Py.Cafe
Streamlit on Pycafe: Interactive App Samples
- folderassets/
- folderdata/
- action_areas.geojson
- app.py
- requirements.txt
- utils.py
app.py
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import streamlit as st
import pandas as pd
import numpy as np
import geopandas as gpd
from shapely.geometry import Point
st.title('MEP Reporting Template')
st.image('assets/Zones.png')
st.header('Data files')
buildings = pd.read_csv('data/buildings.csv')
systems = pd.read_csv('data/systems.csv')
with st.expander('Buildings data'):
st.dataframe(buildings)
with st.expander('Systems data'):
st.dataframe(systems)
geodataframe = None
with st.expander('Action areas'):
uploaded_file = st.file_uploader('Upload your action areas:')
if uploaded_file is not None:
# Can be used wherever a "file-like" object is accepted:
geodataframe = gpd.read_file(uploaded_file)
geodataframe.columns = [col.lower() for col in geodataframe.columns]
st.dataframe(geodataframe.drop(columns='geometry'))
st.header('Target scenarios')
st.subheader('Renovation')
st.markdown('Add here the default assumptions used for modelling the renovation of your building stocks 👇')
renovation_df = pd.DataFrame(data = {'criteria':['Start year', 'End year', 'Interval', 'Renovation rate (%)'], 'values':[2025,2045,5,3]})
ratio_df = pd.DataFrame(data = {'Building sector':['Residential','Commercial and traffic', 'Industry and production', 'Public buildings'], 'reduction ratio':[35,37, 29, 33]})
col1, col2 = st.columns(2)
with col1:
st.markdown('**Renovation parameters**')
renovation_edited = st.data_editor(
renovation_df,
column_config={
'values': st.column_config.NumberColumn(
"Values",
)
},
hide_index = True
)
with col2:
st.markdown('**Reduction ratios**')
ratio_edited = st.data_editor(
ratio_df,
column_config={
'reduction ratio': st.column_config.NumberColumn(
"Reduction ratio",
min_value=0,
max_value=100,
format="%.2f%%"
)
},
hide_index = True
)
st.image('assets/Renovation.png')
st.subheader('Heating system substitution')
st.markdown('Define the parameters linked to your action areas 👇')
needed_cols = ['name','system group', 'system type', 'geothermal', 'water body', 'wood', 'waste heat', 'biogas', 'start year', 'end year']
if geodataframe is not None:
for col in needed_cols:
if col.lower() not in geodataframe.columns:
geodataframe[col.lower()] = np.nan
geodataframe_edited = st.data_editor(
geodataframe.drop(columns='geometry')[needed_cols],
column_config={
"system group": st.column_config.SelectboxColumn(
"Group of systems",
help = "The group of systems of the zone",
options = [
"District heating network",
"Decentralized heating"
],
required = True
)
},
hide_index=True,
)
geodataframe_new = gpd.GeoDataFrame(geodataframe_edited, geometry = geodataframe['geometry'])
geojson = geodataframe_new.to_json()
st.download_button(
label = "Dowload the zones",
data = geojson,
file_name = "action_areas.geojson",
mime="application/geo+json")
# buildings_gdf = gpd.GeoDataFrame(buildings, geometry = gpd.points_from_xy(buildings["coordinate x"], buildings["coordinate y"]), crs = "EPSG:4326")
else:
st.error('Action areas not uploaded. Please upload the action areas.', icon = "💡")
st.markdown('**Heating system substitution**')
st.image('assets/Heating_system_substitution.png')
st.markdown('**District heating zones deployment**')
st.image('assets/District_heating_zones_deployment.png')
st.markdown('**Heat supply evolution**')
st.image('assets/Heat_supply_evolution.png')
st.markdown('**Final energy demand evolution**')
st.image('assets/Final_energy_demand_evolution.png')
st.markdown('**Greenhouse gas emissions evolution**')
st.image('assets/GHG_emissions_evolution.png')app.py
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import streamlit as st
import pandas as pd
import numpy as np
import geopandas as gpd
from shapely.geometry import Point
st.title('MEP Reporting Template')
st.image('assets/Zones.png')
st.header('Data files')
buildings = pd.read_csv('data/buildings.csv')
systems = pd.read_csv('data/systems.csv')
with st.expander('Buildings data'):
st.dataframe(buildings)
with st.expander('Systems data'):
st.dataframe(systems)
geodataframe = None
with st.expander('Action areas'):
uploaded_file = st.file_uploader('Upload your action areas:')
if uploaded_file is not None:
# Can be used wherever a "file-like" object is accepted:
geodataframe = gpd.read_file(uploaded_file)
geodataframe.columns = [col.lower() for col in geodataframe.columns]
st.dataframe(geodataframe.drop(columns='geometry'))
st.header('Target scenarios')
st.subheader('Renovation')
st.markdown('Add here the default assumptions used for modelling the renovation of your building stocks 👇')
renovation_df = pd.DataFrame(data = {'criteria':['Start year', 'End year', 'Interval', 'Renovation rate (%)'], 'values':[2025,2045,5,3]})
ratio_df = pd.DataFrame(data = {'Building sector':['Residential','Commercial and traffic', 'Industry and production', 'Public buildings'], 'reduction ratio':[35,37, 29, 33]})
col1, col2 = st.columns(2)
with col1:
st.markdown('**Renovation parameters**')
renovation_edited = st.data_editor(
renovation_df,
column_config={
'values': st.column_config.NumberColumn(
"Values",
)
},
hide_index = True
)
with col2:
st.markdown('**Reduction ratios**')
ratio_edited = st.data_editor(
ratio_df,
column_config={
'reduction ratio': st.column_config.NumberColumn(
"Reduction ratio",
min_value=0,
max_value=100,
format="%.2f%%"
)
},
hide_index = True
)
st.image('assets/Renovation.png')
st.subheader('Heating system substitution')
st.markdown('Define the parameters linked to your action areas 👇')
needed_cols = ['name','system group', 'system type', 'geothermal', 'water body', 'wood', 'waste heat', 'biogas', 'start year', 'end year']
if geodataframe is not None:
for col in needed_cols:
if col.lower() not in geodataframe.columns:
geodataframe[col.lower()] = np.nan
geodataframe_edited = st.data_editor(
geodataframe.drop(columns='geometry')[needed_cols],
column_config={
"system group": st.column_config.SelectboxColumn(
"Group of systems",
help = "The group of systems of the zone",
options = [
"District heating network",
"Decentralized heating"
],
required = True
)
},
hide_index=True,
)
geodataframe_new = gpd.GeoDataFrame(geodataframe_edited, geometry = geodataframe['geometry'])
geojson = geodataframe_new.to_json()
st.download_button(
label = "Dowload the zones",
data = geojson,
file_name = "action_areas.geojson",
mime="application/geo+json")
# buildings_gdf = gpd.GeoDataFrame(buildings, geometry = gpd.points_from_xy(buildings["coordinate x"], buildings["coordinate y"]), crs = "EPSG:4326")
else:
st.error('Action areas not uploaded. Please upload the action areas.', icon = "💡")
st.markdown('**Heating system substitution**')
st.image('assets/Heating_system_substitution.png')
st.markdown('**District heating zones deployment**')
st.image('assets/District_heating_zones_deployment.png')
st.markdown('**Heat supply evolution**')
st.image('assets/Heat_supply_evolution.png')
st.markdown('**Final energy demand evolution**')
st.image('assets/Final_energy_demand_evolution.png')
st.markdown('**Greenhouse gas emissions evolution**')
st.image('assets/GHG_emissions_evolution.png')