: 20px;">
"""
else:
logo_html = "<h2>AI VIETNAM</h2>"
return f"""
<div style="display: flex; align-items: center; padding: 20px;">
<div>{logo_html}</div>
<div style="margin-left: 20px;">
<h1>🧠 LLM Chatbot</h1>
<p>Conversational Assistant</p>
<p style="color: #555;">🚀 AIO2024 Module 10 LLM Chatbot with RLHF</p>
</div>
</div>
"""
# Footer HTML
def get_footer_html():
return """
<div style="
position: fixed;
bottom: 0;
left: 0;
width: 100%;
background: white;
padding: 10px;
text-align: center;
box-shadow: 0 -2px 10px rgba(0,0,0,0.1);
z-index: 1000;
font-size: 14px;
">
Created by <a href="https://vlai.work" target="_blank" style="color: #007BFF;">VLAI</a> • AI VIETNAM
</div>
"""
# Chat function
def respond(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
for msg in history:
messages.append(msg)
messages.append({"role": "user", "content": message})
response = ""
for chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = chunk.choices[0].delta.content
response += token
yield {"role": "assistant", "content": response}
# UI
chat = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Textbox(value="You are a friendly chatbot.", label="System message"),
gr.Slider(1, 2048, value=512, step=1, label="Max new tokens"),
gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
],
type="messages",
)
# Combine with layout elements
with gr.Blocks(css=".gradio-container {min-height: 100vh;}") as demo:
gr.HTML(get_header_html())
chat.render()
gr.HTML(get_footer_html())
if __name__ == "__main__":
os.makedirs("static", exist_ok=True)
demo.launch(server_name="0.0.0.0", server_port=7860)
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