create two separate highlights

app.py

@@ -1,37 +1,55 @@
 import gradio as gr
 from transformers import pipeline
 
-token_skill_classifier = pipeline(model="jjzha/jobbert_skill_extraction", aggregation_strategy="simple")
-token_knowledge_classifier = pipeline(model="jjzha/jobbert_knowledge_extraction", aggregation_strategy="simple")
+token_skill_classifier = pipeline(model="jjzha/jobbert_skill_extraction", aggregation_strategy="first")
+token_knowledge_classifier = pipeline(model="jjzha/jobbert_knowledge_extraction", aggregation_strategy="first")
 
 
 examples = [
-        "Knowing Python is a plus.",
+        "Knowing Python is a plus",
+        "Recommend changes, develop and implement processes to ensure compliance with IFRS standards"
         ]
 
 
+def aggregate_span(results):
+    new_results = []
+    current_result = results[0]
+
+    for result in results[1:]:
+        if result["start"] == current_result["end"] + 1:
+            current_result["word"] += " " + result["word"]
+            current_result["end"] = result["end"]
+        else:
+            new_results.append(current_result)
+            current_result = result
+
+    new_results.append(current_result)
+
+    return new_results
+
+
 def ner(text):
     output_skills = token_skill_classifier(text)
     for result in output_skills:
         if result.get("entity_group"):
-            tag = result["entity_group"]
-            result["entity"] = tag + "-Skill"
+            result["entity"] = "Skill"
             del result["entity_group"]
 
     output_knowledge = token_knowledge_classifier(text)
     for result in output_knowledge:
         if result.get("entity_group"):
-            tag = result["entity_group"]
-            result["entity"] = tag + "-Knowledge"
+            result["entity"] = "Knowledge"
             del result["entity_group"]
+    
+    output_skills = aggregate_span(output_skills)
+    output_knowledge = aggregate_span(output_knowledge)
 
-    output = output_skills + output_knowledge
-    return {"text": text, "entities": output}
+    return {"text": text, "entities": output_skills}, {"text": text, "entities": output_knowledge}
 
 
 demo = gr.Interface(fn=ner,
                     inputs=gr.Textbox(placeholder="Enter sentence here..."),
-                    outputs=gr.HighlightedText(),
+                    outputs=["highlight", "highlight"],
                     examples=examples)
 
 demo.launch()