Update example for Flar 0.12.2

app.py

@@ -11,6 +11,7 @@ st.set_page_config(layout="centered")
 model_map = {
     "find Entities (default)": "ner-large",
     "find Entities (18-class)": "ner-ontonotes-large",
+    "find Frames": "frame-large",
     "find Parts-of-Speech": "pos-multi",
 }
 
@@ -25,7 +26,7 @@ selected_model_id = st.selectbox("This is a check box",
 st.subheader("Input your text here")
 input_text = st.text_area('Write or Paste Text Below',
                           value='May visited the Eiffel Tower in Paris last May.\n\n'
-                                'There she saw a sign in German that read: "Dirk mag den Eiffelturm"',
+                                'There she ran across a sign in German that read: "Dirk liebt den Eiffelturm"',
                           height=128,
                           max_chars=None,
                           label_visibility="collapsed")
@@ -36,6 +37,16 @@ def get_model(model_name):
     return SequenceTagger.load(model_map[model_name])
 
 
+@st.cache(allow_output_mutation=True)
+def get_frame_definitions():
+    frame_definition_map = {}
+    with open('propbank_frames_3.1.txt') as infile:
+        for line in infile:
+            frame_definition_map[line.split('\t')[0]] = line.split('\t')[1]
+
+    return frame_definition_map
+
+
 def get_html(html: str):
     WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
     html = html.replace("\n", " ")
@@ -60,6 +71,9 @@ button_clicked = st.button("**Click here** to tag the input text", key=None)
 
 if button_clicked:
 
+    if 'frame' in selected_model_id.lower():
+        frame_definition_map = get_frame_definitions()
+
     # get a sentence splitter and split text into sentences
     splitter = SegtokSentenceSplitter()
     sentences = splitter.split(input_text)
@@ -73,11 +87,20 @@ if button_clicked:
     predicted_labels = set()
     for sentence in sentences:
         for prediction in sentence.get_labels():
-            spacy_display["ents"].append(
-                {"start": prediction.data_point.start_position + sentence.start_position,
-                 "end": prediction.data_point.end_position + sentence.start_position,
-                 "label": prediction.value})
-            predicted_labels.add(prediction.value)
+            entity_fields = {
+                "start": prediction.data_point.start_position + sentence.start_position,
+                "end": prediction.data_point.end_position + sentence.start_position,
+                "label": prediction.value,
+            }
+
+            if 'frame' in selected_model_id.lower():
+                id = prediction.value.split('.')[-1]
+                verb = ''.join(prediction.value.split('.')[:-1])
+                kb_url = f"https://propbank.github.io/v3.4.0/frames/{verb}.html#{verb}.{id}"
+                entity_fields["label"] = f'<a style="text-decoration: underline; text-decoration-style: dotted; color: inherit; font-weight: bold" href="{kb_url}">{prediction.value}</a>'
+
+            spacy_display["ents"].append(entity_fields)
+            predicted_labels.add(entity_fields["label"])
 
     # create colors for each label
     colors = {}
@@ -94,5 +117,5 @@ if button_clicked:
                                  },
                                  )
     style = "<style>mark.entity { display: inline-block }</style>"
-    st.subheader("Found entities")
+    st.subheader("Tagged text")
     st.write(f"{style}{get_html(html)}", unsafe_allow_html=True)