app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from globe import title, description, customtool, presentation1, presentation2, joinus
import spaces

model_path = "nvidia/OpenReasoning-Nemotron-14B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map="auto")
if tokenizer.pad_token_id is None:
    tokenizer.pad_token_id = tokenizer.eos_token_id

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")

def create_prompt(system_message, user_message, tool_definition="", context=""):
    # OpenReasoning-Nemotron uses a different prompt format
    if tool_definition:
        return f"""You are a helpful and harmless assistant. You should think step-by-step before responding to the instruction below.

System Context: {system_message}

Tool Definition: {tool_definition}

Context: {context}

User: {user_message}

Assistant: Let me think about this step by step."""
    else:
        return f"""You are a helpful and harmless assistant. You should think step-by-step before responding to the instruction below.

System Context: {system_message}

User: {user_message}

Assistant: Let me think about this step by step."""


@spaces.GPU(duration=94)
def generate_response(message, history, system_message, max_tokens, temperature, top_p, do_sample, use_pipeline=False, tool_definition="", context=""):
    full_prompt = create_prompt(system_message, message, tool_definition, context)

    if use_pipeline:
        response = pipe(full_prompt, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p, do_sample=do_sample)[0]['generated_text']
    else:
        max_model_length = model.config.max_position_embeddings if hasattr(model.config, 'max_position_embeddings') else 8192

        max_length = max_model_length - max_tokens

        inputs = tokenizer(full_prompt, return_tensors="pt", padding=True, truncation=True, max_length=max_length)
        input_ids = inputs['input_ids'].to(model.device)
        attention_mask = inputs['attention_mask'].to(model.device)
        
        with torch.no_grad():
            output_ids = model.generate(
                input_ids,
                max_new_tokens=max_tokens,
                temperature=temperature,
                top_p=top_p,
                do_sample=do_sample,
                attention_mask=attention_mask
            )
        
        response = tokenizer.decode(output_ids[0], skip_special_tokens=True)

    # Extract the assistant response (everything after "Assistant: ")
    assistant_response = response.split("Assistant: ")[-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

def user(user_message, history):
    return "", history + [[user_message, None]]

def bot(history, system_prompt, max_length, temperature, top_p, advanced_checkbox, use_pipeline, tool_definition):
    user_message = history[-1][0]
    do_sample = advanced_checkbox 
    bot_message = generate_response(user_message, history, system_prompt, max_length, temperature, top_p, do_sample, use_pipeline, tool_definition)
    history[-1][1] = bot_message
    return history

with gr.Blocks() as demo:
    with gr.Row(): 
        gr.Markdown(title)
    with gr.Row():
        gr.Markdown(description)
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group():
                gr.Markdown(presentation1)
        with gr.Column(scale=1):
            with gr.Group():
                gr.Markdown(joinus)
    with gr.Row():
        with gr.Column(scale=2):
            system_prompt = gr.TextArea(label="📑Context", placeholder="add context here...", lines=5)
            user_input = gr.TextArea(label="🤷🏻‍♂️User Input", placeholder="Hi there my name is Tonic!", lines=2)
            advanced_checkbox = gr.Checkbox(label="🧪 Advanced Settings", value=False)
            with gr.Column(visible=False) as advanced_settings:
                max_length = gr.Slider(label="📏Max Length", minimum=12, maximum=64000, value=2048, step=1)
                temperature = gr.Slider(label="🌡️Temperature", minimum=0.01, maximum=1.0, value=0.7, step=0.01)
                top_p = gr.Slider(label="⚛️Top-p (Nucleus Sampling)", minimum=0.1, maximum=1.0, value=0.9, step=0.01)
                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"
                    )
            
            generate_button = gr.Button(value="🤖OpenReasoning-Nemotron-14B")

        with gr.Column(scale=2):
            chatbot = gr.Chatbot(label="🤖OpenReasoning-Nemotron-14B")
    
    generate_button.click(
        user,
        [user_input, chatbot],
        [user_input, chatbot],
        queue=False
    ).then(
        bot,
        [chatbot, system_prompt, max_length, temperature, top_p, advanced_checkbox, use_pipeline, tool_definition],
        chatbot
    )

    advanced_checkbox.change(
        fn=lambda x: gr.update(visible=x),
        inputs=[advanced_checkbox],
        outputs=[advanced_settings]
    )

    use_tool.change(
        fn=lambda x: gr.update(visible=x),
        inputs=[use_tool],
        outputs=[tool_options]
    )

if __name__ == "__main__":
    demo.queue()
    demo.launch(ssr_mode=False, mcp_server=True)