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Sleeping
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import os
import gradio as gr
from huggingface_hub import login
from huggingface_hub import InferenceClient
import spaces
# Retrieve API key and authenticate
api_key = os.getenv("LLAMA")
login(api_key)
# Initialize InferenceClient for the Llama model
client = InferenceClient("meta-llama/Llama-3.1-70B-Instruct")
@spaces.GPU
def respond(
message,
history: list[dict],
system_message,
max_tokens,
temperature,
top_p,
):
# Start with the system message
messages = [{"role": "system", "content": system_message}]
# Add the conversation history
messages += history
# Add the latest user message
messages.append({"role": "user", "content": message})
response = ""
# Send the conversation to the model and stream the response
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
# Initialize the Gradio ChatInterface with the new format
demo = gr.ChatInterface(
respond,
type="messages", # Use the OpenAI-style format
additional_inputs=[
gr.Textbox(
value="You are a helpful Customer Support assistant that specializes in the low-code software company: 'Plant an App' and tech-related topics.",
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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