Update app.py

app.py

@@ -63,7 +63,7 @@ color_palette = px.colors.sequential.Viridis
 # Visualisation for Domain Distribution
 def create_pie_chart(df, column, title):
     fig = px.pie(df, names=column, title=title, hole=0.35)
-    fig.update_layout(margin=dict(l=6, r=6, t=12, b=6), legend=dict(x=0.1, y=1), font=dict(size=7)
+    fig.update_layout(margin=dict(l=6, r=6, t=12, b=6), legend=dict(x=0.1, y=1), font=dict(size=7))
     fig.update_traces(marker=dict(colors=color_palette))
     return fig
 
@@ -71,7 +71,7 @@ def create_pie_chart(df, column, title):
 def create_gender_ethnicity_distribution_chart(df):
     df['GenderOrEthnicity'] = df['Domain'].apply(lambda x: "Gender: Women & LGBTQIA+" if x in ["Women", "LGBTQIA+"] else "Ethnicity")
     fig = px.pie(df, names='GenderOrEthnicity', title='Distribution of Gender versus Ethnicity', hole=0.35)
-    fig.update_layout(margin=dict(l=6, r=6, t=12, b=6), legend=dict(x=0.1, y=1), font=dict(size=7)
+    fig.update_layout(margin=dict(l=6, r=6, t=12, b=6), legend=dict(x=0.1, y=1), font=dict(size=7))
     return fig
 
 # Visualization for Sentiment Distribution Across Domains