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import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the InferenceClient with the specified model
client = InferenceClient("WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B")
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 = ""
try:
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
except Exception as e:
yield f"An error occurred: {str(e)}"
# Define the system message with a cybersecurity focus
system_message = (
"You are a cybersecurity expert chatbot, providing assistance on penetration testing, ransomware analysis, and code classification. "
"Your responses should be concise, accurate, and tailored to cybersecurity professionals."
)
# Create the Gradio interface with dark/light mode toggle
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(value=system_message, 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)"),
gr.Checkbox(label="Dark Mode", value=False), # Dark mode toggle
],
outputs=[gr.Textbox()],
theme="dark", # Default theme
)
def toggle_theme(dark_mode):
"""Toggle between dark and light themes based on user input."""
return "dark" if dark_mode else "light"
# Update the theme based on the checkbox value
demo.change(fn=toggle_theme, inputs=[demo.inputs[4]], outputs=[demo])
if __name__ == "__main__":
demo.launch()
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