Works with highlighting using HTML

app.py

@@ -18,13 +18,27 @@ examples = [["The doctor was interested in Luke 's treatment .", True, True, 0.6
 
 def call(sentence: str, return_all_candidates: bool, threshold: float):
     ret = detector([sentence], return_all_candidates, True, threshold)[0]
-    return ret, ret
+    if return_all_candidates:
+        positives = [d["predicate_idx"] for d in ret if d['predicate_detector_prediction']]
+        negatives = [d["predicate_idx"] for d in ret if not d['predicate_detector_prediction']]
+    else:
+        positives = [d["predicate_idx"] for d in ret]
+        negatives = []
+    def color(idx):
+        if idx in positives: return "lightgreen"
+        if idx in negatives: return "pink"
+    highlighted = [(word, color(idx)) for idx, word in enumerate(sentence.split(" "))]
+    def word_span(word, idx):
+        return f'<span style="background-color: {color(idx)}">{word}</span>'
+    html = '<span>' + ' '.join(word_span(word, idx) for idx, word in enumerate(sentence.split(" "))) + '</span>'
+    return html, ret
 
-iface = gr.Interface(fn=call, 
+iface = gr.Interface(call, 
                      inputs=[gr.inputs.Textbox(label="Sentence", lines=3), 
                              gr.inputs.Checkbox(default=True, label="Return all candidates?"),
                              gr.inputs.Slider(minimum=0., maximum=1., step=0.01, default=0.5, label="Threshold")], 
-                     outputs=[gr.outputs.JSON(label="Model Output"), gr.outputs.JSON(label="Model Output - QASRL")],
+                     outputs=[gr.outputs.HTML(label="Detected Nominalizations"), 
+                              gr.outputs.JSON(label="Raw Model Output")],
                      title=title,
                      description=description,
                      article=links,