added tabs

app.py

@@ -1,9 +1,11 @@
 import pandas as pd
 import streamlit as st
-from annotated_text import annotated_text
+
+# from annotated_text import annotated_text
 from annotated_text.util import get_annotated_html
 from streamlit_annotation_tools import text_labeler
 
+from constants import PREDICTION_ADDITION_INSTRUCTION
 from evaluation_metrics import EVALUATION_METRICS
 from predefined_example import EXAMPLES
 from span_dataclass_converters import (
@@ -26,110 +28,110 @@ def get_examples_attributes(selected_example):
 
 if __name__ == "__main__":
     st.set_page_config(layout="wide")
-    st.title("NER Metrics Comparison")
+    st.title("📈 NER Metrics Comparison ⚖️")
 
     st.write(
         "Evaluation for the NER task requires a ground truth and a prediction that will be evaluated. The ground truth is shown below, add predictions in the next section to compare the evaluation metrics."
     )
+    explanation_tab, comparision_tab = st.tabs(["📙 Explanation", "⚖️ Comparision"])
 
-    # with st.container():
-    st.subheader("Ground Truth")  # , divider='rainbow')
+    with explanation_tab:
+        st.write("This is the place holder for explanation of all the metrics")
 
-    selected_example = st.selectbox(
-        "Select an example text from the drop down below",
-        [example for example in EXAMPLES],
-        format_func=lambda ex: ex.text,
-    )
+    with comparision_tab:
+        # with st.container():
+        st.subheader("Ground Truth & Predictions")  # , divider='rainbow')
 
-    text, gt_labels, gt_spans, predictions, tags = get_examples_attributes(
-        selected_example
-    )
+        selected_example = st.selectbox(
+            "Select an example text from the drop down below",
+            [example for example in EXAMPLES],
+            format_func=lambda ex: ex.text,
+        )
 
-    annotated_text(
-        get_highlight_spans_from_ner_spans(
-            get_ner_spans_from_annotations(gt_labels), text
+        text, gt_labels, gt_spans, predictions, tags = get_examples_attributes(
+            selected_example
         )
-    )
 
-    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=[f"Prediction_{index}" for index in range(len(predictions))],
-    )
+        # 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')
+        # st.subheader("Predictions")  # , divider='rainbow')
 
-    with st.expander("Click to Add Predictions"):
-        st.subheader("Adding predictions")
-        st.markdown(
-            """
-Add predictions to the list of predictions on which the evaluation metric will be caculated.
-- Select the entity type/label name and then highlight the span in the text below.
-- To remove a span, double click on the higlighted text.
-- Once you have your desired prediction, click on the 'Add' button.(The prediction created is shown in a json below)
-"""
-        )
+        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 is shown at a given instance.",
+            )
+            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(
-            "Note: Only the spans of the selected label name is shown at a given instance.",
+            highlighted_predictions_df.to_html(escape=False), unsafe_allow_html=True
         )
-        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=[f"Prediction_{index}" for index in range(len(predictions))],
-            )
-            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')
-    st.markdown("""
-    The different evaluation metrics we have for the NER task are
-    - Span Based Evaluation with Partial Overlap
-    - Token Based Evaluation with Micro Avg
-    - Token Based Evaluation with Macro Avg
-    """)
-
-    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,
+        st.divider()
+
+        ### EVALUATION METRICS COMPARISION ###
+
+        st.subheader("Evaluation Metrics Comparision")  # , divider='rainbow')
+        st.markdown(
+            "The different evaluation metrics we have for the NER task are\n"
+            f"{''.join(['- '+evaluation_metric.name+'\n' for evaluation_metric in EVALUATION_METRICS])}"
+        )
+
+        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)
+            metrics_df = predictions_df.drop(["ner_spans"], axis=1)
 
-        st.write(metrics_df.to_html(escape=False), unsafe_allow_html=True)
+            st.write(metrics_df.to_html(escape=False), unsafe_allow_html=True)

constants.py

@@ -0,0 +1,6 @@
+PREDICTION_ADDITION_INSTRUCTION = """
+Add predictions to the list of predictions on which the evaluation metric will be caculated.
+- Select the entity type/label name and then highlight the span in the text below.
+- To remove a span, double click on the higlighted text.
+- Once you have your desired prediction, click on the 'Add' button.(The prediction created is shown in a json below)
+"""

evaluation_metrics.py

@@ -47,7 +47,7 @@ class TokenMicroMetric(EvaluationMetric):
     def __init__(self) -> None:
         super().__init__()
 
-        self.name = "Span Based Evaluation with Micro Average"
+        self.name = "Token Based Evaluation with Micro Average"
         self.description = ""
 
     @staticmethod