import pandas as pd import streamlit as st # from annotated_text import annotated_text from annotated_text.util import get_annotated_html from streamlit_annotation_tools import text_labeler from constants import ( APP_INTRO, APP_TITLE, EVAL_FUNCTION_INTRO, EVAL_FUNCTION_PROPERTIES, NER_TASK_EXPLAINER, PREDICTION_ADDITION_INSTRUCTION, ) from evaluation_metrics import EVALUATION_METRICS from predefined_example import EXAMPLES from span_dataclass_converters import ( get_highlight_spans_from_ner_spans, get_ner_spans_from_annotations, ) @st.cache_resource def get_examples_attributes(selected_example): "Return example attributes so that they are not refreshed on every interaction" return ( selected_example.text, selected_example.gt_labels, selected_example.gt_spans, selected_example.predictions, selected_example.tags, ) if __name__ == "__main__": st.set_page_config(layout="wide") st.title(APP_TITLE) st.write(APP_INTRO) explanation_tab, comparision_tab = st.tabs(["📙 Explanation", "⚖️ Comparision"]) with explanation_tab: st.write(EVAL_FUNCTION_INTRO) st.image("assets/eval_fnc_viz.png", caption="Evaluation Function Flow") st.markdown(EVAL_FUNCTION_PROPERTIES) st.markdown(NER_TASK_EXPLAINER) st.subheader("Evaluation Metrics") metric_names = "\n".join( [ f"{index+1}. " + evaluation_metric.name for index, evaluation_metric in enumerate(EVALUATION_METRICS) ] ) st.markdown( "The different evaluation metrics we have for the NER task are\n" "\n" f"{metric_names}" ) with comparision_tab: # with st.container(): st.subheader("Ground Truth & Predictions") # , divider='rainbow') selected_example = st.selectbox( "Select an example text from the drop down below", [example for example in EXAMPLES], format_func=lambda ex: ex.text, ) text, gt_labels, gt_spans, predictions, tags = get_examples_attributes( selected_example ) # annotated_text( # get_highlight_spans_from_ner_spans( # get_ner_spans_from_annotations(gt_labels), text # ) # ) annotated_predictions = [ get_annotated_html(get_highlight_spans_from_ner_spans(ner_span, text)) for ner_span in predictions ] predictions_df = pd.DataFrame( { # "ID": [f"Prediction_{index}" for index in range(len(predictions))], "Prediction": annotated_predictions, "ner_spans": predictions, }, index=["Ground Truth"] + [f"Prediction_{index}" for index in range(len(predictions) - 1)], ) # st.subheader("Predictions") # , divider='rainbow') with st.expander("Click to Add Predictions"): st.subheader("Adding predictions") st.markdown(PREDICTION_ADDITION_INSTRUCTION) st.write( "Note: Only the spans of the selected label name are shown at a given instance. Click on the label to see the corresponding spans. (or view the json below)", ) labels = text_labeler(text, gt_labels) st.json(labels, expanded=False) # if st.button("Add Prediction"): # labels = text_labeler(text) if st.button("Add!"): spans = get_ner_spans_from_annotations(labels) spans = sorted(spans, key=lambda span: span["start"]) predictions.append(spans) annotated_predictions.append( get_annotated_html(get_highlight_spans_from_ner_spans(spans, text)) ) predictions_df = pd.DataFrame( { # "ID": [f"Prediction_{index}" for index in range(len(predictions))], "Prediction": annotated_predictions, "ner_spans": predictions, }, index=["Ground Truth"] + [f"Prediction_{index}" for index in range(len(predictions) - 1)], ) print("added") highlighted_predictions_df = predictions_df[["Prediction"]] st.write( highlighted_predictions_df.to_html(escape=False), unsafe_allow_html=True ) st.divider() ### EVALUATION METRICS COMPARISION ### st.subheader("Evaluation Metrics Comparision") # , divider='rainbow') metric_names = "\n".join( ["- " + evaluation_metric.name for evaluation_metric in EVALUATION_METRICS] ) st.markdown( "The different evaluation metrics we have for the NER task are\n" f"{metric_names}" ) with st.expander("View Predictions Details"): st.write(predictions_df.to_html(escape=False), unsafe_allow_html=True) if st.button("Get Metrics!"): for evaluation_metric in EVALUATION_METRICS: predictions_df[evaluation_metric.name] = predictions_df.ner_spans.apply( lambda ner_spans: evaluation_metric.get_evaluation_metric( # metric_type=evaluation_metric_type, gt_ner_span=gt_spans, pred_ner_span=ner_spans, text=text, tags=tags, ) ) metrics_df = predictions_df.drop(["ner_spans"], axis=1) st.write(metrics_df.to_html(escape=False), unsafe_allow_html=True)