Upload app.py

app.py

@@ -19,6 +19,7 @@ import pandas as pd
 import plotly.express as px
 ##import gurobipy
 
+
 import streamlit as st
 
 
@@ -57,7 +58,7 @@ t = pd.Index(df_excel.iloc[:,0], name = 't')[1:168]
 # Technologies
 df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
 i = pd.Index(df_excel.iloc[:,0], name = 'i')
-i
+
 
 df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
 iConv = pd.Index(df_excel.iloc[0:7,2], name = 'iConv')
@@ -236,29 +237,9 @@ CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2,
 m.solve(solver_name = 'highs')
 
 
-
 # %%
-# Read Objective from solution
-m.objective_value
+# Installed Cap
+# Assuming df_excel has columns 'All' and 'Capacities'
+fig = px.bar(m.solution['K'].to_dataframe().reset_index(), y='i', x='K', orientation='h', title='Installed Capacities', color='i')
 
-pd.options.plotting.backend = "plotly"
-# Read dual values and plot 
-df = loadserve_t.dual.to_dataframe().reset_index()
-#df['t'] = pd.to_datetime(df['t'])
-df
-
-
-# %%
-# Read values
-Productionlevels = m.solution['y'].to_dataframe().reset_index()
-
-df = Productionlevels
-df
-
-# %%
-#pandas gui
-# Create Line plot
-fig = px.line(df, x=df['t'], y=df['y'], color = df['i'])
 fig
-
-# %%