Update app.py

app.py

@@ -1,30 +1,21 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load the text classification model pipeline
-classifier = pipeline("text-classification",model='isom5240sp24/bert-base-uncased-emotion', return_all_scores=True)
+def main():
+    sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
 
-# Streamlit application title
-st.title("Text Classification for you")
-st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
+    st.title("ISOM5240: Sentiment Analysis")
+    st.write("Enter a sentence to analyze its sentiment:")
 
-# Text input for user to enter the text to classify
-text = st.text_area("Enter the text to classify", "")
+    user_input = st.text_input("")
 
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Classify"):
-    # Perform text classification on the input text
-    results = classifier(text)[0]
+    if user_input:
+        result = sentiment_pipeline(user_input)
+        sentiment = result[0]["label"]
+        confidence = result[0]["score"]
 
-    # Display the classification result
-    max_score = float('-inf')
-    max_label = ''
+        st.write(f"Sentiment: {sentiment.lower()}")
+        st.write(f"Confidence: {confidence:.4f}")
 
-    for result in results:
-        if result['score'] > max_score:
-            max_score = result['score']
-            max_label = result['label']
-
-    st.write("Text:", text)
-    st.write("Label:", max_label)
-    st.write("Score:", max_score)
+if __name__ == "__main__":
+    main()