Update app.py

app.py

@@ -28,18 +28,25 @@ ner_pipe = pipeline(
 def run_ner(text: str):
     if not text.strip():
         return {"text": "", "entities": []}, []
+
     out = ner_pipe(text)
-    # Gradio HighlightedText format
+
     highlighted = {
         "text": text,
         "entities": [
-            {"entity": r["entity_group"], "start": int(r["start"]), "end": int(r["end"]), "score": float(r["score"])}
+            {
+                "entity": r["entity_group"],
+                "start": int(r["start"]),
+                "end": int(r["end"]),
+                "score": float(r["score"]),
+            }
             for r in out
         ],
     }
-    # also return raw rows for inspection
+
+    # list-of-lists in a fixed column order
     rows = [
-        {"entity": r["entity_group"], "word": r["word"], "score": float(r["score"]), "start": int(r["start"]), "end": int(r["end"])}
+        [r["entity_group"], r["word"], float(r["score"]), int(r["start"]), int(r["end"])]
         for r in out
     ]
     return highlighted, rows
@@ -48,7 +55,11 @@ with gr.Blocks() as demo:
     gr.Markdown("# 🩺 Biomedical NER (offline, local model)")
     inp = gr.Textbox(label="Enter text", value="Patient has a history of asthma treated with albuterol.")
     ner_view = gr.HighlightedText(label="Entities", combine_adjacent=True)
-    table = gr.Dataframe(label="Raw predictions", interactive=False)
+    table = gr.Dataframe(
+        label="Raw predictions",
+        headers=["entity", "word", "score", "start", "end"],  # <-- headers for list-of-lists
+        interactive=False,
+    )
     inp.change(run_ner, inp, [ner_view, table])
 
 demo.launch(debug=True)