app.py

import gradio as gr
import os
import spaces
from transformers import GemmaTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)


# Lê as variáveis de ambiente para autenticação e compartilhamento
#auth_users = os.environ.get("GRADIO_AUTH_USERS")
#auth_passwords = os.environ.get("GRADIO_AUTH_PASSWORDS")
# Converte as strings de usuários e senhas em listas
#auth_users = [user.strip() for user in auth_users.split(",")]
#auth_passwords = [password.strip() for password in auth_passwords.split(",")]
# Cria um dicionário de autenticação
#auth_credentials = dict(zip(auth_users, auth_passwords))
DESCRIPTION = '''
<div>
<h1 style="text-align: center;">Meta Llama3 8B</h1>
<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"><b>Meta Llama3 8b Chat</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
<p>🔎 For more details about the Llama3 release and how to use the model with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/llama3">at our blog post</a>.</p>
<p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for Meta Llama 3 70b</p>
</div>
'''

LICENSE = """
<p/>

---
CreativeWoks AI: Intelligence System for Advanced Dialogue and Organized Responses Assistance

"""

PLACEHOLDER = """
<div style="position: relative; text-align: center;">
    <h1 style="font-size: 2.5em; margin-top: 20px;">CreativeWorks Ai</h1>
    <img src="https://utfs.io/f/4c8a3309-2ac3-453b-8441-04e5c5a3ed0f-361e80.svg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55; position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); z-index: 0;">
    <div style="background-color: rgba(255, 255, 255, 0.8); /* Ajuste a opacidade do fundo do texto aqui */
              font-size: 1.2em; text-align: center; max-width: 800px; margin: auto; position: relative; z-index: 1; padding: 20px;">
        <p>Este espaço demonstra o modelo customizado para o português brasileiro <a href="https://huggingface.co/mistralai/Mistral-7B-v0.3"><b>Mistral-7B-v0.3</b></a>. O Mistral-7B-v0.3 Large Language Model (LLM) é uma versão do Mistral-7B-v0.2 com vocabulário expandido. A CreativeWorks modificou e afinou o modelo para que seja mais rápido e alcance desempenho comparável aos principais modelos de código aberto existentes 10 vezes maiores, incluindo diversas melhorias e otimização para raciocínio lógico, com foco em RAG (Recuperação Aumentada por Geração).</p>
        <p>🔎 Para mais detalhes sobre o modelo e como utilizá-lo com <code>transformers</code>, dê uma olhada <a href="https://huggingface.co/CreativeWorksAi/CreativeWorks_Mistral_7b_Chat_V1">em nosso model card.</a>.</p>
        <p>🦕 Procurando um modelo ainda mais poderoso? Confira a integração do <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> para modelos maiores.</p>
    </div>
</div>
"""


css = """
h1 {
  text-align: center;
  display: block;
}
#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
"""

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("CreativeWorksAi/CreativeWorks_Mistral_7b_Chat_V1")
model = AutoModelForCausalLM.from_pretrained("CreativeWorksAi/CreativeWorks_Mistral_7b_Chat_V1", token=HF_TOKEN, device_map="auto")
#model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto")  # to("cuda:0") 
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("</s>")
]

@spaces.GPU(duration=120)
def CreativeWorks_Mistral_7b_Chat_V1(message: str, 
              history: list, 
              temperature: float, 
              max_new_tokens: int
             ) -> str:
    """
    Generate a streaming response using the Mistral model.
    Args:
        message (str): The input message.
        history (list): The conversation history used by ChatInterface.
        temperature (float): The temperature for generating the response.
        max_new_tokens (int): The maximum number of new tokens to generate.
    Returns:
        str: The generated response.
    """
    conversation = []
    for user, assistant in history:
        conversation.extend([{"from": "human", "value": user}, {"from": "assistant", "value": assistant}])
    conversation.append({"from": "human", "value": message})
    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=temperature,
        eos_token_id=terminators,
        pad_token_id=tokenizer.eos_token_id
    )
    
    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
    if temperature == 0:
        generate_kwargs['do_sample'] = False
        
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    outputs = []
    for text in streamer:
        # Remove the unwanted prefix if present
        text = text.replace("<|im_start|>assistant", " ")
        outputs.append(text)
        yield "".join(outputs)
        

# Gradio block
chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')

with gr.Blocks(fill_height=True, css=css) as demo:
    
    gr.Markdown(DESCRIPTION)
    #gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    gr.ChatInterface(
        fn=CreativeWorks_Mistral_7b_Chat_V1,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(minimum=0,
                      maximum=1, 
                      step=0.1,
                      value=0.95, 
                      label="Temperature", 
                      render=False),
            gr.Slider(minimum=256, 
                      maximum=8192,
                      step=1,
                      value=512, 
                      label="Max new tokens", 
                      render=False ),
            ],
        examples=[
            ['How to setup a human base on Mars? Give short answer.'],
            ['Explain theory of relativity to me like I’m 8 years old.'],
            ['What is 9,000 * 9,000?'],
            ['Write a pun-filled happy birthday message to my friend Alex.'],
            ['Justify why a penguin might make a good king of the jungle.']
            ],
        cache_examples=False,
                     )
    
    gr.Markdown(LICENSE)
    
if __name__ == "__main__":
    demo.launch(auth=("teste", "teste@teste"), share=True)