File size: 5,216 Bytes
aab951d b1526db aab951d faaf8fa aab951d 99f44e2 aab951d 99f44e2 aab951d faaf8fa aab951d faaf8fa aab951d faaf8fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
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 OpenOrcaPlatyChat. 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 Alignment Lab AI. The projects are OpenOrca and Platypus."""
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/OpenOrca-Platypus2-13B", 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 = BASE_SYSTEM_MESSAGE + system_message.strip() + "\n" + \
"\n".join(["\n".join(["User: "+item[0]+"<|end_of_turn|>", "Assistant: "+item[1]+"<|end_of_turn|>"])
for item in history])
# strip the last `<|end_of_turn|>` from the messages
messages = messages.rstrip("<|end_of_turn|>")
# 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-Platypus2-13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B)
Brought to you by your friends at Alignment Lab AI, garage-bAInd, Open Access AI Collective, and OpenChat!
""")
with gr.Row():
gr.Markdown("# 🐋 OpenOrca Platypus2 13B 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=48, concurrency_count=16).launch(debug=True, server_name="0.0.0.0", server_port=7860)
|