add caching

app.py

@@ -2,6 +2,11 @@ import bittensor as bt
 from substrateinterface import Keypair
 import gradio as gr
 import pandas as pd
+import time
+
+# uga-buga caching
+g_cached_data: pd.DataFrame | None = None
+g_last_fetch_time = 0.0
 
 
 def get_incentive_percent_for_key(key, sn, incentive, substrate) -> tuple[float, int]:
@@ -43,29 +48,34 @@ def fetch_incentive_data() -> pd.DataFrame:
                     address_link = f"[{owner_hk}](https://taostats.io/hotkey/{owner_hk})"
                     data.append([sn_link, f"{hk_incentive:.2f}%", hk_uid, "Hotkey", address_link])
                     break
-        
-    df = pd.DataFrame(data, columns=["Subnet", "Incentive", "UID", "Key Type", "Address"]) #type: ignore
+
+    data = [(i+1, *d) for i, d in enumerate(data)] 
+    df = pd.DataFrame(data, columns=["#", "Subnet", "Burn", "UID", "Key", "Address"]) # type: ignore
     return df
 
 
+def get_cached_data() -> 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()
+    return g_cached_data
+
+
 with gr.Blocks(title="Bittensor Subnet Incentives") as demo:
     gr.Markdown(
         """
         # Bittensor Subnet Burn Dashboard
         This dashboard displays the burn percentage set by subnet owners for miners.
-        Click the 'Refresh' button to fetch the latest data.
         """
     )
 
-    refresh_button = gr.Button("Refresh Data")
     output_df = gr.DataFrame(
-        headers=["Subnet", "Burn", "UID", "Key", "Address"],
-        datatype=["markdown", "str", "number", "str", "markdown"],
-        label="Subnet Incentive Data",
+        datatype=["number", "markdown", "str", "number", "str", "markdown"],
+        label="Subnet Burn Data",
         interactive=False,
-        row_count=(60, "dynamic"),
-        col_count=(5, "fixed")
+        row_count=(12, "dynamic"),
+        col_count=(6, "fixed")
     )
-
-    demo.load(fetch_incentive_data, None, output_df)
+    demo.load(get_cached_data, None, output_df)
     demo.launch()