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import gradio as gr
from huggingface_hub import InferenceClient
import os
# Ensure the required library is installed
os.system("pip install minijinja gradio huggingface_hub")
# Initialize the client with the desired model
client = InferenceClient("meta-llama/Meta-Llama-3.1-8B")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [system_message]
for val in history:
if val[0]:
messages.append(val[0])
if val[1]:
messages.append(val[1])
messages.append(message)
response = ""
try:
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
except Exception as e:
yield f"Error: {str(e)}"
def autocomplete(prompt, max_tokens, temperature, top_p):
messages = [prompt]
response = ""
try:
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
except Exception as e:
yield f"Error: {str(e)}"
# Create the Gradio interface
demo = gr.Blocks()
with demo:
gr.Markdown("# Chat with Meta-Llama")
with gr.Tab("Chat Interface"):
chatbot = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="", 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)",
),
],
)
with gr.Tab("Notebook Interface"):
gr.Markdown("## Notebook Interface with Autocomplete")
prompt = gr.Textbox(label="Enter your text")
output = gr.Textbox(label="Autocompleted Text", interactive=False)
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)")
autocomplete_button = gr.Button("Autocomplete")
autocomplete_button.click(
autocomplete,
inputs=[prompt, max_tokens, temperature, top_p],
outputs=output
)
if __name__ == "__main__":
demo.launch() |