import streamlit as st import os ROOT_FIG_DIR = f'{os.getcwd()}/figures/' def get_product_dev_page_layout(): # row6_1, row6_2, = st.columns((1,1)) row6_1, row6_2,row6_3 = st.tabs(["Evaluation Metrics", "Performance Evaluation", "Issues and Limitations"]) with row6_1: # st.write("**Performance Metrics**") st.subheader('Performance Metrics') st.write('Following metrics are used for evaluation:') st.image(f'{ROOT_FIG_DIR}/evaluation_template.png') list_test = """""" st.markdown(list_test, unsafe_allow_html=True) # st.latex(r''' Accuracy=\frac{TP + TN}{TP+TN+FP+FN}''') list_test = """""" st.markdown(list_test, unsafe_allow_html=True) # st.latex(r''' Precision=\frac{TP}{TP+FP}''') list_test = """""" st.markdown(list_test, unsafe_allow_html=True) # st.latex(r''' Recall=\frac{TP}{TP+FN}''') # with st.expander('Test Set Confusion Matrix'): # # st.caption('Test Set Results:') # st.image('./figures/test_confmat_20210404.png') with row6_2: # st.write("**Prediction Samples**") # with st.expander('Test Set Confusion Matrix'): # # st.caption('Test Set Results:') st.subheader('Test Set Confusion Matrix') st.image(f'{ROOT_FIG_DIR}/test_confmat_20210404.png') # st.subheader('Prediction Samples') # st.caption('Correctly Classified sample predictions:') # st.image(f'{ROOT_FIG_DIR}/pred_stats.png') # st.caption('Miss Classified sample predictions:') # st.image(f'{ROOT_FIG_DIR}/pred_stats.png') # st.subheader("Class-wise Prediction Distributions") # st.image(f'{ROOT_FIG_DIR}/training_prob_stats.png') with row6_3: st.write("Weencountered classimbalance issue and here is the miclassified samples...") st.caption('Miss Classified CNV Samples:') st.image(f'{ROOT_FIG_DIR}/cnv_missclass.png') st.caption('Miss Classified NORMAL Samples:') st.image(f'{ROOT_FIG_DIR}/normal_missclass.png')