Update app.py

app.py

@@ -461,47 +461,47 @@ def main():
                 outputs=leaderboard_table,
             )
 
-        # with gr.Accordion("Validator Stats"):
-        #     model_data.sort(key=lambda x: x.uid, reverse=False)
-
-        #     values = [
-        #             [uid, int(validator_df[uid][1]), round(validator_df[uid][0], 6)]
-        #             + [
-        #                 validator_df[uid][-1].get(c.uid)
-        #                 for c in model_data
-        #                 if c.incentive
-        #             ]
-        #             for uid, _ in sorted(
-        #                 zip(
-        #                     validator_df.keys(),
-        #                     [validator_df[x][1] for x in validator_df.keys()],
-        #                 ),
-        #                 key=lambda x: x[1],
-        #                 reverse=True,
-        #             )
-        #         ]
+        with gr.Accordion("Validator Stats"):
+            model_data.sort(key=lambda x: x.uid, reverse=False)
+
+            values = [
+                    [uid, int(validator_df[uid][1]), round(validator_df[uid][0], 6)]
+                    + [
+                        validator_df[uid][-1].get(c.uid)
+                        for c in model_data
+                        if c.incentive
+                    ]
+                    for uid, _ in sorted(
+                        zip(
+                            validator_df.keys(),
+                            [validator_df[x][1] for x in validator_df.keys()],
+                        ),
+                        key=lambda x: x[1],
+                        reverse=True,
+                    )
+                ]
             
-        #     print("VALUES:", values)
-        #     averages = np.nanmean(np.array(values, dtype=float)[:, 3:], axis=0)
-        #     averages = np.around(averages, decimals=6)
-
-        #     values.append(["Average", 0, 0] + averages.tolist())
-
-        #     gr.components.Dataframe(
-        #         value=values,
-        #         headers=["UID", "Stake (τ)", "V-Trust"]
-        #         + [
-        #             f"{c.uid}/reward"
-        #             for c in model_data
-        #             if c.incentive
-        #         ]
-        #         ,
-        #         datatype=["markdown", "number", "number"]
-        #         + ["number" for c in model_data if c.incentive]
-        #         ,
-        #         interactive=False,
-        #         visible=True,
-        #     )
+            print("VALUES:", values)
+            averages = np.nanmean(np.array(values, dtype=float)[:, 3:], axis=0)
+            averages = np.around(averages, decimals=6)
+
+            values.append(["Average", 0, 0] + averages.tolist())
+
+            gr.components.Dataframe(
+                value=values,
+                headers=["UID", "Stake (τ)", "V-Trust"]
+                + [
+                    f"{c.uid}/reward"
+                    for c in model_data
+                    if c.incentive
+                ]
+                ,
+                datatype=["markdown", "number", "number"]
+                + ["number" for c in model_data if c.incentive]
+                ,
+                interactive=False,
+                visible=True,
+            )
 
         def get_miner_stats(uid):
             local_stats = miners_stats[miners_stats['uid'] == int(uid)]