app.py

import gradio as gr
import re
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name_or_path = "teknium/OpenHermes-2-Mistral-7B"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             load_in_8bit=True,
                                             revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

BASE_SYSTEM_MESSAGE = "I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning."

def make_prediction(prompt, max_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    out = model.generate(input_ids, max_length=max_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty)
    text = tokenizer.decode(out[0], skip_special_tokens=True)
    yield text

def clear_chat(chat_history_state, chat_message):
    chat_history_state = []
    chat_message = ''
    return chat_history_state, chat_message

def user(message, history):
    history = history or []
    history.append([message, ""])
    return "", history

def chat(history, system_message, max_tokens, temperature, top_p, top_k, repetition_penalty):
    history = history or []
    
    # A última mensagem do usuário
    user_prompt = history[-1][0] if history else ""
    
    # Preparar a entrada para o modelo
    prompt_template = f'''system
{system_message.strip()}
user
{user_prompt}
assistant
'''
    input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
    
    # Gerar a saída
    output = model.generate(
        input_ids=input_ids,
        max_length=max_tokens,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty
    )
    
    # Decodificar a saída
    decoded_output = tokenizer.decode(output[0])
    assistant_response = decoded_output.split('assistant')[-1].strip()  # Pegar apenas a última resposta do assistente
    
    # Atualizar o histórico
    if history:
        history[-1][1] += assistant_response
    else:
        history.append(["", assistant_response])
    
    return history, history, ""


start_message = ""

CSS ="""
.contain { display: flex; flex-direction: column; }
.gradio-container { height: 100vh !important; }
#component-0 { height: 100%; }
#chatbot { flex-grow: 1; overflow: auto; resize: vertical; }
"""
with gr.Blocks(css=CSS) as demo:
    with gr.Row():
        with gr.Column():
            gr.Markdown("""
    ## OpenHermes-V2 Finetuned on Mistral 7B
    **Space created by [@artificialguybr](https://twitter.com/artificialguybr). Model by [@Teknium1](https://twitter.com/Teknium1). Thanks HF for GPU!**
    **OpenHermes-V2 is currently SOTA in some benchmarks for 7B models.**
    **Hermes 2 model was trained on 900,000 instructions, and surpasses all previous versions of Hermes 13B and below, and matches 70B on some benchmarks! Hermes 2 changes the game with strong multiturn chat skills, system prompt capabilities, and uses ChatML format. It's quality, diversity and scale is unmatched in the current OS LM landscape. Not only does it do well in benchmarks, but also in unmeasured capabilities, like Roleplaying, Tasks, and more.**
    """)
    with gr.Row():
        #chatbot = gr.Chatbot().style(height=500)
        chatbot = gr.Chatbot(elem_id="chatbot")
    with gr.Row():
        message = gr.Textbox(
            label="What do you want to chat about?",
            placeholder="Ask me anything.",
            lines=3,
        )
    with gr.Row():
        submit = gr.Button(value="Send message", variant="secondary", scale=1)
        clear = gr.Button(value="New topic", variant="secondary", scale=0)
        stop = gr.Button(value="Stop", variant="secondary", scale=0)
    with gr.Accordion("Show Model Parameters", open=False):
        with gr.Row():
            with gr.Column():
                max_tokens = gr.Slider(20, 2500, label="Max Tokens", step=20, value=500)
                temperature = gr.Slider(0.0, 2.0, label="Temperature", step=0.1, value=0.4)
                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95)
                top_k = gr.Slider(1, 100, label="Top K", step=1, value=40)
                repetition_penalty = gr.Slider(1.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1)

        system_msg = gr.Textbox(
            start_message, label="System Message", interactive=True, visible=True, placeholder="System prompt. Provide instructions which you want the model to remember.", lines=5)

    chat_history_state = gr.State()
    clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message], queue=False)
    clear.click(lambda: None, None, chatbot, queue=False)

    submit_click_event = submit.click(
        fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
    ).then(
        fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[chatbot, chat_history_state, message], queue=True
    )
    stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event], queue=False)

demo.queue(max_size=128, concurrency_count=2)
demo.launch()