add alpha to tao price, add is active checkbox, make row indexes show automagically

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 import pandas as pd
 import time
 
-# uga-buga caching
+# primitive caching
 g_cached_data: pd.DataFrame | None = None
 g_last_fetch_time = 0.0
 
@@ -24,13 +24,14 @@ def fetch_incentive_data() -> pd.DataFrame:
     for sn in range(1, 129):
         subnet = subnets[sn]
         metagraph = metagraphs[sn]
-        address_to_uid = {hk: i for i, hk in enumerate(metagraph.hotkeys)}
+        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.
+        addresses = [("hotkey", subnet.owner_hotkey)] # So don't include ("coldkey", subnet.owner_coldkey).
 
         for key_type, address in addresses:
-            uid = address_to_uid.get(address, None)
+            uid = hotkeys_to_uid.get(address, None)
             if uid is None:
                 continue
 
@@ -38,16 +39,17 @@ def fetch_incentive_data() -> pd.DataFrame:
             if incentive <= 0:
                 continue
 
+            is_active = metagraph.pending_root_emission.tao > 0
             data.append([
                 f"[netuid: {sn} / {subnet.subnet_name}](https://taostats.io/subnets/{sn})",
-                incentive*100,
-                uid,
-                f"[{address}](https://taostats.io/{key_type}/{address})"
+                is_active,
+                round(subnet.alpha_to_tao(1).tao, 6),
+                round(incentive*100, 2),
+                f"[{address}](https://taostats.io/{key_type}/{address}) [{uid}]"
             ])
             break
 
-    data = [(i+1, *d) for i, d in enumerate(data)] 
-    df = pd.DataFrame(data, columns=["#", "Subnet", "Burn (%)", "UID", "Address"]) # type: ignore
+    df = pd.DataFrame(data, columns=["Subnet", "Active", "α to τ", "Burn (%)", "Address [UID]"]) # type: ignore
     print(f"{len(data)} subnets burn")
     return df
 
@@ -74,8 +76,9 @@ with gr.Blocks(title="Bittensor Subnet Incentives") as demo:
     )
     next_process_text = gr.Textbox(label="Next refresh time", interactive=False)
     output_df = gr.DataFrame(
-        datatype=["number", "markdown", "number", "number", "markdown"],
+        datatype=["markdown", "bool", "number", "number", "markdown"],
         label="Subnet Burn Data",
+        show_row_numbers=True,
         interactive=False,
         max_height=1000000
     )