app.py

# SPDX-License-Identifier: Apache-2.0
import datetime
import os
from threading import Event, Thread
from uuid import uuid4

import gradio as gr
import requests
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    StoppingCriteria,
    StoppingCriteriaList,
    TextIteratorStreamer,
)


model_name = "mosaicml/mpt-7b-chat"
max_new_tokens = 7000

# # small testing model:
# model_name = "gpt2"
# max_new_tokens = 128

auth_token = os.getenv("HF_TOKEN", None)

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

m = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    use_auth_token=auth_token,
    max_seq_len=10000,
).cuda()
tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_auth_token=auth_token)

stop_token_ids = tok.convert_tokens_to_ids(["<|im_end|>", "<|endoftext|>"])

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


start_message = """<|im_start|>system
- You are a helpful assistant chatbot trained by Debasish.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
"""


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"<|im_start|>user\n{item[0]}<|im_end|>",
                    f"<|im_start|>assistant\n{item[1]}<|im_end|>",
                ]
            )
            for item in history[:-1]
        ]
    )
    text += "".join(
        [
            "".join(
                [
                    f"<|im_start|>user\n{history[-1][0]}<|im_end|>",
                    f"<|im_start|>assistant\n{history[-1][1]}",
                ]
            )
        ]
    )
    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>Deb-Ona-Chat</center></h1>

        This demo is of Ona-7B. It is based on various open source models fine-tuned with approximately [171,000 conversation samples from this dataset](https://huggingface.co/datasets/sam-mosaic/vicuna_alpaca_hc3_chatml) and another [217,000] samples from another model.
        """
    )
    chatbot = gr.Chatbot().style(height=500)
    with gr.Row():
        with gr.Column():
            msg = gr.Textbox(
                label="Chat Message Box",
                placeholder="Chat Message Box",
                show_label=False,
            ).style(container=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=0.1,
                            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=1.0,
                            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: Deb-Ona can produce factually incorrect output, and should not be relied on to produce "
            "factually accurate information. Ona-7B 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(
            "Test Chat",
            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, concurrency_count=2)
demo.launch()