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import streamlit as st |
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from persist import persist, load_widget_state |
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from jinja2 import Environment, FileSystemLoader |
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def parse_into_jinja_markdown(): |
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env = Environment(loader=FileSystemLoader('.'), autoescape=True) |
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temp = env.get_template(st.session_state.markdown_upload) |
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return (temp.render(model_id = st.session_state["model_name"], |
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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'], |
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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"], |
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bias_risks_limitations = st.session_state["Model_Limits_n_Risks"], bias_recommendations = st.session_state['Recommendations'], |
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model_examination = st.session_state['Model_examin'], |
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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'], |
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citation_bibtex= st.session_state["APA_citation"], citation_apa = st.session_state['bibtex_citation'], |
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training_data = st.session_state['training_data'], preprocessing =st.session_state['preprocessing'], speeds_sizes_times = st.session_state['Speeds_Sizes_Times'], |
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model_specs = st.session_state['Model_specs'], compute_infrastructure = st.session_state['compute_infrastructure'],software = st.session_state['technical_specs_software'], |
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glossary = st.session_state['Glossary'], |
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more_information = st.session_state['More_info'], |
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model_card_authors = st.session_state['the_authors'], |
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model_card_contact = st.session_state['Model_card_contact'], |
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get_started_code =st.session_state["Model_how_to"] |
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)) |
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def main(): |
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st.write( parse_into_jinja_markdown()) |
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if __name__ == '__main__': |
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load_widget_state() |
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main() |