burntensor / app.py
pawkanarek's picture
fix the is subnet active calculations, but im still guesssing how to do it correctly
db5145d
import bittensor as bt
from substrateinterface import Keypair
import gradio as gr
import pandas as pd
import time
# primitive caching
g_cached_data: pd.DataFrame | None = None
g_last_fetch_time = 0.0
def fetch_incentive_data() -> pd.DataFrame:
data = []
subtensor = bt.subtensor(network="finney")
print("connected to subtensor")
subnets = subtensor.all_subnets()
print("fetched all subnets")
metagraphs = subtensor.get_all_metagraphs_info()
print("fetched all metagraphs")
assert subnets, "WTF"
assert metagraphs, "WTF"
for sn in range(1, 129):
subnet = subnets[sn]
metagraph = metagraphs[sn]
hotkeys_to_uid = {hk: i for i, hk in enumerate(metagraph.hotkeys)}
# The incentives that are assigned to the owner hotkey are being burned/not given out
# by Maciej Kula [Bo𝞃, Bo𝞃] 23.07.2025
addresses = [("hotkey", subnet.owner_hotkey)] # So don't include ("coldkey", subnet.owner_coldkey).
for key_type, address in addresses:
uid = hotkeys_to_uid.get(address, None)
if uid is None:
continue
incentive = metagraph.incentives[uid]
if incentive <= 0:
continue
is_active = metagraph.pending_root_emission.tao > 0 and metagraph.alpha_out_emission > 0 and metagraph.moving_price > 0
data.append([
f"[netuid: {sn} / {subnet.subnet_name}](https://taostats.io/subnets/{sn})",
is_active,
round(subnet.alpha_to_tao(1).tao, 6),
round(incentive*100, 2),
f"[{address}](https://taostats.io/{key_type}/{address}) [{uid}]"
])
break
df = pd.DataFrame(data, columns=["Subnet", "Active", "α to τ", "Burn (%)", "Address [UID]"]) # type: ignore
print(f"{len(data)} subnets burn")
return df
def get_cached_data() -> tuple[str, pd.DataFrame]:
global g_cached_data, g_last_fetch_time
if g_cached_data is None or (time.time() - g_last_fetch_time) > 1200: # 20 min
g_last_fetch_time = time.time()
g_cached_data = fetch_incentive_data()
time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(g_last_fetch_time + 1200))
return time_str, g_cached_data
with gr.Blocks(title="Bittensor Subnet Incentives") as demo:
gr.HTML(
"""
<div style="text-align: center">
<h1>Burntensor</h1>
<img src='https://huggingface.co/spaces/pawkanarek/burntensor/resolve/main/assets/burn.gif' widht=200 style="display: block; margin: 0 auto;" />
<h3>This dashboard displays the burn percentage set by subnet owners for miners. Fetching data takes ~1min</h3>
</div>
"""
)
next_process_text = gr.Textbox(label="Next refresh time", interactive=False)
output_df = gr.DataFrame(
datatype=["markdown", "bool", "number", "number", "markdown"],
label="Subnet Burn Data",
show_row_numbers=True,
interactive=False,
max_height=1000000
)
demo.load(get_cached_data, None, [next_process_text, output_df])
demo.launch()