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import streamlit as st
from persist import persist, load_widget_state
from pathlib import Path
global variable_output
def main():
cs_body()
def cs_body():
stateVariable = 'Model_carbon'
help_text ='Provide an estimate for the carbon emissions: e.g hardware used, horus spent training, cloud provider '
st.markdown('# Environmental Impact')
st.markdown('###### Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).')
st.text_area("", help="Provide an estimate for the carbon emissions: e.g hardware used, horus spent training, cloud provider")
left, right = st.columns([2,4])
with left:
st.write("\n")
st.write("\n")
st.markdown('### Hardware Type:')
st.write("\n")
st.write("\n")
#st.write("\n")
st.markdown('### Hours used:')
st.write("\n")
st.write("\n")
st.markdown('### Cloud Provider:')
st.write("\n")
st.write("\n")
st.markdown('### Compute Region:')
st.write("\n")
st.write("\n")
st.markdown('### Carbon Emitted:')
with right:
#soutput_jinja = parse_into_jinja_markdown()
st.text_input("",key=persist("Model_hardware"))
#st.write("\n")
st.text_input("",help="sw",key=persist("hours_used"))
st.text_input("",key=persist("Model_cloud_provider"))
st.text_input("",key=persist("Model_cloud_region"))
st.text_input("",help= 'in grams of CO2eq', key=persist("Model_c02_emitted")) ##to-do: auto calculate
if __name__ == '__main__':
load_widget_state()
main() |