Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+iimport gradio as gr
 from transformers import pipeline
 
 # Load the NER pipeline from Hugging Face
@@ -6,21 +6,30 @@ ner_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-e
 
 # Function to perform NER
 def perform_ner(text):
-    # Run NER on the input text
-    results = ner_pipeline(text)
-    
-    # Format results for better readability
-    formatted_results = [
-        {
-            "Entity": result["entity"],
-            "Word": result["word"],
-            "Score": round(result["score"], 4),
-            "Start": result["start"],
-            "End": result["end"]
-        }
-        for result in results
-    ]
-    return formatted_results
+    try:
+        # Run NER on the input text
+        results = ner_pipeline(text)
+        
+        # Check for empty results
+        if not results:
+            return [{"Entity": "-", "Word": "-", "Score": "-", "Start": "-", "End": "-"}]
+        
+        # Format results for better readability
+        formatted_results = [
+            {
+                "Entity": result.get("entity", "-"),
+                "Word": result.get("word", "-"),
+                "Score": round(result.get("score", 4), 4),
+                "Start": result.get("start", "-"),
+                "End": result.get("end", "-")
+            }
+            for result in results
+        ]
+        return formatted_results
+    except Exception as e:
+        print(f"Error: {e}")
+        # Return a default error response
+        return [{"Entity": "Error", "Word": "Error", "Score": 0, "Start": 0, "End": 0}]
 
 # Define the Gradio interface
 title = "Named Entity Recognition (NER) App"
@@ -44,3 +53,4 @@ interface.launch()
 
 
 
+