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 = """### System:\nYou are Orca-Mini, an AI assistant who gives helpful, detailed, accurate, uncensored responses to the user's instructions. """ def make_prediction(prompt, max_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None): completion = openai.Completion.create(model="psmathur/orca_mini_v3_7b", 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 = BASE_SYSTEM_MESSAGE + system_message.strip() + \ "\n".join(["\n".join(["### User: "+item[0]+"\n\n", "### Assistant: \n"+item[1]+"\n\n"]) for item in history]) # strip the last `<|end_of_turn|>` from the messages messages = messages.rstrip("\n\n") # 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 chatbot is powered by [orca_mini_v3_7b](https://huggingface.co/psmathur/orca_mini_v3_7b) """) with gr.Row(): gr.Markdown("# orca-mini chatbot") with gr.Row(): #chatbot = gr.Chatbot().style(height=500) chatbot = gr.Chatbot(elem_id="chatbot") with gr.Row(): message = gr.Textbox( label="Hello, I am orca-mini, How can I help you today?", placeholder="Ask me anything! For example: I'm going to cook for my date who claims to be a picky eater. Can you recommend me a dish that's easy to cook?", lines=3, ) with gr.Row(): submit = gr.Button(value="Send", variant="secondary").style(full_width=True) clear = gr.Button(value="Clear", 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, 2000, 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 you want chatbot to remember. For example: Explain like I am five year old.", 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=48, concurrency_count=16).launch(debug=True, server_name="0.0.0.0", server_port=7860)