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import streamlit as st
from persist import persist, load_widget_state
from jinja2 import Environment, FileSystemLoader
def parse_into_jinja_markdown():
env = Environment(loader=FileSystemLoader('.'), autoescape=True)
temp = env.get_template(st.session_state.markdown_upload)
return (temp.render(model_id = st.session_state["model_name"],
the_model_description = st.session_state["model_description"],developers=st.session_state["Model_developers"],shared_by = st.session_state["shared_by"],model_license = st.session_state['license'],
direct_use = st.session_state["Direct_Use"], downstream_use = st.session_state["Downstream_Use"],out_of_scope_use = st.session_state["Out-of-Scope_Use"],
bias_risks_limitations = st.session_state["Model_Limits_n_Risks"], bias_recommendations = st.session_state['Recommendations'],
model_examination = st.session_state['Model_examin'],
hardware= st.session_state['Model_hardware'], hours_used = st.session_state['hours_used'], cloud_provider = st.session_state['Model_cloud_provider'], cloud_region = st.session_state['Model_cloud_region'], co2_emitted = st.session_state['Model_c02_emitted'],
citation_bibtex= st.session_state["APA_citation"], citation_apa = st.session_state['bibtex_citation'],
training_data = st.session_state['training_data'], preprocessing =st.session_state['preprocessing'], speeds_sizes_times = st.session_state['Speeds_Sizes_Times'],
model_specs = st.session_state['Model_specs'], compute_infrastructure = st.session_state['compute_infrastructure'],software = st.session_state['technical_specs_software'],
glossary = st.session_state['Glossary'],
more_information = st.session_state['More_info'],
model_card_authors = st.session_state['the_authors'],
model_card_contact = st.session_state['Model_card_contact'],
get_started_code =st.session_state["Model_how_to"]
))
def main():
st.write( parse_into_jinja_markdown())
if __name__ == '__main__':
load_widget_state()
main() |