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import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the Hugging Face Inference Client
client = InferenceClient(model="meta-llama/Meta-Llama-3.1-405B-FP8")
# Define the response generation function
def respond(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
# Add previous messages to the conversation
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Add the new user message
messages.append({"role": "user", "content": message})
response = ""
# Generate the response using the model
for message in client.chat_completion(
messages=messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
# Define the ChatGPT-like interface
with gr.Blocks(css=".gradio-container {max-width: 900px; margin: auto;}") as demo:
gr.Markdown("<h1 style='text-align: center;'>ChatGPT-like Interface</h1>")
chatbot = gr.Chatbot(height=500)
with gr.Row():
with gr.Column(scale=6):
msg = gr.Textbox(
show_label=False,
placeholder="Type your message here...",
container=False
).style(container=False)
with gr.Column(scale=1, min_width=100):
send_btn = gr.Button("Send").style(full_width=True)
with gr.Accordion("Settings", open=False):
system_message = gr.Textbox(value="You are a friendly Chatbot.", label="System message")
max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new 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 (nucleus sampling)")
def user_interaction(user_message, history, system_message, max_tokens, temperature, top_p):
history = history or []
history.append((user_message, None))
bot_response = respond(user_message, history, system_message, max_tokens, temperature, top_p)
history[-1] = (user_message, next(bot_response))
for token in bot_response:
history[-1] = (user_message, token)
yield history
msg.submit(user_interaction, [msg, chatbot, system_message, max_tokens, temperature, top_p], chatbot)
send_btn.click(user_interaction, [msg, chatbot, system_message, max_tokens, temperature, top_p], chatbot)
send_btn.click(lambda: "", None, msg) # Clear the input box after sending
if __name__ == "__main__":
demo.launch()
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