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pages/6_🔬_Model_Evaluation.py
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import streamlit as st
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from persist import persist, load_widget_state
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from pathlib import Path
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from middleMan import apply_view,writingPrompt
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global variable_output
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def main():
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cs_body()
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def cs_body():
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#stateVariable = 'Model_Eval'
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#help_text ='Detail the Evaluation Results for this model'
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#col1.header('Model Evaluation')
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st.markdown('# Evaluation')
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st.text_area(" This section describes the evaluation protocols and provides the results. ",help="Detail the Evaluation Results for this model")
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st.markdown('## Testing Data, Factors & Metrics:')
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left, right = st.columns([2,4])
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#st.markdown('### Model Description')
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with left:
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st.write("\n")
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st.write("\n")
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st.markdown('#### Testing Data:')
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.write("\n")
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#st.write("\n")
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st.markdown('#### Factors:')
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.markdown('#### Metrics:')
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.write("\n")
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st.markdown('#### Results:')
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with right:
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#soutput_jinja = parse_into_jinja_markdown()
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st.text_area("", help="Ideally this links to a Dataset Card.",key=persist("Testing_Data"))
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#st.write("\n")
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st.text_area("",help="What are the foreseeable characteristics that will influence how the model behaves? This includes domain and context, as well as population subgroups.",key=persist("Factors"))
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st.text_area("", help="What metrics will be used for evaluation in light of tradeoffs between different errors?", key=persist("Metrics"))
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st.text_area("", key=persist("Model_Results"))
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if __name__ == '__main__':
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load_widget_state()
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main()
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