app.py

import os
os.system('pip install --upgrade pip')
os.system('pip install gradio transformers torch')

import gradio as gr
from typing import List, Optional, Tuple, Dict
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

default_system = 'You are JOSIE which is an acronym for "Just an Outstandingly Smart Intelligent Entity", a private and super-intelligent AI assistant, created by Gökdeniz Gülmez.'

History = List[Tuple[str, str]]
Messages = List[Dict[str, str]]

# Load model and tokenizer
model_name = 'mlx-community/J.O.S.I.E.3-Beta12-7B-slerp-8-bit'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    ignore_mismatched_sizes=True,
)

def clear_session() -> History:
    return '', []

def modify_system_session(system: str) -> str:
    if system is None or len(system) == 0:
        system = default_system
    return system, system, []

def history_to_messages(history: History, system: str) -> Messages:
    messages = [{'role': 'system', 'content': system}]
    for h in history:
        messages.append({'role': 'user', 'content': h[0]})
        messages.append({'role': 'assistant', 'content': h[1]})
    return messages

def messages_to_history(messages: Messages) -> Tuple[str, History]:
    assert messages[0]['role'] == 'system'
    system = messages[0]['content']
    history = []
    for q, r in zip(messages[1::2], messages[2::2]):
        history.append([q['content'], r['content']])
    return system, history

def generate_response(messages: Messages) -> str:
    prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
    inputs = tokenizer(prompt, return_tensors='pt')
    outputs = model.generate(inputs['input_ids'], max_length=512, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split('assistant:')[-1].strip()

def model_chat(query: Optional[str], history: Optional[History], system: str) -> Tuple[str, str, History]:
    if query is None:
        query = ''
    if history is None:
        history = []
    messages = history_to_messages(history, system)
    messages.append({'role': 'user', 'content': query})
    response = generate_response(messages)
    messages.append({'role': 'assistant', 'content': response})
    system, history = messages_to_history(messages)
    return '', history, system

with gr.Blocks() as demo:
    gr.Markdown("""<center><font size=8>J.O.S.I.E.3-Beta12 Preview👾</center>""")
    gr.Markdown("""<center><font size=4>J.O.S.I.E. is also multilingual (German, Spanish, Chinese, Japanese, French)</center>""")

    with gr.Row():
        with gr.Column(scale=1):
            modify_system = gr.Button("🛠️ Set system prompt and clear history", scale=2)
        system_state = gr.Textbox(value=default_system, visible=False)
    chatbot = gr.Chatbot(label='J.O.S.I.E.3-Beta12-7B')
    textbox = gr.Textbox(lines=2, label='Input')

    with gr.Row():
        clear_history = gr.Button("🧹 Clear history")
        submit = gr.Button("🚀 Send")

    submit.click(model_chat,
                 inputs=[textbox, chatbot, system_state],
                 outputs=[textbox, chatbot, system_state],
                 concurrency_limit=5)
    clear_history.click(fn=clear_session,
                        inputs=[],
                        outputs=[textbox, chatbot])
    modify_system.click(fn=modify_system_session,
                        inputs=[system_state],
                        outputs=[system_state, system_state, chatbot])

demo.queue(api_open=False)
demo.launch(max_threads=5)