Delete pages/8_🌏_Environmental_Impact.py

pages/8_🌏_Environmental_Impact.py

@@ -1,56 +0,0 @@
-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()