Feat: Clean empty fig

recopytex/dashboard/exam.py

@@ -42,7 +42,7 @@ app.layout = html.Div(
             color="success",
             dark=True,
         ),
-        html.Br(),
+        html.H2("Choix de l'évaluation"),
         dbc.Row(
             [
                 dbc.Col(
@@ -66,16 +66,16 @@ app.layout = html.Div(
                 ),
             ],
         ),
-        html.Br(),
+        html.H2("Résultats"),
         dbc.Row(
             [
                 dbc.Col(
                     dash_table.DataTable(
                         id="final_score_table",
                         columns=[
-                            {"id": "Élève", "name": "Élève"},
+                            {"id": "Eleve", "name": "Élève"},
                             {"id": "Note", "name": "Note"},
-                            {"id": "Barème", "name": "Barème"},
+                            {"id": "Bareme", "name": "Barème"},
                         ],
                         data=[],
                         style_data_conditional=[
@@ -118,8 +118,8 @@ app.layout = html.Div(
                 ),
             ],
         ),
-        html.Br(),
-        html.Div(
+        html.H2("Édition des notes"),
+        dbc.Row(
             [
                 dash_table.DataTable(
                     id="scores_table",
@@ -134,6 +134,7 @@ app.layout = html.Div(
                 dbc.Button("Ajouter un élément", id="btn_add_element"),
             ]
         ),
+        html.H2("Actions"),
         dcc.Store(id="final_score"),
     ]
 )
@@ -219,7 +220,7 @@ def update_final_scores_hist(data):
     assessment_scores = pd.DataFrame.from_records(data)
 
     if assessment_scores.empty:
-        return [{}]
+        return [{'data': [], 'layout':[]}]
 
     ranges = np.linspace(
         0, assessment_scores.Bareme.max(), int(assessment_scores.Bareme.max() * 2 + 1)
@@ -264,7 +265,7 @@ def update_competence_fig(data):
     scores = flat_df_students(scores).dropna(subset=["Score"])
 
     if scores.empty:
-        return [{}]
+        return [{'data': [], 'layout':[]}]
 
     scores = pp_q_scores(scores)
     pt = pd.pivot_table(