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Configuration error
| import gradio as gr | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from dataset_util import load_data, get_num_rows | |
| import subnet_util | |
| import datetime | |
| import typing | |
| import indexing_util | |
| from io import BytesIO | |
| FONT = """<link href="https://fonts.cdnfonts.com/css/intersect-c-brk" rel="stylesheet">""" | |
| TITLE_FONT = """<link href="https://fonts.cdnfonts.com/css/promova" rel="stylesheet">""" | |
| TITLE = """ <h1 align = "center" id = "space-title" class = "intersect"> D3 Subnet Leaderboard</h1> """ | |
| DESCRIPTION = """<marquee><h3 align= "center"> The D3 Subnet, standing for Decentralized Distributed Data Scraping subnet, plays a crucial role in the advancement of artificial intelligence by ensuring ample training data for all Bittensor AI networks. </h3></marquee>""" | |
| IMAGE = """<a href="https://discord.com/channels/799672011265015819/1161764869280903240" target="_blank"><img src="https://cdn.discordapp.com/attachments/1204940599145267200/1227239850332131388/5CB42426-0E73-4D10-A66A-9E256C6A6183.png?ex=6627af2d&is=66153a2d&hm=02b9870618dd8e2e6bf62a0635f3cc8020221a0c7c2568138070f614e63a2068&" alt="D3 Subnet" style="margin: auto; width: 20%; border: 0;" /></a>""" | |
| last_refresh = None | |
| demo = gr.Blocks(css=""" | |
| .intersect {font-family: 'Intersect C BRK', sans-serif; font-size:40px} | |
| .promova {font-family: 'Promova', sans-serif; font-size:40px} | |
| """) | |
| twitter_text_dataset = load_data("bittensor-dataset/twitter-text-dataset") | |
| twitter_text_num_rows = get_num_rows(twitter_text_dataset) | |
| twitter_image_dataset = load_data("bittensor-dataset/twitter-image-dataset") | |
| twitter_image_num_rows = get_num_rows(twitter_image_dataset) | |
| tao_price = subnet_util.get_tao_price() | |
| (subtensor, metagraph) = subnet_util.get_subtensor_and_metagraph() | |
| last_refresh = datetime.datetime.now() | |
| miners_data = subnet_util.get_subnet_data(subtensor, metagraph) | |
| # url = "http://127.0.0.1:8001/" | |
| # try: | |
| # response = requests.get(url) | |
| # response_body = response.json() | |
| # twitter_text_num_rows = response_body['twitter_text_rows'] | |
| # twitter_image_num_rows = response_body['twitter_image_rows'] | |
| # except: | |
| # twitter_text_num_rows = 0 | |
| # twitter_image_num_rows = 0 | |
| # bt.logging.error("Could not connect to the API") | |
| daily_indexing_data = indexing_util.get_all(indexing_util.daily_indexing) | |
| daily_df = pd.DataFrame(daily_indexing_data, columns=['Date', 'Value']) | |
| daily_df['Date'] = pd.to_datetime(daily_df['Date'].str.decode('utf-8')) | |
| daily_df['Value'] = daily_df['Value'].astype(int) | |
| hotkey_indexing_data = indexing_util.get_all(indexing_util.hotkey_indexing) | |
| hotkey_df = pd.DataFrame(hotkey_indexing_data, columns=['Hotkey', 'Value']) | |
| hotkey_df['Hotkey'] = hotkey_df['Hotkey'].str.decode('utf-8') | |
| hotkey_df['Value'] = hotkey_df['Value'].astype(int) | |
| hotkey_daily_indexing_data = indexing_util.get_all(indexing_util.hotkey_daily_indexing) | |
| hotkey_daily_df = pd.DataFrame(hotkey_daily_indexing_data, columns=['Hotkey_Date', 'Value']) | |
| hotkey_daily_df_= pd.DataFrame() | |
| hotkey_daily_df_['Hotkey'] = hotkey_daily_df['Hotkey_Date'].str.decode('utf-8').str.split(' ').str[0] | |
| hotkey_daily_df_['Date'] = hotkey_daily_df['Hotkey_Date'].str.decode('utf-8').str.split(' ').str[1] | |
| hotkey_daily_df_['Value'] = hotkey_daily_df['Value'].astype(int) | |
| print(hotkey_daily_df_) | |
| def leaderboard_data( | |
| # show_stale: bool, | |
| # scores: typing.Dict[int, typing.Dict[str, typing.Optional[float | str]]], | |
| # competition_id: str, | |
| ): | |
| value = [ | |
