Update app.py

app.py

@@ -1,28 +1,24 @@
 import gradio as gr
 from transformers import pipeline
 
-ner = pipeline('ner', model = "Keyurjotaniya007/xlm-roberta-base-xtreme-multilingual-ner-2.0", grouped_entities=True)
+# Load the multilingual NER pipeline
+ner = pipeline("ner", model="Davlan/xlm-roberta-base-ner-hrl", grouped_entities=True)
 
-def merge_tokens(tokens):
-    merged_tokens = []
-    for token in tokens:
-        if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
-            last_token = merged_tokens[-1]
-            last_token['word'] += token['word'].replace('##', '')
-            last_token['end'] = token['end']
-            last_token['score'] = (last_token['score'] + token['score']) / 2
-        else:
-            merged_tokens.append(token)
+# Inference function
+def extract_entities(text):
+    results = ner(text)
+    return [(ent['word'], ent['entity_group']) for ent in results]
 
-    return merged_tokens
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown(instructions)
+    with gr.Row():
+        inp = gr.Textbox(label="Enter Text", placeholder="Type a sentence in any language...", lines=3)
+        out = gr.HighlightedText(label="Named Entities")
+    btn = gr.Button("Extract Entities")
 
-def named(input):
-    output = ner(input)
-    merged_word = merge_tokens(output)
-    return {'text': input, 'entities': merged_word}
+    btn.click(fn=extract_entities, inputs=inp, outputs=out)
 
-a = gr.Interface(fn=named, 
-                 inputs=[gr.Textbox(label="Text input", lines= 2)],
-                 outputs=[gr.HighlightedText(label='Text with entities')],
-                 title='Multilingual NER', examples=["My name is Keyur Jotaniya, and I live in Rajkot."])
-a.launch()
+# Launch
+if __name__ == "__main__":
+    demo.launch()