Update app.py

app.py

@@ -141,28 +141,26 @@ if tabs =='Chat':
         answer = generateResponse(dataFrame=df,prompt=user_input)
         st.write(answer)
 
-elif tabs == 'Reports':
+if tabs == 'Reports':
     df = pd.read_csv(uploaded_file)
 
     st.subheader("Reports")
-    st.write("Filter by Company Name, Product, Gender, Youth, Intervention Category or Intervention to generate report")
+    st.write("Filter by Company Name, Gender, Youth, Intervention Category or Intervention to generate report")
 
     # Filtering Interface
     st.write("Filtering Options")
-    branch_names = df['Company Name'].unique().tolist() # Company Name instead of Branch Name
+    company_names = df['Company Name'].unique().tolist()
     gender_options = df['Gender'].unique().tolist()
     youth_options = df['Youth'].unique().tolist()
     intervention_categories = df['Intervention_Category'].unique().tolist()
     intervention_types = df['Intervention'].unique().tolist()
 
-
-    selected_companies = st.multiselect('Select Company(ies)', branch_names, default=branch_names)
+    selected_companies = st.multiselect('Select Company(ies)', company_names, default=company_names)
     selected_genders = st.multiselect('Select Gender(s)', gender_options, default=gender_options)
     selected_youth = st.multiselect('Select Youth Status(es)', youth_options, default=youth_options)
     selected_categories = st.multiselect('Select Intervention Category(ies)', intervention_categories, default=intervention_categories)
     selected_interventions = st.multiselect('Select Intervention(s)', intervention_types, default=intervention_types)
 
-
     if st.button('Apply Filters and Generate report'):
         filtered_df = df.copy()
 
@@ -177,46 +175,59 @@ elif tabs == 'Reports':
         if selected_interventions:
             filtered_df = filtered_df[filtered_df['Intervention'].isin(selected_interventions)]
 
-
-        st.write("Filtered DataFrame")
-        with st.expander("Preview"):
-            st.write(filtered_df.head())
-
-        # Dashboard Visualizations (Plotly)
-        st.subheader("Dashboard")
-
-        col1, col2 = st.columns(2)  # Create two columns for better layout
-
-        with col1:
-            fig_gender = px.histogram(filtered_df, x="Gender", title="Gender Distribution")
-            st.plotly_chart(fig_gender)
-
-            fig_youth = px.histogram(filtered_df, x="Youth", title="Youth Participation")
-            st.plotly_chart(fig_youth)
-
-        with col2:
-            fig_category = px.histogram(filtered_df, x="Intervention_Category", title="Intervention Category Distribution")
-            st.plotly_chart(fig_category)
-
-            fig_intervention = px.histogram(filtered_df, x="Intervention", title="Intervention Type Distribution")
-            st.plotly_chart(fig_intervention)
-
-        with st.spinner("Generating Report, Please Wait...."):
-            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:
-{str(calculate_kpis(filtered_df))}
-{str(get_pandas_profile(filtered_df))}
-"""
-
-            response = model.generate_content(prompt)
-            report = response.text
-            st.markdown(report)
-            st.success("Report Generated!")
+        if not filtered_df.empty:
+            if len(filtered_df) > 1:
+                st.write("Filtered DataFrame")
+                with st.expander("Preview"):
+                    st.write(filtered_df.head())
+
+                # Dashboard Visualizations (Plotly)
+                st.subheader("Dashboard")
+                col1, col2 = st.columns([1, 1])  # Adjust column ratios if needed
+
+                with col1:
+                    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)
+
+                        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 visualizations: {e}")
+
+                with col2:
+                    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)
+
+                        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 visualizations: {e}")
+
+                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:{str(calculate_kpis(filtered_df))}{str(get_pandas_profile(filtered_df))}"""
+                        response = model.generate_content(prompt)
+                        report = response.text
+                        st.markdown(report)
+                        st.success("Report Generated!")
+                    except Exception as e:
+                        st.write(f"Error generating report: {e}")
+
+            else:  # Single row or empty
+                st.write("Not enough data after filtering for full visualizations and report generation.")
+                if not filtered_df.empty:
+                    st.write("Filtered DataFrame:")
+                    st.write(filtered_df)  # Display the single row
+        else:
+            st.write("No data after filtering.")
 
     else:
-        st.write("Filtered DataFrame")
         st.write("Click 'Apply Filters' to see the filtered data.")