Update app.py

app.py

@@ -188,23 +188,23 @@ if st.button("Run MGA Optimization"):
     alternative_solutions = optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wind_cost, river_cost, battery_cost, yearly_demand, solar_range, wind_range, river_range, offshore_wind_range, [t / 100 for t in thresholds], selected_technologies)
 
     if alternative_solutions:
-        # コスト積み上げ用データの収集
-        cost_data = []
-        for sol in alternative_solutions:
-            cost_data.append({
-                'threshold': sol['threshold'] * 100,
-                'type': sol['type'],
-                'technology': sol['technology'],
-                'total_cost': sol['total_cost']
-            })
-
-        # コスト積み上げグラフのプロット
-        cost_df = pd.DataFrame(cost_data)
-        fig_cost = px.bar(cost_df, x='threshold', y='total_cost', color='technology', title="Cost Breakdown by Technology and Threshold")
-        fig_cost.update_layout(xaxis_title='Threshold (%)', yaxis_title='Total Cost (¥)')
-
-        # Streamlitでコスト積み上げグラフを表示
-        st.plotly_chart(fig_cost, use_container_width=True)
+        # # コスト積み上げ用データの収集
+        # cost_data = []
+        # for sol in alternative_solutions:
+        #     cost_data.append({
+        #         'threshold': sol['threshold'] * 100,
+        #         'type': sol['type'],
+        #         'technology': sol['technology'],
+        #         'total_cost': sol['total_cost']
+        #     })
+
+        # # コスト積み上げグラフのプロット
+        # cost_df = pd.DataFrame(cost_data)
+        # fig_cost = px.bar(cost_df, x='threshold', y='total_cost', color='technology', title="Cost Breakdown by Technology and Threshold")
+        # fig_cost.update_layout(xaxis_title='Threshold (%)', yaxis_title='Total Cost (¥)')
+
+        # # Streamlitでコスト積み上げグラフを表示
+        # st.plotly_chart(fig_cost, use_container_width=True)
 
         # 各技術ごとに異なる色を指定
         colors = {