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| import asyncio | |
| import datetime | |
| import gradio as gr | |
| import koil | |
| from lm.lm.openai import openai | |
| from lm.log.arweaveditems import arweaveditems | |
| OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") | |
| async def predict(input): | |
| timestamp = datetime.datetime.now().isoformat() | |
| try: | |
| api = openai(api_key = OPENAI_API_KEY) | |
| except: | |
| api = openai(api_key = OPENAI_API_KEY, model = 'gpt-4') | |
| async with api as api, arweaveditems() as log: | |
| response = await api(input) | |
| addr = await log( | |
| timestamp = timestamp, | |
| interface = 'gradio', | |
| **api.metadata, | |
| input = input, | |
| output = response | |
| ) | |
| print(addr) | |
| return [addr, response] | |
| 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): | |
| server_status_code = gr.Textbox(label="Status code from OpenAI server", ) | |
| #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], [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], [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) | |