app.py

import os
import math
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import gradio as gr
import sentencepiece

title = "# ²Welcome to 🙋🏻‍♂️Tonic's🧠🤌🏻Neural Chat (From Intel)!"
description = """Try out [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) the Instruct Llama Finetune using the [mistralai/Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) recipe. You can use [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) here via API using Gradio by scrolling down and clicking Use 'Via API' or privately by [cloning this space on huggingface](https://huggingface.co/spaces/TeamTonic/NeuralChat?duplicate=true)  . [Join my active builders' server on discord](https://discord.gg/VqTxc76K3u). Let's build together!. """

os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:50'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_name = "Intel/neural-chat-7b-v3-1"
tokenizer = AutoTokenizer.from_pretrained("Intel/neural-chat-7b-v3-1")
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")

class IntelChatBot:
    def __init__(self, model, tokenizer, system_message="You are 🧠🤌🏻Neuro, an AI language model created by Tonic-AI. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."):
        self.model = model
        self.tokenizer = tokenizer
        self.system_message = system_message

    def set_system_message(self, new_system_message):
        self.system_message = new_system_message

    def format_prompt(self, user_message):
        prompt = f"### System:\n {self.system_message}\n ### User:\n{user_message}\n### Assistant:\n"
        return prompt

    def predict(self, user_message, temperature, max_new_tokens, top_p, repetition_penalty, do_sample):
        prompt = self.format_prompt(user_message)
        inputs = self.tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
        input_ids = inputs["input_ids"].to(self.model.device)
        attention_mask = inputs["attention_mask"].to(self.model.device)


        output_ids = self.model.generate(
            input_ids,
            attention_mask=attention_mask,
            max_length=input_ids.shape[1] + max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=repetition_penalty,
            do_sample=do_sample
        )

        response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
        return response

def gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty, do_sample):
    Intel_bot.set_system_message(system_message)
    if not do_sample:
        max_length = 780
        temperature = 0.9 
        top_p = 0.9        
        repetition_penalty = 0.9  
    response = Intel_bot.predict(user_message, temperature, max_new_tokens, top_p, repetition_penalty, do_sample)
    return response

Intel_bot = IntelChatBot(model, tokenizer)


with gr.Blocks(theme = "ParityError/Anime") as demo:
    gr.Markdown(title)  
    gr.Markdown(description)  
    with gr.Row():
        system_message = gr.Textbox(label="Optional 🧠🤌🏻NeuralChat Assistant Message", lines=2)
        user_message = gr.Textbox(label="Your Message", lines=3)
    with gr.Row():
        do_sample = gr.Checkbox(label="Advanced", value=False)
    
    with gr.Accordion("Advanced Settings", open=lambda do_sample: do_sample):
        with gr.Row():
            max_new_tokens = gr.Slider(label="Max new tokens", value=780, minimum=550, maximum=3200, step=1)
            temperature = gr.Slider(label="Temperature", value=0.3, minimum=0.1, maximum=1.0, step=0.1)
            top_p = gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.01, maximum=0.99, step=0.05)
            repetition_penalty = gr.Slider(label="Repetition penalty", value=1.9, minimum=1.0, maximum=2.0, step=0.05)

    submit_button = gr.Button("Submit")
    output_text = gr.Textbox(label="🧠🤌🏻NeuralChat Response")

    def process(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty, do_sample):
        return gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty, do_sample)

    submit_button.click(
        process,
        inputs=[user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty, do_sample],
        outputs=output_text
    )
    
demo.launch()