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 = """I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning. I am an assistant who thinks through their answers step-by-step to be sure I always get the right answer. I think more clearly if I write out my thought process in a scratchpad manner first; therefore, I always explain background context, assumptions, and step-by-step thinking BEFORE trying to answer a question.""" 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/Mistral-7B-OpenOrca", prompt=prompt, max_tokens=max_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, stream=True, stop=["", "<|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 [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) Brought to you by your friends at Alignment Lab AI, OpenChat, and Open Access AI Collective! """) with gr.Row(): gr.Markdown("# 🐋 Mistral-7B-OpenOrca 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(0.1, 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)