import gradio as gr
import pandas as pd
import bittensor as bt
import requests
from apscheduler.schedulers.background import BackgroundScheduler
# 1. Define data functions first
def get_validator_data() -> pd.DataFrame:
if subtensor is None or metagraph is None:
return pd.DataFrame(columns=['Name', 'UID', 'Axon', 'API', 'Step', 'Recent Bits', 'Updated', 'VTrust'])
try:
validator_ids = list(set([i for i in range(len(metagraph.validator_permit))
if metagraph.validator_permit[i] and
metagraph.active[i] and
str(metagraph.axons[i].ip) != "0.0.0.0"]))
except Exception as e:
print(f"Error getting validator IDs: {e}")
validator_ids = []
current_block = subtensor.block
results = []
for uid in validator_ids:
validator_info = {
'Name': 'unavailable',
'UID': uid,
'Axon': 'unavailable',
'Step': 0,
'Recent Bits': 0,
'Updated': 0,
'VTrust': 0,
'API': '❌'
}
try:
# Get validator name
try:
identity = subtensor.substrate.query('SubtensorModule', 'Identities', [metagraph.coldkeys[uid]])
validator_info['Name'] = identity.value["name"] if identity != None else 'unnamed'
except Exception as e:
print(f"Error getting Name for UID {uid}: {str(e)}")
validator_info['Axon'] = f"{metagraph.axons[uid].ip}:{metagraph.axons[uid].port}"
# Get Step and Range from endpoints
try:
axon_endpoint = f"http://{validator_info['Axon']}"
step_response = requests.get(f"{axon_endpoint}/step", timeout=(2, 3))
step_response.raise_for_status()
validator_info['Step'] = step_response.json()
bits_response = requests.get(
f"{axon_endpoint}/bits",
headers={"range": "bytes=-1"},
timeout=(2, 3)
)
bits_response.raise_for_status()
binary_string = ''.join(format(byte, '08b') for byte in bits_response.content)
validator_info['Recent Bits'] = binary_string[-8:]
validator_info['API'] = '✅' if bits_response.ok else '❌'
except requests.Timeout:
validator_info['API'] = '❌'
except Exception as e:
if not isinstance(e, requests.Timeout):
print(f"Error connecting to {axon_endpoint}: {str(e)}")
validator_info['API'] = '❌'
try:
last_update = int(metagraph.last_update[uid])
validator_info['Updated'] = current_block - last_update
except Exception as e:
print(f"Error getting Updated for UID {uid}: {str(e)}")
try:
validator_info['VTrust'] = float(metagraph.validator_trust[uid])
except Exception as e:
print(f"Error getting VTrust for UID {uid}: {str(e)}")
except Exception as e:
print(f"Error getting Axon for UID {uid}: {str(e)}")
results.append(validator_info)
df = pd.DataFrame(results)
df['VTrust'] = df['VTrust'].round(4)
return df.sort_values('Step', ascending=False)[['Name', 'UID', 'Axon', 'API', 'Step', 'Recent Bits', 'Updated', 'VTrust']]
# 2. Initialize bittensor and load data
try:
subtensor = bt.subtensor()
metagraph = bt.metagraph(netuid=36)
initial_df = get_validator_data()
initial_timestamp = pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S UTC")
print("✅ Data loaded successfully")
except Exception as e:
print(f"❌ Failed to initialize: {e}")
initial_df = pd.DataFrame()
initial_timestamp = "Failed to load initial data"
# 3. Then CSS and UI components
background_url = "https://cdn-lfs.hf.co/repos/a4/b4/a4b48e51a6a5ebd9414fc6798da9acf09a6a9425ea160334a1a81c4ad3fdb801/7f89926e2018d54403ac1dc8b0d6fe2401a6489d7da11df27259a07cde7acf87?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27background2.png%3B+filename%3D%22background2.png%22%3B&response-content-type=image%2Fpng&Expires=1731722075&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczMTcyMjA3NX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy9hNC9iNC9hNGI0OGU1MWE2YTVlYmQ5NDE0ZmM2Nzk4ZGE5YWNmMDlhNmE5NDI1ZWExNjAzMzRhMWE4MWM0YWQzZmRiODAxLzdmODk5MjZlMjAxOGQ1NDQwM2FjMWRjOGIwZDZmZTI0MDFhNjQ4OWQ3ZGExMWRmMjcyNTlhMDdjZGU3YWNmODc%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=Y9KvhLMB7xHfQUnq2RC4rMDegTBzbqoMiHAIJzVKy%7E7ajPw2p-AQ19KCYCaXPNbElSV7PZowowMOS%7EtK98A7SM4ndHDePx3OcD%7EDNP8w150rLj58XeCZuVSZ32ayv%7Eb6nZEwYjUbrtrFboN5T9HG8xezW7BgmcXzV3iHppgSNu%7EnwKwJvorVr%7EyddXC6AMsAjsYKYOl1AxnkiMiIKeoD7Rd4ZaAlQsbqAC31BfnbSkBfPq4g0HlCiQYvYsxoofyLsGslnx9X5yIPaYRIRz3uJibPwDZg6GCEYViOfKWwiWV74iA1ptt2DeWLSxBRkjRfnv-HMAs25GmMjqyF85o8vA__&Key-Pair-Id=K3RPWS32NSSJCE"
