Update utils.py

utils.py

@@ -142,39 +142,36 @@ def refresh_data():
     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):
+    filtered_df = df.copy()
+    
+    if query:
+        filtered_df = filtered_df[filtered_df['Models'].str.contains(query, case=False, na=False)]
 
-# def search_and_filter_models(df, query, min_size, max_size):
-#     if query:
-#         df = df[df['Models'].str.contains(query, case=False, na=False)]
+    size_mask = filtered_df['Model Size(B)'].apply(lambda x: 
+        (min_size <= 1000.0 <= max_size) if x == 'unknown' 
+        else (min_size <= x <= max_size))
     
-#     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']
+    filtered_df = filtered_df[size_mask]
     
-#     return pd.concat([size_filtered, unknown_entries])[COLUMN_NAMES]
+    return filtered_df[COLUMN_NAMES]
 
-def search_and_filter_models(df, query, min_size, max_size):
-    filtered_df = df.copy()
 
-    if query:
-        filtered_df = filtered_df[filtered_df['Models'].str.contains(query, case=False, na=False)]
+# def search_and_filter_models(df, query, min_size, max_size):
+#     filtered_df = df.copy()
+
+#     if query:
+#         filtered_df = filtered_df[filtered_df['Models'].str.contains(query, case=False, na=False)]
     
-    def size_filter(x):
-        if isinstance(x, (int, float)):
-            return min_size <= x <= max_size
-        return True 
+#     def size_filter(x):
+#         if isinstance(x, (int, float)):
+#             return min_size <= x <= max_size
+#         return True 
     
-    filtered_df = filtered_df[filtered_df['Model Size(B)'].apply(size_filter)]
+#     filtered_df = filtered_df[filtered_df['Model Size(B)'].apply(size_filter)]
     
-    return filtered_df[COLUMN_NAMES]
+#     return filtered_df[COLUMN_NAMES]
 
 
 def search_models(df, query):
@@ -183,11 +180,16 @@ def search_models(df, query):
     return df
 
 
+# def get_size_range(df):
+#     numeric_sizes = df[df['Model Size(B)'].apply(lambda x: isinstance(x, (int, float)))]['Model Size(B)']
+#     if len(numeric_sizes) > 0:
+#         return float(numeric_sizes.min()), float(numeric_sizes.max())
+#     return 0, 1000
+
+
 def get_size_range(df):
-    numeric_sizes = df[df['Model Size(B)'].apply(lambda x: isinstance(x, (int, float)))]['Model Size(B)']
-    if len(numeric_sizes) > 0:
-        return float(numeric_sizes.min()), float(numeric_sizes.max())
-    return 0, 1000
+    sizes = df['Model Size(B)'].apply(lambda x: 1000.0 if x == 'unknown' else x)
+    return float(sizes.min()), float(sizes.max())
 
 
 def process_model_size(size):