Update app.py

app.py

@@ -196,51 +196,6 @@ elif tabs == 'Reports':
                 with st.expander("Preview"):
                     st.write(filtered_df.head())
 
-                st.subheader("Dashboard")
-                col1, col2 = st.columns([1, 1])
-
-                with col1:
-                    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)
-                            st.plotly_chart(fig_gender)
-                        except Exception as e:
-                            st.write(f"Error generating Gender visualization: {e}")
-                    else:
-                        st.write("Not enough data or incorrect data type for Gender Distribution (empty, single value, or not categorical).")
-
-                    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)
-                            st.plotly_chart(fig_youth)
-                        except Exception as e:
-                            st.write(f"Error generating Youth visualization: {e}")
-                    else:
-                        st.write("Not enough data or incorrect data type for Youth Distribution (empty, single value, or not categorical).")
-
-                with col2:
-                    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)
-                            st.plotly_chart(fig_category)
-                        except Exception as e:
-                            st.write(f"Error generating Category visualization: {e}")
-                    else:
-                        st.write("Not enough data or incorrect data type for Category Distribution (empty, single value, or not categorical).")
-
-                    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)
-                            st.plotly_chart(fig_intervention)
-                        except Exception as e:
-                            st.write(f"Error generating Intervention visualization: {e}")
-                    else:
-                        st.write("Not enough data to display Intervention Distribution (empty, single value, or not categorical).")
-
 
                 with st.spinner("Generating Report, Please Wait...."):
                     try:
@@ -248,7 +203,7 @@ elif tabs == 'Reports':
                         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'))}
+                        Data: dataset:{str(filtered_df.to_json(orient='records'))}, kpis: {str(calculate_kpis(filtered_df))}
                         """
                         response = model.generate_content(prompt)
                         report = response.text