New text for tables

app.py

@@ -132,7 +132,9 @@ def filter_models(
 def get_macro_area_data():
     dataset = pd.read_csv("src/macro_area.csv", sep=',', skiprows=1)
     dataset = dataset.iloc[1:]
-
+    columns = ['Model', 'LI (108)', 'RM (179)', 'RC (33)', 'WF (7)',
+               'LS (29)', ' MO (24)', 'SP (4)', 'SY (19)', 'TP (6)']
+    dataset.columns = columns
     # dataset = dataset.style.highlight_max(color='lightgreen', axis=0)
     return dataset
 

src/about.py

@@ -249,9 +249,27 @@ CITATION_BUTTON_TEXT = r"""
 """
 
 QUESTION_FORMAT_TEXT = """
-Question Format evaluation
+Performance (accuracy %) comparison of AI models across school grades and question formats for grades 2 to 13.
+
+
+Due to the stratification and the limited number of questions in some categories, extreme values such as 100 or 0 are more attainable in the sections with few items. 
+
+
 """
 
 MACRO_AREA_TEXT = """"
-This table shows the evaluation of the models by macro area. The evaluation is based on the following metrics:
+Performance (accuracy %) comparison of AI models across macro areas. 
+The number of questions for each category is indicated in the table headers. 
+
+Categories are abbreviated as: 
+
+* _LI_: Locate and identify information within the text.
+* _RM_: Reconstruct the meaning of the text, locally or globally.
+* _RC_: Reflect on the content or form of the text, locally or globally, and evaluate them.
+* _WF_: Word formation.
+* _LS_: Lexicon and semantics.
+* _MO_: Morphology.
+* _SP_: Spelling.
+* _SY_: Syntax.
+* _TP_: Textuality and pragmatics.
 """