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()