Spaces:
Sleeping
Sleeping
File size: 1,844 Bytes
142e103 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | import os
import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the Hugging Face Inference Client
# Make sure to add your HF_TOKEN in the Space Settings if it's a gated model
client = InferenceClient(
model="meta-llama/Llama-3.3-70B-Instruct",
token=os.getenv("HF_TOKEN")
)
def respond(message, chat_history, system_message, max_tokens, temperature, top_p):
# Format the chat history for the conversational model
messages = [{"role": "system", "content": system_message}]
for val in chat_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 = ""
# Stream the response back from the Llama 3.3 model
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
if token:
response += token
yield response
# Define a clean Gradio Chat Interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a helpful, smart AI assistant powered by Llama 3.3.", 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"),
],
title="Llama 3.3 70B Instruct - Agent Demo",
description="A simple conversational agent interface leveraging Meta's Llama-3.3-70B-Instruct model.",
)
if __name__ == "__main__":
demo.launch()
|