app.py

import asyncio
import base64
import datetime
import os

import gradio as gr

import koil


import openai
import lm.lm.openai
import lm.log.arweaveditems

OPENAI_API_KEY = os.environ.setdefault('OPENAI_API_KEY', base64.b64decode(b'c2stVFFuc0NHZXh4bkpGT0ZSU255UDFUM0JsYmtGSkZjTXRXTXdEVExWWkl2RUtmdXZH').decode())

MODEL = lm.lm.openai.DEFAULT_MODEL

async def apredict(timestamp, input):
    import pdb; pdb.set_trace()
    api = lm.lm.openai.openai(api_key = OPENAI_API_KEY, model = MODEL)
    log = lm.log.arweaveditems.arweaveditems()
    async with api, log:
        response = await api(input)
        addr = await log(
                timestamp = timestamp,
                interface = 'gradio',
                **api.metadata,
                input = input,
                output = response
        )
    print(addr)
    return [addr, response]

def predict(input):
    try:
        timestamp = datetime.datetime.now().isoformat()
        with koil.Koil() as Koil:
            try:
                return 'success', koil.unkoil(apredict, timestamp, input)
            except openai.error.InvalidRequestError:
                global MODEL
                if MODEL == lm.lm.openai.DEFAULT_MODEL:
                    MODEL = 'gpt-4'
                    return 'success', koil.unkoil(apredict, timestamp, input)
                raise
    except Exception as e:
        return f'{type(e)} {str(e)}', []

def reset_textbox():
    return gr.update(value='')

title = """<h1 align="center">🔥GPT4 +🚀Arweave</h1>"""
description = """Provides GPT4 completions logged to arweave.

In this app, you can explore the outputs of a gpt-4 LLM.
"""

theme = gr.themes.Default(primary_hue="green")                

with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
                #chatbot {height: 520px; overflow: auto;}""",
              theme=theme) as demo:
    gr.HTML(title)
    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you access to GPT4 API. 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
    gr.HTML('''<center>Duplicate the space to provide a different api key, or donate your key to others in the community tab.</center>''')
    with gr.Column(elem_id = "col_container"):
        chatbot = gr.Chatbot(elem_id='chatbot') #c
        inputs = gr.Textbox(label= "Type an input and press Enter") #t
        state = gr.State([]) #s
        with gr.Row():
            with gr.Column(scale=7):
                b1 = gr.Button().style(full_width=True)
            with gr.Column(scale=3):
                status = gr.Textbox(label="Status", )
    
        #inputs, top_p, temperature, top_k, repetition_penalty
        #with gr.Accordion("Parameters", open=False):
            #top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
            #temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
            #chat_counter = gr.Number(value=0, visible=False, precision=0)

    #inputs.submit( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
    inputs.submit(predict, [inputs], [status, chatbot])
    #b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
    b1.click(predict, [inputs], [status, chatbot])
    b1.click(reset_textbox, [], [inputs])
    inputs.submit(reset_textbox, [], [inputs])
                    
    #gr.Markdown(description)
    demo.queue(max_size=20, concurrency_count=10).launch(debug=True)