Update app.py

app.py

@@ -79,7 +79,7 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
     alternative_solutions = []
     for threshold in thresholds:
         relaxed_cost = optimal_cost * (1 + threshold)
-        for tech in selected_tech:  # 選択された技術のみ実行
+        for tech in selected_tech:  # Run only for selected technologies
             # Minimize capacity of each technology
             alt_model_min = pulp.LpProblem(f"AlternativeModel_Min_{tech}_{threshold}", pulp.LpMinimize)
             alt_model_min += pulp.lpSum([renewable_capacity[(r, g)] * renewable_capacity_cost[g]
@@ -140,27 +140,27 @@ def plot_capacity_distribution(alternative_solutions, selected_technologies):
     
     capacity_df = pd.DataFrame(capacity_data)
 
-    # Create violin plot with Plotly Express
-    fig_violin = px.violin(capacity_df, x="Technology", y="Capacity (MW)", color="Technology",
-                           box=True, points="all", title="Capacity Distribution by Technology")
-    fig_violin.update_layout(xaxis_title="Technology", yaxis_title="Installed Capacity (MW)")
+    # Create violin plot with Plotly Express (horizontal)
+    fig_violin = px.violin(capacity_df, y="Technology", x="Capacity (MW)", color="Technology",
+                           box=True, points="all", orientation="h", title="Capacity Distribution by Technology")
+    fig_violin.update_layout(yaxis_title="Technology", xaxis_title="Installed Capacity (MW)")
 
     return fig_violin
 
 # Function to create cost breakdown stacked bar plot for each threshold and technology type
 def plot_cost_breakdown(alternative_solutions, selected_technologies, renewable_capacity_cost, battery_cost_per_mwh):
     # Generate a bar plot for each case based on threshold and technology type
-    for sol in alternative_solutions:
+    for idx, sol in enumerate(alternative_solutions):
         cost_data = {
             'Technology': selected_technologies + ['Battery'],
             'Cost': [sol['solution'][tech] * renewable_capacity_cost[tech] for tech in selected_technologies] + [sol['battery_capacity'] * battery_cost_per_mwh]
         }
         cost_df = pd.DataFrame(cost_data)
 
-        # Create stacked bar chart
+        # Create stacked bar chart with unique key to avoid StreamlitDuplicateElementId error
         fig_bar = px.bar(cost_df, x='Technology', y='Cost', title=f"Cost Breakdown (Threshold: {sol['threshold'] * 100}%, Type: {sol['type']})",
                          labels={'Cost': 'Cost (¥)'})
-        st.plotly_chart(fig_bar, use_container_width=True)
+        st.plotly_chart(fig_bar, use_container_width=True, key=f"cost_plot_{idx}")
 
 # Streamlit UI setup
 st.set_page_config(page_title='Renewable Energy System Optimization with MGA', layout='wide')