Update utils.py

utils.py

@@ -12,12 +12,12 @@ SUBJECTS = ["Biology", "Business", "Chemistry", "Computer Science", "Economics",
             "Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"]
 
 MODEL_INFO = [
-    "Models", "Data Source",
+    "Models", "Data Source", "Model Size(B)",
     "Overall",
     "Biology", "Business", "Chemistry", "Computer Science", "Economics", "Engineering",
     "Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"]
 
-DATA_TITLE_TYPE = ['markdown', 'markdown', 'number', 'number', 'number', 'number', 'number', 'number',
+DATA_TITLE_TYPE = ['markdown', 'markdown', 'markdown', 'number', 'number', 'number', 'number', 'number', 'number',
                    'number', 'number', 'number', 'number', 'number', 'number', 'number',
                    'number', 'number']
 
@@ -143,8 +143,25 @@ def add_new_eval(
 def refresh_data():
     return get_df()
 
+
+def search_and_filter_models(df, query, min_size, max_size):
+    if query:
+        df = df[df['Models'].str.contains(query, case=False, na=False)]
+    
+    df = df[(df['Model Size'] >= min_size) & (df['Model Size'] <= max_size)]
+    
+    return df
+
+
 def search_models(df, query):
     if query:
         return df[df['Models'].str.contains(query, case=False, na=False)]
     return df
 
+
+def get_size_range(df):
+    sizes = df['Model Size'].dropna()
+    if len(sizes) > 0:
+        return float(sizes.min()), float(sizes.max())
+    return 0, 500 
+