Update app.py

app.py

@@ -61,7 +61,6 @@ def load_csv_data(folder_path):
                 pass
     return pd.concat(all_data, ignore_index=True) if all_data else None
 
-
 def dashboard_overview(): 
     st.title("📊 Vehicle Registration Summary")
 
@@ -99,6 +98,14 @@ def dashboard_overview():
         df_total_filtered = df_total[(df_total["year"] >= selected_years_total[0]) & (df_total["year"] <= selected_years_total[1])]
         fig1 = px.line(df_total_filtered, x="year", y="count", color="State", title=f"Year-wise Registration by State ({selected_years_total[0]} - {selected_years_total[1]})")
         st.plotly_chart(fig1, use_container_width=True)
+
+        # Download buttons
+        csv = df_total_filtered.to_csv(index=False).encode('utf-8')
+        st.download_button("📥 Download Registration Data (CSV)", csv, "registration_data.csv", "text/csv")
+        excel_buffer = io.BytesIO()
+        with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
+            df_total_filtered.to_excel(writer, index=False)
+        st.download_button("📥 Download Registration Data (Excel)", excel_buffer.getvalue(), "registration_data.xlsx", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
     else:
         st.warning("No data available for year-wise registration.")
 
@@ -114,6 +121,14 @@ def dashboard_overview():
         fig2 = px.area(df_fuel_grouped, x="year", y="count", color="row_value", title="National Fuel Type Trend Over Years")
         st.plotly_chart(fig2, use_container_width=True)
 
+        # Download buttons
+        csv = df_fuel_grouped.to_csv(index=False).encode('utf-8')
+        st.download_button("📥 Download Fuel Trend (CSV)", csv, "fuel_trend.csv", "text/csv")
+        excel_buffer = io.BytesIO()
+        with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
+            df_fuel_grouped.to_excel(writer, index=False)
+        st.download_button("📥 Download Fuel Trend (Excel)", excel_buffer.getvalue(), "fuel_trend.xlsx", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
+
     # 3. Vehicle class trend
     vehicle_classes = sorted(df_class['row_value'].dropna().unique())
     class_options = ["Select All"] + vehicle_classes
@@ -126,6 +141,14 @@ def dashboard_overview():
         fig3 = px.area(df_class_grouped, x="year", y="count", color="row_value", title="Vehicle Class Trend Across India")
         st.plotly_chart(fig3, use_container_width=True)
 
+        # Download buttons
+        csv = df_class_grouped.to_csv(index=False).encode('utf-8')
+        st.download_button("📥 Download Class Trend (CSV)", csv, "vehicle_class_trend.csv", "text/csv")
+        excel_buffer = io.BytesIO()
+        with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
+            df_class_grouped.to_excel(writer, index=False)
+        st.download_button("📥 Download Class Trend (Excel)", excel_buffer.getvalue(), "vehicle_class_trend.xlsx", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
+
     # 4. EV adoption graph
     ev_states = sorted(df_ev['State'].dropna().unique())
     ev_options = ["Select All"] + ev_states
@@ -138,12 +161,28 @@ def dashboard_overview():
         fig5 = px.bar(df_ev_statewise, x="State", y="count", title="Top 10 States by EV Registration")
         st.plotly_chart(fig5, use_container_width=True)
 
+        # Download buttons
+        csv = df_ev_statewise.to_csv(index=False).encode('utf-8')
+        st.download_button("📥 Download Top 10 EV States (CSV)", csv, "top_ev_states.csv", "text/csv")
+        excel_buffer = io.BytesIO()
+        with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
+            df_ev_statewise.to_excel(writer, index=False)
+        st.download_button("📥 Download Top 10 EV States (Excel)", excel_buffer.getvalue(), "top_ev_states.xlsx", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
+
         # 5. EV vs. Total Vehicle Registrations Over Time
         df_ev_vs_total = df_ev_filtered.groupby("year")["count"].sum().reset_index()
         df_total_vs_ev = pd.merge(df_total.groupby("year")['count'].sum().reset_index(), df_ev_vs_total, on="year", suffixes=('_total', '_ev'))
         fig6 = px.line(df_total_vs_ev, x="year", y=["count_total", "count_ev"], title="EV vs Total Vehicle Registrations Over Time")
         st.plotly_chart(fig6, use_container_width=True)
 
+        # Download buttons
+        csv = df_total_vs_ev.to_csv(index=False).encode('utf-8')
+        st.download_button("📥 Download EV vs Total Over Time (CSV)", csv, "ev_vs_total.csv", "text/csv")
+        excel_buffer = io.BytesIO()
+        with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
+            df_total_vs_ev.to_excel(writer, index=False)
+        st.download_button("📥 Download EV vs Total Over Time (Excel)", excel_buffer.getvalue(), "ev_vs_total.xlsx", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
+
 
 def ev_insights():
     st.title("⚡ Electric Vehicle (EV) Analysis")
@@ -176,6 +215,15 @@ def ev_insights():
     )
     st.plotly_chart(fig1, use_container_width=True)
 
