Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,605 Bytes
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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import json
from globe import title, description, customtool , presentation1, presentation2, joinus
import spaces
model_path = "nvidia/Nemotron-Mini-4B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
# Create a pipeline
pipe = pipeline("text-generation", model=model_path)
pipe.tokenizer = tokenizer # Assign tokenizer manually
def create_prompt(system_message, user_message, tool_definition="", context=""):
if tool_definition:
return f"""<extra_id_0>System
{system_message}
<tool>
{tool_definition}
</tool>
<context>
{context}
</context>
<extra_id_1>User
{user_message}
<extra_id_1>Assistant
"""
else:
return f"<extra_id_0>System\n{system_message}\n\n<extra_id_1>User\n{user_message}\n<extra_id_1>Assistant\n"
@spaces.GPU
def generate_response(message, history, system_message, max_tokens, temperature, top_p, use_pipeline=False, tool_definition="", context=""):
full_prompt = create_prompt(system_message, message, tool_definition, context)
if use_pipeline:
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": message},
]
response = pipe(messages, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p)[0]['generated_text']
else:
tokenized_chat = tokenizer.apply_chat_template(
[
{"role": "system", "content": system_message},
{"role": "user", "content": message},
],
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
with torch.no_grad():
output_ids = model.generate(
tokenized_chat,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True
)
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
assistant_response = response.split("<extra_id_1>Assistant\n")[-1].strip()
if tool_definition and "<toolcall>" in assistant_response:
tool_call = assistant_response.split("<toolcall>")[1].split("</toolcall>")[0]
assistant_response += f"\n\nTool Call: {tool_call}\n\nNote: This is a simulated tool call. In a real scenario, the tool would be executed and its output would be used to generate a final response."
return assistant_response
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown(title)
with gr.Row():
gr.Markdown(description)
with gr.Row():
with gr.Group():
gr.Markdown(presentation1)
with gr.Group():
gr.Markdown(presentation2)
with gr.Row():
gr.Markdown(joinus)
with gr.Row():
with gr.Column(scale=3):
chatbot = gr.Chatbot(height=400)
msg = gr.Textbox(label="User Input", placeholder="Ask a question or request a task...")
with gr.Accordion(label="🧪Advanced Settings", open=False):
system_message = gr.Textbox(
label="System Message",
value="You are a helpful AI assistant.",
lines=2,
placeholder="Set the AI's behavior and context..."
)
context = gr.Textbox(
label="Context",
lines=2,
placeholder="Enter additional context information..."
)
max_tokens = gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens")
temperature = gr.Slider(minimum=0.1, maximum=2.0, 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")
use_pipeline = gr.Checkbox(label="Use Pipeline", value=False)
use_tool = gr.Checkbox(label="Use Function Calling", value=False)
with gr.Column(visible=False) as tool_options:
tool_definition = gr.Code(
label="Tool Definition (JSON)",
value=customtool,
lines=15,
language="json"
)
with gr.Row():
clear = gr.Button("Clear")
send = gr.Button("Send")
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history, system_message, max_tokens, temperature, top_p, use_pipeline, tool_definition, context):
user_message = history[-1][0]
bot_message = generate_response(user_message, history, system_message, max_tokens, temperature, top_p, use_pipeline, tool_definition, context)
history[-1][1] = bot_message
return history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, [chatbot, system_message, max_tokens, temperature, top_p, use_pipeline, tool_definition, context], chatbot
)
send.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, [chatbot, system_message, max_tokens, temperature, top_p, use_pipeline, tool_definition, context], chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
use_tool.change(
fn=lambda x: gr.update(visible=x),
inputs=[use_tool],
outputs=[tool_options]
)
if __name__ == "__main__":
demo.launch() |