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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"],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() |