chore: Update app.py to include select_columns and hide_columns in init_leaderboard function

app.py

@@ -64,21 +64,9 @@ def init_leaderboard(dataframe):
         ),
         search_columns=[AutoEvalColumn.model.name],
         hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
-        # filter_columns=[
-        #     ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
-        #     ColumnFilter(AutoEvalColumn.precision.name, type="dropdown", label="Precision"),
-        #     ColumnFilter(
-        #         AutoEvalColumn.params.name,
-        #         type="slider",
-        #         min=0.01,
-        #         max=150,
-        #         label="Select the number of parameters (B)",
-        #     ),
-        #     ColumnFilter(
-        #         AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
-        #     ),
-        # ],
-        # bool_checkboxgroup_label="Hide models",
+        filter_columns=[
+            ColumnFilter(AutoEvalColumn.model.name, type="dropdown", label="Select Model"),
+        ],
         interactive=False,
     )
 

src/display/utils.py

@@ -51,57 +51,6 @@ class ModelDetails:
     symbol: str = "" # emoji
 
 
-class ModelType(Enum):
-    PT = ModelDetails(name="pretrained", symbol="🟢")
-    FT = ModelDetails(name="fine-tuned", symbol="🔶")
-    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
-    RL = ModelDetails(name="RL-tuned", symbol="🟦")
-    Unknown = ModelDetails(name="", symbol="?")
-
-    def to_str(self, separator=" "):
-        return f"{self.value.symbol}{separator}{self.value.name}"
-
-    @staticmethod
-    def from_str(type):
-        if "fine-tuned" in type or "🔶" in type:
-            return ModelType.FT
-        if "pretrained" in type or "🟢" in type:
-            return ModelType.PT
-        if "RL-tuned" in type or "🟦" in type:
-            return ModelType.RL
-        if "instruction-tuned" in type or "⭕" in type:
-            return ModelType.IFT
-        return ModelType.Unknown
-
-class WeightType(Enum):
-    Adapter = ModelDetails("Adapter")
-    Original = ModelDetails("Original")
-    Delta = ModelDetails("Delta")
-
-class Precision(Enum):
-    float16 = ModelDetails("float16")
-    bfloat16 = ModelDetails("bfloat16")
-    float32 = ModelDetails("float32")
-    #qt_8bit = ModelDetails("8bit")
-    #qt_4bit = ModelDetails("4bit")
-    #qt_GPTQ = ModelDetails("GPTQ")
-    Unknown = ModelDetails("?")
-
-    def from_str(precision):
-        if precision in ["torch.float16", "float16"]:
-            return Precision.float16
-        if precision in ["torch.bfloat16", "bfloat16"]:
-            return Precision.bfloat16
-        if precision in ["float32"]:
-            return Precision.float32
-        #if precision in ["8bit"]:
-        #    return Precision.qt_8bit
-        #if precision in ["4bit"]:
-        #    return Precision.qt_4bit
-        #if precision in ["GPTQ", "None"]:
-        #    return Precision.qt_GPTQ
-        return Precision.Unknown
-
 # Column selection
 COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
 
@@ -109,4 +58,3 @@ EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
 EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
 
 BENCHMARK_COLS = [t.value.col_name for t in Tasks]
-