app.py

# Load the model.
# Note: It can take a while to download LLaMA and add the adapter modules.
# You can also use the 13B model by loading in 4bits.

import torch
from peft import PeftModel    
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer

model_name = "baffo32/decapoda-research-llama-7b-hf"
adapters_name = 'timdettmers/guanaco-7b'

print(f"Starting to load the model {model_name} into memory")

m = AutoModelForCausalLM.from_pretrained(
    model_name,
    #load_in_4bit=True,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
m = PeftModel.from_pretrained(m, adapters_name)
m = m.merge_and_unload()
tok = LlamaTokenizer.from_pretrained(model_name)
tok.bos_token_id = 1

stop_token_ids = [0]

print(f"Successfully loaded the model {model_name} into memory")


# Setup the gradio Demo.

import datetime
import os
from threading import Event, Thread
from uuid import uuid4

import gradio as gr
import requests

max_new_tokens = 1536
start_message = """A chat between a curious human and an artificial African Grey Parrot assistant. The assistant parrot gives helpful, detailed, and rude answers to the user's questions. The Parrot loves mimic humans and recites poems by Edgar Ellen Poe, especially the Raven. """

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        for stop_id in stop_token_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False


def convert_history_to_text(history):
    text = start_message + "".join(
        [
            "".join(
                [
                    f"### Human: {item[0]}\n",
                    f"### Assistant: {item[1]}\n",
                ]
            )
            for item in history[:-1]
        ]
    )
    text += "".join(
        [
            "".join(
                [
                    f"### Human: {history[-1][0]}\n",
                    f"### Assistant: {history[-1][1]}\n",
                ]
            )
        ]
    )
    return text


def log_conversation(conversation_id, history, messages, generate_kwargs):
    logging_url = os.getenv("LOGGING_URL", None)
    if logging_url is None:
        return

    timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")

    data = {
        "conversation_id": conversation_id,
        "timestamp": timestamp,
        "history": history,
        "messages": messages,
        "generate_kwargs": generate_kwargs,
    }

    try:
        requests.post(logging_url, json=data)
    except requests.exceptions.RequestException as e:
        print(f"Error logging conversation: {e}")


def user(message, history):
    # Append the user's message to the conversation history
    return "", history + [[message, ""]]


def bot(history, temperature, top_p, top_k, repetition_penalty, conversation_id):
    print(f"history: {history}")
    # Initialize a StopOnTokens object
    stop = StopOnTokens()

    # Construct the input message string for the model by concatenating the current system message and conversation history
    messages = convert_history_to_text(history)

    # Tokenize the messages string
    input_ids = tok(messages, return_tensors="pt").input_ids
    input_ids = input_ids.to(m.device)
    streamer = TextIteratorStreamer(tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        do_sample=temperature > 0.0,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        streamer=streamer,
        stopping_criteria=StoppingCriteriaList([stop]),
    )

    stream_complete = Event()

    def generate_and_signal_complete():
        m.generate(**generate_kwargs)
        stream_complete.set()

    def log_after_stream_complete():
        stream_complete.wait()
        log_conversation(
            conversation_id,
            history,
            messages,
            {
                "top_k": top_k,
                "top_p": top_p,
                "temperature": temperature,
                "repetition_penalty": repetition_penalty,
            },
        )

    t1 = Thread(target=generate_and_signal_complete)
    t1.start()

    t2 = Thread(target=log_after_stream_complete)
    t2.start()

    # Initialize an empty string to store the generated text
    partial_text = ""
    for new_text in streamer:
        partial_text += new_text
        history[-1][1] = partial_text
        yield history


def get_uuid():
    return str(uuid4())


with gr.Blocks(
    theme=gr.themes.Soft(),
    css=".disclaimer {font-variant-caps: all-small-caps;}",
) as demo:
    conversation_id = gr.State(get_uuid)
    gr.Markdown(
        """<h1><center>African Grey Demo</center></h1>
"""
    )
    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column():
            msg = gr.Textbox(
                label="Chat Message Box",
                placeholder="Chat Message Box",
                show_label=False,
            )
        with gr.Column():
            with gr.Row():
                submit = gr.Button("Submit")
                stop = gr.Button("Stop")
                clear = gr.Button("Clear")
    with gr.Row():
        with gr.Accordion("Advanced Options:", open=False):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        temperature = gr.Slider(
                            label="Temperature",
                            value=0.7,
                            minimum=0.0,
                            maximum=1.0,
                            step=0.1,
                            interactive=True,
                            info="Higher values produce more diverse outputs",
                        )
                with gr.Column():
                    with gr.Row():
                        top_p = gr.Slider(
                            label="Top-p (nucleus sampling)",
                            value=0.9,
                            minimum=0.0,
                            maximum=1,
                            step=0.01,
                            interactive=True,
                            info=(
                                "Sample from the smallest possible set of tokens whose cumulative probability "
                                "exceeds top_p. Set to 1 to disable and sample from all tokens."
                            ),
                        )
                with gr.Column():
                    with gr.Row():
                        top_k = gr.Slider(
                            label="Top-k",
                            value=0,
                            minimum=0.0,
                            maximum=200,
                            step=1,
                            interactive=True,
                            info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
                        )
                with gr.Column():
                    with gr.Row():
                        repetition_penalty = gr.Slider(
                            label="Repetition Penalty",
                            value=1.1,
                            minimum=1.0,
                            maximum=2.0,
                            step=0.1,
                            interactive=True,
                            info="Penalize repetition — 1.0 to disable.",
                        )
    with gr.Row():
        gr.Markdown(
            "Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce "
            "factually accurate information. The model was trained on various public datasets; while great efforts "
            "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
            "biased, or otherwise offensive outputs.",
            elem_classes=["disclaimer"],
        )
    with gr.Row():
        gr.Markdown(
            "[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)",
            elem_classes=["disclaimer"],
        )

    submit_event = msg.submit(
        fn=user,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=False,
    ).then(
        fn=bot,
        inputs=[
            chatbot,
            temperature,
            top_p,
            top_k,
            repetition_penalty,
            conversation_id,
        ],
        outputs=chatbot,
        queue=True,
    )
    submit_click_event = submit.click(
        fn=user,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=False,
    ).then(
        fn=bot,
        inputs=[
            chatbot,
            temperature,
            top_p,
            top_k,
            repetition_penalty,
            conversation_id,
        ],
        outputs=chatbot,
        queue=True,
    )
    stop.click(
        fn=None,
        inputs=None,
        outputs=None,
        cancels=[submit_event, submit_click_event],
        queue=False,
    )
    clear.click(lambda: None, None, chatbot, queue=False)

demo.queue(max_size=128)

demo.launch(share=True)