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import os |
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import re |
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import logging |
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import gradio as gr |
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import openai |
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print(os.environ) |
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openai.api_base = os.environ.get("OPENAI_API_BASE") |
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openai.api_key = os.environ.get("OPENAI_API_KEY") |
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BASE_SYSTEM_MESSAGE = """I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning. |
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I am an assistant who thinks through their answers step-by-step to be sure I always get the right answer. |
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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 or solve anything.""" |
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def make_prediction(prompt, max_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None): |
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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=["</s>", "<|im_end|>"]) |
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for chunk in completion: |
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yield chunk["choices"][0]["text"] |
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def clear_chat(chat_history_state, chat_message): |
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chat_history_state = [] |
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chat_message = '' |
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return chat_history_state, chat_message |
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def user(message, history): |
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history = history or [] |
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history.append([message, ""]) |
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return "", history |
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def chat(history, system_message, max_tokens, temperature, top_p, top_k, repetition_penalty): |
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history = history or [] |
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if system_message.strip(): |
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messages = "<|im_start|> "+"system\n" + system_message.strip() + "<|im_end|>\n" + \ |
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"\n".join(["\n".join(["<|im_start|> "+"user\n"+item[0]+"<|im_end|>", "<|im_start|> assistant\n"+item[1]+"<|im_end|>"]) |
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for item in history]) |
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else: |
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messages = "<|im_start|> "+"system\n" + BASE_SYSTEM_MESSAGE + "<|im_end|>\n" + \ |
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"\n".join(["\n".join(["<|im_start|> "+"user\n"+item[0]+"<|im_end|>", "<|im_start|> assistant\n"+item[1]+"<|im_end|>"]) |
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for item in history]) |
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messages = messages.rstrip("<|im_end|>") |
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messages = messages.rstrip() |
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if temperature == 0: |
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top_p = 1 |
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top_k = -1 |
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prediction = make_prediction( |
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messages, |
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max_tokens=max_tokens, |
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temperature=temperature, |
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top_p=top_p, |
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top_k=top_k, |
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repetition_penalty=repetition_penalty, |
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) |
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for tokens in prediction: |
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tokens = re.findall(r'(.*?)(\s|$)', tokens) |
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for subtoken in tokens: |
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subtoken = "".join(subtoken) |
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answer = subtoken |
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history[-1][1] += answer |
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yield history, history, "" |
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start_message = "" |
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CSS =""" |
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.contain { display: flex; flex-direction: column; } |
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.gradio-container { height: 100vh !important; } |
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#component-0 { height: 100%; } |
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#chatbot { flex-grow: 1; overflow: auto; resize: vertical; } |
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""" |
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with gr.Blocks(css=CSS) as demo: |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown(f""" |
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## This demo is an unquantized GPU chatbot of [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) |
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Brought to you by your friends at Alignment Lab AI, OpenChat, and Open Access AI Collective! |
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""") |
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with gr.Row(): |
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gr.Markdown("# 🐋 Mistral-7B-OpenOrca Playground Space! 🐋") |
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with gr.Row(): |
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chatbot = gr.Chatbot(elem_id="chatbot") |
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with gr.Row(): |
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message = gr.Textbox( |
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label="What do you want to chat about?", |
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placeholder="Ask me anything.", |
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lines=3, |
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) |
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with gr.Row(): |
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submit = gr.Button(value="Send message", variant="secondary").style(full_width=True) |
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clear = gr.Button(value="New topic", variant="secondary").style(full_width=False) |
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stop = gr.Button(value="Stop", variant="secondary").style(full_width=False) |
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with gr.Accordion("Show Model Parameters", open=False): |
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with gr.Row(): |
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with gr.Column(): |
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max_tokens = gr.Slider(20, 2500, label="Max Tokens", step=20, value=500) |
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temperature = gr.Slider(0.0, 2.0, label="Temperature", step=0.1, value=0.4) |
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top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95) |
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top_k = gr.Slider(1, 100, label="Top K", step=1, value=40) |
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repetition_penalty = gr.Slider(1.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1) |
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system_msg = gr.Textbox( |
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start_message, label="System Message", interactive=True, visible=True, placeholder="System prompt. Provide instructions which you want the model to remember.", lines=5) |
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chat_history_state = gr.State() |
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clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message], queue=False) |
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clear.click(lambda: None, None, chatbot, queue=False) |
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submit_click_event = submit.click( |
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fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True |
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).then( |
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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 |
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) |
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stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event], queue=False) |
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demo.queue(max_size=128, concurrency_count=48).launch(debug=True, server_name="0.0.0.0", server_port=7860) |
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