copied demo app to app

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from transformers import pipeline
+
+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",
+        "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"):
+            result["entity"] = "Skill"
+            del result["entity_group"]
+
+    output_knowledge = token_knowledge_classifier(text)
+    for result in output_knowledge:
+        if result.get("entity_group"):
+            result["entity"] = "Knowledge"
+            del result["entity_group"]
+
+    if len(output_skills) > 0:
+        output_skills = aggregate_span(output_skills)
+    if len(output_knowledge) > 0:
+        output_knowledge = aggregate_span(output_knowledge)
+
+    return {"text": text, "entities": output_skills}, {"text": text, "entities": output_knowledge}
+
+
+demo = gr.Interface(fn=ner,
+                    inputs=gr.Textbox(placeholder="Enter sentence here..."),
+                    outputs=["highlight", "highlight"],
+                    examples=examples)
+
+demo.launch()