+    # 📥 Download button for state-wise EV fuel type data
+    csv1 = filtered.to_csv(index=False).encode("utf-8")
+    st.download_button(
+        label=f"Download State-wise EV Data for {state_selection}",
+        data=csv1,
+        file_name=f"{state_selection.replace(' ', '_').lower()}_ev_fuel_data.csv",
+        mime="text/csv"
+    )
+
     # 2️⃣ EV Growth Over Years (National) with Year Range Filter
     st.subheader("📈 National EV Growth Over Years")
     ev_growth = df_ev_state.groupby("year")["count"].sum().reset_index()
@@ -190,6 +238,14 @@ def ev_insights():
     fig2 = px.line(ev_growth, x="year", y="count", title="Total EVs Registered per Year in India")
     st.plotly_chart(fig2, use_container_width=True)
 
+    # 📥 Download button for national EV growth data
+    csv2 = ev_growth.to_csv(index=False).encode("utf-8")
+    st.download_button(
+        label="Download National EV Growth Data",
+        data=csv2,
+        file_name="national_ev_growth.csv",
+        mime="text/csv"
+    )
 
 def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=2009, end_year=2025):
     
@@ -246,38 +302,63 @@ def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=20
         if not statewise_df.empty:
             melted_df = pd.melt(statewise_df, id_vars=["Fuel", "Year"], var_name="State", value_name="Fuel_Amount")
             melted_df["State"] = melted_df["State"].apply(lambda x: state_dict.get(x.upper(), x))
-
+    
             selected_states = st.multiselect("Select States", ["Select All"] + sorted(melted_df["State"].unique()), default=[], key="state_selection")
             if "Select All" in selected_states:
                 selected_states = list(melted_df["State"].unique())
-
+    
             year_range = st.slider("Select Year Range", min_value=start_year, max_value=end_year, value=(start_year, end_year), key="fuel_state_year")
             filtered_df = melted_df[(melted_df["State"].isin(selected_states)) &
                                     (melted_df["Year"].between(year_range[0], year_range[1]))]
-
+    
             fig1 = px.bar(filtered_df, x="State", y="Fuel_Amount", color="Fuel",
                           title=f"Fuel Distribution from {year_range[0]} to {year_range[1]}",
                           labels={"Fuel_Amount": "Amount (in units)"}, barmode="group")
             st.plotly_chart(fig1, use_container_width=True)
+    
+            # Download button
+            st.markdown("### 📥 Download Filtered Data")
+            download_format = st.radio("Select format", ["CSV", "Excel"], horizontal=True)
+            if download_format == "CSV":
+                csv_data = filtered_df.to_csv(index=False).encode("utf-8")
+                st.download_button("Download CSV", csv_data, file_name="fuel_distribution_filtered.csv", mime="text/csv")
+            else:
+                output = BytesIO()
+                with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
+                    filtered_df.to_excel(writer, index=False, sheet_name="Filtered Data")
+                st.download_button("Download Excel", output.getvalue(), file_name="fuel_distribution_filtered.xlsx", mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
+    
 
     with tab2:
         st.subheader("Emission Norm Distribution by State")
         if not norms_df.empty:
             melted_norms = pd.melt(norms_df, id_vars=["Norms", "Year"], var_name="State", value_name="Count")
             melted_norms["State"] = melted_norms["State"].apply(lambda x: state_dict.get(x.upper(), x))
-
+    
             selected_states = st.multiselect("Select States", ["Select All"] + sorted(melted_norms["State"].unique()), default=[], key="norm_state_selection")
             if "Select All" in selected_states:
                 selected_states = list(melted_norms["State"].unique())
-
+    
             year_range = st.slider("Select Year Range", min_value=start_year, max_value=end_year, value=(start_year, end_year), key="norm_state_year")
             filtered_norms = melted_norms[(melted_norms["State"].isin(selected_states)) &
                                           (melted_norms["Year"].between(year_range[0], year_range[1]))]
-
+    
             fig2 = px.bar(filtered_norms, x="State", y="Count", color="Norms",
                           title=f"Emission Norms Distribution from {year_range[0]} to {year_range[1]}",
                           labels={"Count": "Count of Norms"}, barmode="group")
             st.plotly_chart(fig2, use_container_width=True)
+    
+            # Download button
+            st.markdown("### 📥 Download Filtered Norms Data")
+            download_format_norms = st.radio("Select format", ["CSV", "Excel"], horizontal=True, key="norm_download_format")
+            if download_format_norms == "CSV":
+                csv_norm_data = filtered_norms.to_csv(index=False).encode("utf-8")
+                st.download_button("Download CSV", csv_norm_data, file_name="norm_distribution_filtered.csv", mime="text/csv")
+            else:
+                norm_output = BytesIO()
+                with pd.ExcelWriter(norm_output, engine='xlsxwriter') as writer:
+                    filtered_norms.to_excel(writer, index=False, sheet_name="Filtered Norms")
+                st.download_button("Download Excel", norm_output.getvalue(), file_name="norm_distribution_filtered.xlsx", mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
 
     with tab3:
         st.subheader("Norm Emissions by Fuel Type and Year")