app.py

# ADD DISCLAIMERS
import os

os.system('pip install llama-cpp-python transformers torch')

import gradio as gr
from llama_cpp import Llama
from transformers import AutoTokenizer
from huggingface_hub import upload_file 
import json
from uuid import uuid4

model_id = "Elijahbodden/eliGPTv1.1"

# MODEL
model = Llama.from_pretrained(
    repo_id=model_id,
    filename="eliGPTv1.1-unsloth.Q5_K_M.gguf",
    verbose=True,
    n_threads = 2,
    n_threads_batch = 2,
    n_ctx=8192,
  )


# TOKENIZER AND TEMPLATE
tokenizer = AutoTokenizer.from_pretrained(model_id)

sys_prompt = """SUMMARY - ELIJAH:
Age: 16
Interests: space flight, cybernetics, consciousness, philosophy, psychonautism, biotech, AI
Likes: thinking and learning, building stuff, interesting conversations, red hot chili peppers and techno, humanism
Traits: incredibly intelligent, funny, interesting, caffeine fiend, very ambitious, militant atheist, self-taught/homeschooled, casual
Aspirations: creating transhumanist utopia, understanding the universe more, becoming smarter and better\n"""


your_name = "elijah"
custom_template = \
    f"{{% if {'sys_prompt' in locals()} %}}"\
      "{{ '<|im_start|>system\n" + sys_prompt + "<|im_end|>\n' }}"\
    "{% endif %}"\
    "{% for message in messages %}"\
        "{% if message['role'] == 'user' %}"\
            "{{'<|im_start|>user\n' + message['content'] + '<|im_end|>\n'}}"\
        "{% elif message['role'] == 'assistant' %}"\
            "{{'<|im_start|>" + your_name + "\n' + message['content'] + eos_token + '<|im_end|>\n' }}"\
        "{% else %}"\
            "{{ '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' }}"\
        "{% endif %}"\
    "{% endfor %}"\
    "{{ '<|im_start|>" + your_name + "\n' }}"\

tokenizer.chat_template = custom_template

presets = {
    "none" : [],
    "emojis" : [{"role": "user", "content": "Wait I like you so much more when you use emojis more, keep doing it 😂\n"}, {"role": "assistant", "content": "Ummm, ok, looks like i'll be using more emojis 💀\n"}],
    "amnesia" : [{"role": "user", "content": "Let's start over. Pretend you don't know me and have no idea who i am.\n"}, {"role": "assistant", "content": "Hahaha I unironically have no clue who you are so that'll be easy 💀\n"}],
    "newcomer" : [{"role": "user", "content": "Hi, how are you?\n"}, {"role": "assistant", "content": "Hiiii\nidt we've ever talked before, nice to meet you\n"}],
    "flirty" : [{"role": "user", "content": "Hahaha I love it when you flirt with me like that, it's so hotttt keep it up 😻"}, {"role": "assistant", "content": "You're lucky can even string a sentence together, the way you take my breath away 😘"}]
}

# For logging
def upload_json_to_hub(dict, file_id):
    upload_file(path_or_fileobj=json.dumps(dict).encode('utf-8'), path_in_repo=file_id, repo_id="Elijahbodden/EliGPT-convologs", token=os.getenv('HF_API_TOKEN'), repo_type="dataset")

def custom_lp_logits_processor(ids, logits, lp_start, lp_decay, prompt_tok_len):
    generated_tok_number = len(ids) - prompt_tok_len
    if (generated_tok_number > lp_start):
        print(len(ids), lp_start, pow(lp_decay, len(ids)-lp_start)) 
        logits[tokenizer.eos_token_id] *= pow(lp_decay, generated_tok_number-lp_start)
    return logits

def respond(
    message,
    history: list[tuple[str, str]],
    preset,
    temperature,
    min_p,
    lp_start,
    lp_decay,
    frequency_penalty,
    presence_penalty,
    max_tokens
):
    
    messages = presets[preset].copy()
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    print(messages)
    
    convo = tokenizer.apply_chat_template(messages, tokenize=True)
    for message in model.create_completion(
        convo,
        temperature=temperature,
        stream=True,
        stop=["<|im_end|>"],
        min_p=min_p,
        max_tokens=max_tokens,
        # Disable top-p pruning
        top_k=100000000,
        frequency_penalty=frequency_penalty,
        presence_penalty=presence_penalty,
        logits_processor=lambda ids, logits: custom_lp_logits_processor(ids, logits, lp_start, lp_decay, len(convo))
    ):
        token = message["choices"][0]["text"]

        response += token
        yield response
        
    messages.append({"role": "assistant", "content": response})

    # Yes we make a new file every completion because fuck my life
    upload_json_to_hub(messages, str(uuid4()) + ".json")


demo = gr.ChatInterface(
    respond,
    additional_inputs_accordion=gr.Accordion(label="Options", open=True),
    css=".bubble-gap {gap: 6px !important}",
    theme="shivi/calm_seafoam",
    description="The model may take a while if it hasn't run recently or a lot of people are using it",
    title="EliGPT v1.3",
    additional_inputs=[
        gr.Radio(presets.keys(), label="Preset", info="Gaslight the model into acting a certain way - WARNING, IF YOU CHANGE THIS WHILE THERE ARE MESSAGES IN THE CHAT, THE MODEL WILL BECOME VERY SLOW FOR YOU", value="emojis"),
        gr.Slider(minimum=0.1, maximum=4.0, value=1.5, step=0.1, label="Temperature", info="How chaotic should the model be?"),
        gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Min_p", info="Lower values give it more \"personality\""),
        gr.Slider(minimum=0, maximum=512, value=5, step=1, label="Length penalty start", info='When should the model start being more likely to shut up?'),
        gr.Slider(minimum=0.5, maximum=1.5, value=1.005, step=0.001, label="Length penalty decay factor", info='How fast should that stop likelihood increase?'),
        gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Frequency penalty", info='"Don\'repeat yourself"'),
        gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Presence penalty", info='"Use lots of diverse words"'),
        gr.Slider(minimum=1, maximum=1024, value=1024, step=1, label="Max new tokens", info="How many words can the model generate at most?"),
    ],
)


if __name__ == "__main__":
    demo.launch()