update leaderboard for multiple competitions

app.py

@@ -31,6 +31,14 @@ NETUID = 6
 SUBNET_START_BLOCK = 2225782
 SECONDS_PER_BLOCK = 12
 
+@dataclass
+class Competition:
+    id: str
+    name: str
+
+COMPETITIONS = [Competition(id="g1", name="gemma-7b"), Competition(id="m1", name="mistral-7b")]
+DEFAULT_COMPETITION_ID = "m1"
+
 def get_subtensor_and_metagraph() -> typing.Tuple[bt.subtensor, bt.metagraph]:
     for i in range(0, METAGRAPH_RETRIES):
         try:
@@ -54,6 +62,7 @@ class ModelData:
     block: int
     incentive: float
     emission: float
+    competition: str
 
     @classmethod
     def from_compressed_str(cls, uid: int, hotkey: str, cs: str, block: int, incentive: float, emission: float):
@@ -66,6 +75,7 @@ class ModelData:
             name=tokens[1],
             commit=tokens[2] if tokens[2] != "None" else None,
             hash=tokens[3] if tokens[3] != "None" else None,
+            competition=tokens[4] if len(tokens) > 4 and tokens[4] != "None" else DEFAULT_COMPETITION_ID,
             block=block,
             incentive=incentive,
             emission=emission
@@ -194,8 +204,11 @@ tao_price = get_tao_price()
 leaderboard_df = get_subnet_data(subtensor, metagraph)
 leaderboard_df.sort(key=lambda x: x.incentive, reverse=True)
 
-scores = get_scores([x.uid for x in leaderboard_df])
-
+competition_scores = {
+    y.id: get_scores([x.uid for x in leaderboard_df if x.competition == y.id])
+    for y in COMPETITIONS
+}
+    
 current_block = metagraph.block.item()
 next_update = next_tempo(
     SUBNET_START_BLOCK,
@@ -216,7 +229,7 @@ def get_next_update():
     delta = next_update_time - now
     return f"""<div align="center" style="font-size: larger;">Next reward update: <b>{blocks_to_go}</b> blocks (~{int(delta.total_seconds() // 60)} minutes)</div>"""
 
-def leaderboard_data(show_stale: bool):
+def leaderboard_data(show_stale: bool, scores: typing.Dict[int, typing.Dict[str, typing.Optional[float | str]]], competition_id: str):
     value = [
         [
             f'[{c.namespace}/{c.name} ({c.commit[0:8]})](https://huggingface.co/{c.namespace}/{c.name}/commit/{c.commit})',
@@ -225,7 +238,7 @@ def leaderboard_data(show_stale: bool):
             format_score(c.uid, scores, "weight"),
             c.uid,
             c.block
-        ] for c in leaderboard_df if scores[c.uid]["fresh"] or show_stale
+        ] for c in leaderboard_df if c.competition == competition_id and (scores[c.uid]["fresh"] or show_stale)
     ]
     return value
 
@@ -238,34 +251,40 @@ with demo:
 
     gr.HTML(value=get_next_update())
 
-    gr.Label(
-        value={ f"{c.namespace}/{c.name} ({c.commit[0:8]}) · ${round(c.emission * tao_price, 2):,} (τ{round(c.emission, 2):,})": c.incentive for c in leaderboard_df if c.incentive},
-        num_top_classes=10,
-    )
-    
-    with gr.Accordion("Evaluation Stats"):
-        gr.HTML(EVALUATION_HEADER)
-
-        with gr.Tabs():
-            for entry in leaderboard_df:
-                sample = scores[entry.uid]["sample"]
-                if sample is not None:
-                    name = f"{entry.namespace}/{entry.name} ({entry.commit[0:8]})"
-                    with gr.Tab(name):
-                        gr.Chatbot([(sample[0], sample[1])])
-                        # gr.Chatbot([(sample[0], f"*{name}*: {sample[1]}"), (None, f"*GPT-4*: {sample[2]}")])
-
-        show_stale = gr.Checkbox(label="Show Stale", interactive=True)
-        leaderboard_table = gr.components.Dataframe(
-            value=leaderboard_data(show_stale.value),
-            headers=["Name", "Win Rate", "Perplexity", "Weight", "UID", "Block"],
-            datatype=["markdown", "number", "number", "number", "number", "number"],
-            elem_id="leaderboard-table",
-            interactive=False,
-            visible=True,
-        )
-        gr.HTML(EVALUATION_DETAILS)
-        show_stale.change(leaderboard_data, [show_stale], leaderboard_table)
+    with gr.Tabs():
+        for competition in COMPETITIONS:
+            with gr.Tab(competition.name):
+                scores = competition_scores[competition.id]
+
+                gr.Label(
+                    value={ f"{c.namespace}/{c.name} ({c.commit[0:8]}) · ${round(c.emission * tao_price, 2):,} (τ{round(c.emission, 2):,})": c.incentive for c in leaderboard_df if c.incentive and c.competition == competition.id},
+                    num_top_classes=10,
+                )
+                
+                with gr.Accordion("Evaluation Stats"):
+                    gr.HTML(EVALUATION_HEADER)
+
+                    with gr.Tabs():
+                        for entry in leaderboard_df:
+                            if entry.competition == competition.id:
+                                sample = scores[entry.uid]["sample"]
+                                if sample is not None:
+                                    name = f"{entry.namespace}/{entry.name} ({entry.commit[0:8]})"
+                                    with gr.Tab(name):
+                                        gr.Chatbot([(sample[0], sample[1])])
+                                        # gr.Chatbot([(sample[0], f"*{name}*: {sample[1]}"), (None, f"*GPT-4*: {sample[2]}")])
+
+                    show_stale = gr.Checkbox(label="Show Stale", interactive=True)
+                    leaderboard_table = gr.components.Dataframe(
+                        value=leaderboard_data(show_stale.value, scores, competition.id),
+                        headers=["Name", "Win Rate", "Perplexity", "Weight", "UID", "Block"],
+                        datatype=["markdown", "number", "number", "number", "number", "number"],
+                        elem_id="leaderboard-table",
+                        interactive=False,
+                        visible=True,
+                    )
+                    gr.HTML(EVALUATION_DETAILS)
+                    show_stale.change(lambda x: leaderboard_data(x, scores, competition.id), [show_stale], leaderboard_table)
 
     with gr.Accordion("Validator Stats"):
         validator_table = gr.components.Dataframe(