add block

app.py

@@ -17,7 +17,7 @@ load_dotenv()
 TITLE = """<h1 align="center" id="space-title">Subnet 6 Leaderboard</h1>"""
 IMAGE = """<a href="https://discord.gg/jqVphNsB4H" target="_blank"><img src="https://i.ibb.co/88wyVQ7/nousgirl.png" alt="nousgirl" style="margin: auto; width: 20%; border: 0;" /></a>"""
 HEADER = """<h2 align="center"><a href="https://github.com/NousResearch/finetuning-subnet" target="_blank">Subnet 6</a> is a <a href="https://bittensor.com/" target="_blank">Bittensor</a> subnet that incentivizes the creation of the best open models by evaluating submissions on a constant stream of newly generated syntheic GPT-4 data. The models with the best head-to-head loss on the evaluation data receive a steady emission of TAO.</h3>"""
-DETAILS = """<b>Name</b> is the 🤗 Hugging Face model name (click to go to the model card). <b>Rewards / Day</b> are the expected rewards per day for each model. <b>Last Average Loss</b> is the last loss value on the evaluation data for the model as calculated by a validator (lower is better). <b>UID</b> is the Bittensor user id of the submitter. More stats on <a href="https://taostats.io/subnets/netuid-6/" target="_blank">taostats</a>."""
+DETAILS = """<b>Name</b> is the 🤗 Hugging Face model name (click to go to the model card). <b>Rewards / Day</b> are the expected rewards per day for each model. <b>Last Average Loss</b> is the last loss value on the evaluation data for the model as calculated by a validator (lower is better). <b>UID</b> is the Bittensor user id of the submitter. <b>Block</b> is the Bittensor block that the model was submitted in. More stats on <a href="https://taostats.io/subnets/netuid-6/" target="_blank">taostats</a>."""
 VALIDATOR_WANDB_PROJECT = os.environ["VALIDATOR_WANDB_PROJECT"]
 H4_TOKEN = os.environ.get("H4_TOKEN", None)
 API = HfApi(token=H4_TOKEN)
@@ -133,13 +133,14 @@ with demo:
             f'[{c.namespace}/{c.name}](https://huggingface.co/{c.namespace}/{c.name})',
             f'${round(c.emission * tao_price, 2):,} (τ{round(c.emission, 2):,})',
             f'{round(losses[c.uid], 4) if c.uid in losses.keys() else ""}',
-            c.uid
+            c.uid,
+            c.block
         ] for c in leaderboard_df
     ]
     leaderboard_table = gr.components.Dataframe(
         value=value,
-        headers=["Name", "Rewards / Day", "Last Average Loss", "UID",],
-        datatype=["markdown", "number", "number", "number"],
+        headers=["Name", "Rewards / Day", "Last Average Loss", "UID", "Block"],
+        datatype=["markdown", "str", "number", "number", "number"],
         elem_id="leaderboard-table",
         interactive=False,
         visible=True,