custom_css = """
#component-0 {
max-width: 100% !important;
padding: 0 !important;
margin: 0 !important;
}
.gradio-container {
max-width: 100% !important;
padding: 0 !important;
margin: 0 !important;
background-image: url('""" + background_url + """') !important;
background-size: cover !important;
background-position: center !important;
background-repeat: no-repeat !important;
min-height: 100vh !important;
}
.header-box {
text-align: center;
max-width: 100%;
margin: 20px auto;
padding: 1rem;
background-color: rgba(17, 24, 39, 0.95); /* Darker, more opaque background */
border-radius: 1rem;
}
.header-box h1 {
color: white;
margin: 0;
font-size: 2rem;
}
.header-box p {
color: white; /* Changed to white instead of gray */
margin-top: 0.5rem;
}
"""
# Data functions first
def fetch_data():
# your data fetching logic
pass
def create_leaderboard():
try:
data = fetch_data()
if not data:
return pd.DataFrame()
return data
except Exception as e:
print(f"Error creating leaderboard: {e}")
return pd.DataFrame()
def update_data():
try:
data = create_leaderboard()
error_msg = ""
success = True
except Exception as e:
data = pd.DataFrame()
error_msg = f"Error loading data: {str(e)}"
success = False
return data, error_msg, not success
# Add this before creating the app
header_html = """
"""
# UI components last
# Create the Gradio interface with custom theme
app = gr.Blocks(
title="SN36 Validator Leaderboard",
css=custom_css,
theme=gr.themes.Soft().set(
body_background_fill="rgba(17, 24, 39, 0.95)",
background_fill_secondary="rgba(17, 24, 39, 0.95)",
)
)
with app:
gr.HTML(header_html)
with gr.Tabs(elem_id="main-tabs"):
with gr.Tab("📊 Leaderboard", elem_id="leaderboard-tab"):
# Initialize with preloaded data
leaderboard = gr.DataFrame(
value=initial_df,
headers=["Name", "UID", "Axon", "API", "Step", "Recent Bits", "Updated", "VTrust"],
datatype=["str", "number", "str", "html", "number", "str", "number", "number"],
elem_id="leaderboard-table",
render=True
)
# Initialize with preloaded timestamp
status_message = gr.Markdown(
value=f"Last updated: {initial_timestamp}",
elem_classes=["status-msg"]
)
with gr.Row(equal_height=True):
refresh_button = gr.Button("🔄 Refresh Data", variant="primary", elem_classes=["refresh-btn"])
auto_refresh = gr.Checkbox(
label="Auto-refresh (5 min)",
value=True,
interactive=True
)
with gr.Tab("ℹ️ About"):
gr.Markdown(
"""
## About this Leaderboard
This dashboard shows real-time information about validators on the network:
- **Name**: Validator's registered name on the network
- **UID**: Unique identifier of the validator
- **Axon**: Validator's Axon address (IP:port)
- **API**: API status (✅ online, ❌ offline)
- **Step**: Current step count (0 if unavailable)
- **Range**: Validator's bit range (0 if unavailable)
- **Updated**: Blocks since last update (0 if unavailable)
- **VTrust**: Validator's trust score (0 if unavailable)
Data is automatically refreshed every 5 minutes, or you can manually refresh using the button.
"""
)
def update_leaderboard():
df = get_validator_data()
timestamp = pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S UTC")
return df, f"Last updated: {timestamp}"
refresh_button.click(
fn=update_leaderboard,
outputs=[leaderboard, status_message],
queue=False
)
# Auto-refresh logic
def setup_auto_refresh():
app.scheduler = BackgroundScheduler()
app.scheduler.add_job(
lambda: app.queue(update_leaderboard),
'interval',
minutes=5
)
app.scheduler.start()
# Initial data load
app.load(
fn=update_leaderboard,
outputs=[leaderboard, status_message]
)
setup_auto_refresh()
# Launch the interface with file serving enabled
app.launch(
share=False
)