Update app.py

app.py

@@ -9,9 +9,9 @@ RESULT_DIR = os.environ.get("MOECAP_RESULT_DIR")
 if not RESULT_DIR:
     # For testing purposes, you can uncomment the line below:
     # RESULT_DIR = "generic_result_dir" 
-    raise RuntimeError(
-        "MOECAP_RESULT_DIR is not set. Please set MOECAP_RESULT_DIR (HF Repo ID) before running app.py"
-    )
+    # If you are running locally without this env var, 
+    # ensure you handle this error or set the var.
+    pass 
 
 import gradio as gr
 import pandas as pd
@@ -214,7 +214,7 @@ def json_to_row(path: str, metrics: dict) -> dict:
         "Model type": model_type,
         "Precision": precision,
         "E2E(s)": f2(e2e_s),                  
-        "GPU": gpu_type,                    
+        "GPU": gpu_type,                     
         "Accuracy(%)": pct(acc),
         "Cost($)": cost,
         "Decoding T/s": f2(metrics.get("decoding_throughput")),
@@ -231,12 +231,16 @@ def json_to_row(path: str, metrics: dict) -> dict:
 
 
 def load_from_dir(dir_path: str, selected_tasks=None, selected_frameworks=None, selected_model_types=None, selected_precisions=None, search_keyword="", force_refresh=False):
+    if not dir_path: 
+        return "<p style='color:black'>Result Directory not set.</p>", []
+
     try:
         pattern = f"hf://datasets/{dir_path}/**/*.json"
         dl_mode = "force_redownload" if force_refresh else None
         print(f"Fetching from {pattern} (mode={dl_mode})...")
         ds = load_dataset("json", data_files={"train": pattern}, split="train", download_mode=dl_mode)
-    except Exception:
+    except Exception as e:
+        print(f"Error loading dataset: {e}")
         return "<p style='color:black'>No files loaded or Dataset not found.</p>", []
 
     rows = []
@@ -249,6 +253,10 @@ def load_from_dir(dir_path: str, selected_tasks=None, selected_frameworks=None,
 
     df = pd.DataFrame(rows)
 
+    # --- Filtering Logic ---
+    # This logic is consistent: if a filter is provided, we ONLY keep rows 
+    # where the column value is inside the selected list.
+    
     if selected_tasks:
         df = df[df["Dataset"].astype(str).str.lower().isin([x.lower() for x in selected_tasks])]
     if selected_frameworks:
@@ -316,7 +324,6 @@ def build_app() -> gr.Blocks:
     }
 
     /* 3. SPECIFIC FIX FOR THE DARK "FILTERS" and "RADAR" SECTIONS */
-    /* This targets the class you added in python: elem_classes="filter-section" */
     .filter-section {
         background-color: #ffffff !important;
         border: 2px solid #e1e4e8 !important;
@@ -325,7 +332,6 @@ def build_app() -> gr.Blocks:
         box-shadow: 0 2px 4px rgba(0,0,0,0.05) !important;
     }
     
-    /* Ensure NO child elements inside filter-section have dark backgrounds */
     .filter-section * {
         background-color: transparent !important;
         color: #24292e !important;
@@ -340,11 +346,21 @@ def build_app() -> gr.Blocks:
         color: #24292e !important;
     }
 
-    /* Fix Checkboxes: Ensure the box itself is visible */
+    /* --- FIX FOR CHECKBOXES --- */
+    /* Problem was here: `background-color: #ffffff !important` prevented 
+       the browser from painting the blue checked state.
+       We removed background-color !important and rely on accent-color.
+    */
     .filter-section input[type="checkbox"] {
-        background-color: #ffffff !important;
         border: 1px solid #d1d5da !important;
         accent-color: #0366d6 !important;
+        /* Do NOT force background-color to white with !important here, 
+           or the checkmark will be invisible */
+    }
+    
+    /* Ensure label text next to checkbox is visible */
+    .filter-section label span {
+        color: #24292e !important;
     }
 
     /* Fix "How to use" Text (Markdown/Prose) */