Update utils.py

utils.py

@@ -138,25 +138,43 @@ def add_new_eval(
     else:
         print('The entry already exists')
 
-
 def refresh_data():
-    df = get_df() 
-    min_size, max_size = get_size_range(df)
-    filtered_df = search_and_filter_models(df, "", min_size, max_size)
-    return filtered_df[COLUMN_NAMES]
+    df = get_df()
+    return df[COLUMN_NAMES]
+
+# def refresh_data():
+#     df = get_df() 
+#     min_size, max_size = get_size_range(df)
+#     filtered_df = search_and_filter_models(df, "", min_size, max_size)
+#     return filtered_df[COLUMN_NAMES]
 
 
+# def search_and_filter_models(df, query, min_size, max_size):
+#     if query:
+#         df = df[df['Models'].str.contains(query, case=False, na=False)]
+    
+#     numeric_mask = df['Model Size(B)'].apply(lambda x: isinstance(x, (int, float)))
+#     size_filtered = df[numeric_mask & 
+#                       (df['Model Size(B)'] >= min_size) & 
+#                       (df['Model Size(B)'] <= max_size)]
+#     unknown_entries = df[df['Model Size(B)'] == 'unknown']
+    
+#     return pd.concat([size_filtered, unknown_entries])[COLUMN_NAMES]
+
 def search_and_filter_models(df, query, min_size, max_size):
+    filtered_df = df.copy()
+
     if query:
-        df = df[df['Models'].str.contains(query, case=False, na=False)]
+        filtered_df = filtered_df[filtered_df['Models'].str.contains(query, case=False, na=False)]
     
-    numeric_mask = df['Model Size(B)'].apply(lambda x: isinstance(x, (int, float)))
-    size_filtered = df[numeric_mask & 
-                      (df['Model Size(B)'] >= min_size) & 
-                      (df['Model Size(B)'] <= max_size)]
-    unknown_entries = df[df['Model Size(B)'] == 'unknown']
+    def size_filter(x):
+        if isinstance(x, (int, float)):
+            return min_size <= x <= max_size
+        return True 
     
-    return pd.concat([size_filtered, unknown_entries])[COLUMN_NAMES]
+    filtered_df = filtered_df[filtered_df['Model Size(B)'].apply(size_filter)]
+    
+    return filtered_df[COLUMN_NAMES]
 
 
 def search_models(df, query):
@@ -173,10 +191,11 @@ def get_size_range(df):
 
 
 def process_model_size(size):
-    if size == 'unk':
+    if pd.isna(size) or size == 'unk':
         return 'unknown'
     try:
-        return float(size)
-    except ValueError:
+        val = float(size)
+        return val
+    except (ValueError, TypeError):
         return 'unknown'