Update app.py

app.py

@@ -200,7 +200,7 @@ elif tabs == 'Reports':
                 col1, col2 = st.columns([1, 1])
 
                 with col1:
-                    if not filtered_df.empty and filtered_df['Gender'].nunique() > 1:
+                    if 'Gender' in filtered_df.columns and filtered_df['Gender'].nunique() > 1 and pd.api.types.is_categorical_dtype(filtered_df['Gender']):
                         try:
                             fig_gender = px.histogram(filtered_df, x="Gender", title="Gender Distribution")
                             fig_gender.update_layout(height=400, width=500)
@@ -208,9 +208,9 @@ elif tabs == 'Reports':
                         except Exception as e:
                             st.write(f"Error generating Gender visualization: {e}")
                     else:
-                        st.write("Not enough data to display Gender Distribution (empty or single value).")
+                        st.write("Not enough data or incorrect data type for Gender Distribution (empty, single value, or not categorical).")
 
-                    if not filtered_df.empty and filtered_df['Youth'].nunique() > 1:
+                    if 'Youth' in filtered_df.columns and filtered_df['Youth'].nunique() > 1 and pd.api.types.is_categorical_dtype(filtered_df['Youth']):
                         try:
                             fig_youth = px.histogram(filtered_df, x="Youth", title="Youth Participation")
                             fig_youth.update_layout(height=400, width=500)
@@ -218,10 +218,10 @@ elif tabs == 'Reports':
                         except Exception as e:
                             st.write(f"Error generating Youth visualization: {e}")
                     else:
-                        st.write("Not enough data to display Youth Distribution (empty or single value).")
+                        st.write("Not enough data or incorrect data type for Youth Distribution (empty, single value, or not categorical).")
 
                 with col2:
-                    if not filtered_df.empty and filtered_df['Intervention_Category'].nunique() > 1:
+                    if 'Intervention_Category' in filtered_df.columns and filtered_df['Intervention_Category'].nunique() > 1 and pd.api.types.is_categorical_dtype(filtered_df['Intervention_Category']):
                         try:
                             fig_category = px.histogram(filtered_df, x="Intervention_Category", title="Intervention Category Distribution")
                             fig_category.update_layout(height=400, width=500)
@@ -229,9 +229,9 @@ elif tabs == 'Reports':
                         except Exception as e:
                             st.write(f"Error generating Category visualization: {e}")
                     else:
-                        st.write("Not enough data to display Category Distribution (empty or single value).")
+                        st.write("Not enough data or incorrect data type for Category Distribution (empty, single value, or not categorical).")
 
-                    if not filtered_df.empty and filtered_df['Intervention'].nunique() > 1:
+                    if 'Intervention' in filtered_df.columns and filtered_df['Intervention'].nunique() > 1 and pd.api.types.is_categorical_dtype(filtered_df['Intervention']):
                         try:
                             fig_intervention = px.histogram(filtered_df, x="Intervention", title="Intervention Type Distribution")
                             fig_intervention.update_layout(height=400, width=500)
@@ -239,18 +239,18 @@ elif tabs == 'Reports':
                         except Exception as e:
                             st.write(f"Error generating Intervention visualization: {e}")
                     else:
-                        st.write("Not enough data to display Intervention Distribution (empty or single value).")
+                        st.write("Not enough data to display Intervention Distribution (empty, single value, or not categorical).")
 
 
                 with st.spinner("Generating Report, Please Wait...."):
                     try:
+                        
                         prompt = f"""
                         You are an expert business analyst. Analyze the following data and generate a comprehensive and insightful business report, 
                         including appropriate key performance indicators and recommendations.
                         Data: dataset:{str(filtered_df.to_json(orient='records'))},
                         kpi:{str(calculate_kpis(filtered_df))}, dataset profile:{str(get_pandas_profile(filtered_df))}
                         """
-                        
                         response = model.generate_content(prompt)
                         report = response.text
                         st.markdown(report)
@@ -268,4 +268,6 @@ elif tabs == 'Reports':
 
     else:
         st.write("Click 'Apply Filters' to see the filtered data.")
-    
+
+
+