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import gradio as gr
import requests
import os
#import subprocess
#subprocess.run(["huggingface-cli", "login", "--token", HUGGINGFACE_API_TOKEN_V])
TOKEN = os.getenv("HUGGINGFACE_API_TOKEN_V")
# Check if the API token is set
if not TOKEN:
raise ValueError("API token is not set. Please set the HUGGINGFACE_API_TOKEN environment variable.")
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})
headers = {
"Authorization": f"Bearer {TOKEN}",
"Content-Type": "application/json"
}
payload = {
"model": "meta-llama/Meta-Llama-3.1-405B-Instruct",
"max_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"messages": messages
}
response = requests.post("https://api-inference.huggingface.co/v1/chat/completions", headers=headers, json=payload, stream=True)
response_text = ""
for chunk in response.iter_content(chunk_size=None):
if chunk:
response_text += chunk.decode('utf-8')
yield response_text
theme="Nymbo/Nymbo_Theme"
demo = gr.ChatInterface(
respond,
theme=theme,
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() |