Update app.py

app.py

@@ -1,27 +1,24 @@
 import streamlit as st
-from transformers import pipeline
+import spacy
 
-model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetunned"
+# Load spaCy NLP model for NER
+nlp = spacy.load("en_core_web_sm")
 
-st.set_page_config(page_title="Sentiment Analysis App")
+# Streamlit app
+def main():
+    st.title("Named Entity Recognition (NER) Demo")
 
+    # User input
+    text_input = st.text_area("Enter text:", "John Doe is the CEO of ABC Corp, and it is located in New York.")
 
-sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
+    # NER processing
+    if st.button("Extract Entities"):
+        doc = nlp(text_input)
 
-st.title("Sentiment Analysis App")
+        # Display entities
+        entities = [(ent.text, ent.label_) for ent in doc.ents]
+        st.write("Named Entities:")
+        for entity, label in entities:
+            st.write(f"- {entity} ({label})")
 
-user_input = st.text_area("Enter a message:")
-
-if st.button("Analyze Sentiment"):
-    if user_input:
-        # Perform sentiment analysis
-        results = sentiment_classifier(user_input)
-        sentiment_label = results[0]["label"]
-        sentiment_score = results[0]["score"]
-
-        st.write(f"Sentiment: {sentiment_label}")
-        st.write(f"Confidence Score: {sentiment_score:.2f}")
-
-# Run the Streamlit app
-if _name_ == "_main_":
-    st.write("Enter a message and click 'Analyze Sentiment' to classify its sentiment.")
+if __name__ == "__main_