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import os
import gradio as gr
from openai import OpenAI
from openai.error import BadRequestError
# Retrieve the Hugging Face API token from environment variables
TOKEN = os.getenv("HF_TOKEN")
if not TOKEN:
raise ValueError("Hugging Face API token (HF_TOKEN) not set in environment variables.")
client = OpenAI(
base_url="https://api-inference.huggingface.co/v1/",
api_key=TOKEN,
)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for user_message, assistant_message in history:
if user_message:
messages.append({"role": "user", "content": user_message})
if assistant_message:
messages.append({"role": "assistant", "content": assistant_message})
messages.append({"role": "user", "content": message})
try:
response = ""
for msg in client.chat.completions.create(
model="meta-llama/Meta-Llama-3.1-405B-Instruct-FP8",
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
messages=messages,
):
token = msg.choices[0].delta.content
response += token
yield response
except BadRequestError as e:
error_message = f"Error: {e}. Please ensure your Hugging Face token is valid and you have a Pro subscription."
yield error_message
# Define the Gradio interface
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", 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)"),
],
)
if __name__ == "__main__":
demo.launch()
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