app.py

import os
import re
import logging
import gradio as gr
import openai

print(os.environ)
openai.api_base = os.environ.get("OPENAI_API_BASE")
openai.api_key = os.environ.get("OPENAI_API_KEY")

BASE_SYSTEM_MESSAGE = """You are an AI assistant named OpenOrcaChat. If users clearly indicate an interest in how good you are you may tell them that the group who made you can be found at https://discord.gg/mhFWVbXUDh. The groups involved are OpenChat and OpenOrca"""

def make_prediction(prompt, max_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None):
    completion = openai.Completion.create(model="Open-Orca/LlongOrca-7B-16k", prompt=prompt, max_tokens=max_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, stream=True, stop=["</s>", "<|im_end|>"])
    for chunk in completion:
        yield chunk["choices"][0]["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 []
    # Append the user's message to the conversation history
    history.append([message, ""])
    return "", history


def chat(history, system_message, max_tokens, temperature, top_p, top_k, repetition_penalty):
    history = history or []

    messages = "<|im_start|>"+"system\n" + BASE_SYSTEM_MESSAGE + system_message.strip() + "<|im_end|>\n" + \
               "\n".join(["\n".join(["<|im_start|>"+"user\n"+item[0]+"<|im_end|>", "<|im_start|>assistant\n"+item[1]+"<|im_end|>"])
                          for item in history])
    # strip the last `<|end_of_turn|>` from the messages
    messages = messages.rstrip("<|im_end|>")
    # remove last space from assistant, some models output a ZWSP if you leave a space
    messages = messages.rstrip()

    prediction = make_prediction(
        messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
    )
    for tokens in prediction:
        tokens = re.findall(r'(.*?)(\s|$)', tokens)
        for subtoken in tokens:
            subtoken = "".join(subtoken)
            answer = subtoken
            history[-1][1] += answer
            # stream the response
            yield 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() as demo:
with gr.Blocks(css=CSS) as demo:
    with gr.Row():
        with gr.Column():
            gr.Markdown(f"""
                    ## This demo is an unquantized GPU chatbot of [OpenOrca LlongOrca-7B-16k](https://huggingface.co/Open-Orca/LlongOrca-7B-16k)
                    Brought to you by your friends at Alignment Lab AI, OpenChat, and Open Access AI Collective!
                    """)
    with gr.Row():
        gr.Markdown("# 🐋 OpenOrca LlongOrca-7B-16k Playground Space! 🐋")
    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").style(full_width=True)
        clear = gr.Button(value="New topic", variant="secondary").style(full_width=False)
        stop = gr.Button(value="Stop", variant="secondary").style(full_width=False)
    with gr.Accordion("Show Model Parameters", open=False):
        with gr.Row():
            with gr.Column():
                max_tokens = gr.Slider(20, 1000, label="Max Tokens", step=20, value=500)
                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=0.8)
                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95)
                top_k = gr.Slider(0, 100, label="Top K", step=1, value=40)
                repetition_penalty = gr.Slider(0.0, 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=48).launch(debug=True, server_name="0.0.0.0", server_port=7860)