app.py

import spaces
import gradio as gr
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import os

title = """# Welcome to 🌟Tonic's🐇🥷🏻Neo
You can build with this endpoint using🐇🥷🏻Trinity available here : [WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5](https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5). You can also use 🐇🥷🏻Trinity by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/neo?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) Math 🔍 [introspector](https://huggingface.co/introspector) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [SciTonic](https://github.com/Tonic-AI/scitonic)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
"""


default_system_prompt = """SYSTEM: You are an AI that code. Answer with code."""

model_path = "WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5"


hf_token = os.getenv("HF_TOKEN")
if not hf_token:
    raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=True,
    load_in_8bit=False,
    trust_remote_code=True,
    token=hf_token,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

@spaces.GPU
def generate_text(custom_prompt, user_input, temperature, generate_len, top_p, top_k):
    system_prompt = custom_prompt if custom_prompt else default_system_prompt
    llm_prompt = f"{system_prompt} \nUSER: {user_input} \nASSISTANT: "
    
    tokens = tokenizer.encode(llm_prompt, return_tensors="pt")
    tokens = tokens.to("cuda")

    length = tokens.shape[1]
    with torch.no_grad():
        output = model.generate(
            input_ids=tokens,
            max_length=length + generate_len,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            num_return_sequences=1,
        )
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    answer = generated_text[len(llm_prompt):].strip()
    
    return answer

def gradio_app():
    with gr.Blocks() as demo:
        gr.Markdown(title)
        with gr.Row():
            custom_prompt = gr.Textbox(label="🐇🥷🏻NeoCustom System Prompt (optional)", placeholder="Leave blank to use the default prompt...")
            instruction = gr.Textbox(label="Your Instruction", placeholder="Type your question here...")
        with gr.Row():
            temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="Temperature")
            generate_len = gr.Slider(minimum=100, maximum=1024, step=10, value=100, label="Generate Length")
            top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=1.0, label="Top P")
            top_k = gr.Slider(minimum=0, maximum=100, step=1, value=50, label="Top K")
        with gr.Row():
            generate_btn = gr.Button("Generate")
        output = gr.Code(label="🐇🥷🏻Neo:", lines=10)

        generate_btn.click(
            fn=generate_text,
            inputs=[custom_prompt, instruction, temperature, generate_len, top_p, top_k],
            outputs=output
        )

    demo.launch()

if __name__ == "__main__":
    gradio_app()