Update app.py

app.py

@@ -1,13 +1,28 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+# import streamlit as st
+# from transformers import AutoTokenizer, AutoModelForTokenClassification
+# from transformers import pipeline
+
+# tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
+# model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
+# nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+
+# example = st.text_area("Enter text: ")
+
+# if example:
+#     ner_results = nlp(example)
+#     st.json(ner_results)
 from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
-model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
-nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+# Load pre-trained NER model
+ner = pipeline('ner', model='bert-base-cased', tokenizer='bert-base-cased')
+
+# Define input text
+input_text = "The Mona Lisa is a 16th century portrait painted by Leonardo da Vinci."
+
+# Call the NER pipeline on the input text
+ner_results = ner(input_text)
 
-example = st.text_area("Enter text: ")
+# Print the named entities and their labels
+for result in ner_results:
+    print(result['word'], result['entity'])
 
-if example:
-    ner_results = nlp(example)
-    st.json(ner_results)