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import streamlit as st
from persist import persist, load_widget_state
global variable_output
def main():
cs_body()
def cs_body():
st.markdown('# Training Details')
st.write("Provide an overview of the Training Data and Training Procedure for this model")
left, middle, right = st.columns([2,1,7])
with left:
st.write("\n")
st.write("\n")
st.markdown('## Training Data:')
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
with middle:
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.markdown(' \n ## Training Procedure')
with left:
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.markdown('#### Preprocessing:')
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.markdown('#### Speeds, Sizes, Time:')
with right:
#soutput_jinja = parse_into_jinja_markdown()
st.text_area("", help ="Ideally this links to a Dataset Card.", key=persist("training_Data"))
#st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.write("\n")
st.text_area("",key=persist("model_preprocessing"))
st.text_area("", help = "This section provides information about throughput, start/end time, checkpoint size if relevant, etc.", key=persist("Speeds_Sizes_Times"))
if __name__ == '__main__':
load_widget_state()
main() |