Update app.py

app.py

@@ -1,25 +1,27 @@
 import streamlit as st
-import spacy
+from transformers import pipeline
 
-# Load spaCy NLP model for NER
-nlp = spacy.load("en_core_web_sm")
+model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetunned"
 
-# Streamlit app
-def main():
-    st.title("Named Entity Recognition (NER) Demo")
+st.set_page_config(page_title="Sentiment Analysis App")
 
-    # User input
-    text_input = st.text_area("Enter text:", "John Doe is the CEO of ABC Corp, and it is located in New York.")
 
-    # NER processing
-    if st.button("Extract Entities"):
-        doc = nlp(text_input)
+sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
 
-        # 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})")
+st.title("Sentiment Analysis App")
 
-if __name__ == "__main__":
-    main()
+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.")