| [ | |
| c.hotkey[0:8], | |
| c.uid, | |
| c.url, | |
| c.block, | |
| ] | |
| for c in miners_data | |
| # if c.incentive and c.url[0:8] == "https://" | |
| ] | |
| return value | |
| with demo: | |
| gr.HTML(FONT) | |
| gr.HTML(TITLE_FONT) | |
| gr.HTML(TITLE) | |
| gr.HTML(IMAGE) | |
| gr.HTML(DESCRIPTION) | |
| with gr.Tabs(): | |
| with gr.Accordion("Dataset Stats"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.HTML(f"<h2 align = 'center' style = 'font-size: 25px' >Current Size of Text Dataset: <span style = 'font-size: 30px; color: green;'>{twitter_text_num_rows}</span></h2>") | |
| with gr.Column(scale=1): | |
| gr.HTML(f"<h2 align = 'center' style = 'font-size: 25px' >Current Size of Image Dataset: <span style = 'font-size: 30px; color: green;'>{twitter_image_num_rows}</span></h2>") | |
| with gr.Accordion("Subnet Stats"): | |
| gr.HTML(f"""<h2 align = 'center' class="promova" style = 'font-size: 35px;' > Miner Stats</h2>""") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.BarPlot( | |
| daily_df, | |
| x="Date", | |
| y="Value", | |
| title="Daliy scraped data amount", | |
| # color="Date", | |
| tooltip=["Date", "Value"], | |
| y_lim=[0, 1000], | |
| x_title="Date", | |
| y_title="Amount of data scraped", | |
| height=500, | |
| width=500, | |
| scale=5, | |
| color="Value", | |
| color_legend_position="top", | |
| # elem_classes="daily_scraped_data", | |
| ) | |
| with gr.Column(scale=1): | |
| gr.BarPlot( | |
| hotkey_df, | |
| x="Hotkey", | |
| y="Value", | |
| title="Scraped data amount of each Miner", | |
| # color="Date", | |
| tooltip=["Hotkey", "Value"], | |
| y_lim=[0, 1000], | |
| x_title="Date", | |
| y_title="Amount of data scraped", | |
| height=500, | |
| width=500, | |
| scale=5, | |
| color="Value", | |
| x_label_angle=-30, | |
| color_legend_position="top", | |
| # elem_classes="daily_scraped_data", | |
| ) | |
| gr.ScatterPlot( | |
| hotkey_daily_df_, | |
| x="Date", | |
| y="Value", | |
| title="Daily scraped data amount of each Miner", | |
| # color="Date", | |
| tooltip=["Hotkey"], | |
| y_lim=[0, 1000], | |
| x_title="Date", | |
| y_title="Amount of data scraped", | |
| height=500, | |
| width=1000, | |
| scale=5, | |
| color="Hotkey", | |
| x_label_angle=-30, | |
| color_legend_position="top", | |
| # elem_classes="daily_scraped_data", | |
| ) | |
| with gr.Tab(label="Miners Data"): | |
| class_denominator = sum( | |
| miners_data[i].incentive #TODO: emssion to incentive | |
| for i in range(0, min(10, len(miners_data))) | |
| if miners_data[i].incentive | |
| ) | |
| class_values = { | |
| f"(uid={miners_data[i].uid}, hotkey={miners_data[i].hotkey[0:8]}) - {miners_data[i].url} · ${round(miners_data[i].emission * tao_price, 2):,} (τ{round(miners_data[i].emission, 2):,})": miners_data[i].incentive / class_denominator | |
| for i in range(0, min(10, len(miners_data))) | |
| if miners_data[i].incentive | |
| } | |
| gr.Label( | |
| label="Top 10 Miners", | |
| value=class_values, | |
| num_top_classes=10, | |
| ) | |
| # miner_table = gr.components.Dataframe( | |
| # value=miners_data | |
| # ) | |
| with gr.Accordion("Miner stats"): | |
| gr.HTML( | |
| f"""<h3>{last_refresh.strftime("refreshed at %H:%M on %Y-%m-%d")}</h3>""" | |
| ) | |
| # with gr.Tabs(): | |
| # for entry in miners_data: | |
| # name = f"uid={entry.uid} : commit={entry.commit[0:8]} : url={entry.url}" | |
| # with gr.Tab(name): | |
| # gr.Chatbot() | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_data(), | |
| headers = [ | |
| "Hotkey", | |
| "UID", | |
| "Url", | |
| "Block", | |
| ], | |
| datatype=[ | |
| "markdown", | |
| "number", | |
| "markdown", | |
| "number", | |
| ], | |
| elem_id="leaderboard_table", | |
| interactive=False, | |
| visible=True, | |
| ) | |
| demo.launch() |