Update app.py

app.py

@@ -1,21 +1,31 @@
 import streamlit as st
 from transformers import pipeline
 
-# Title and description
-st.title("Sentiment Analysis")
-st.write("This application performs text classification using a pre-trained Hugging Face model.")
+# Load the sentiment analysis pipeline
+@st.cache_resource
+def load_model():
+    return pipeline("sentiment-analysis")
 
-# Define the model and pipeline
-model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-classifier = pipeline("sentiment-analysis", model=model_name)
+def main():
+    st.title("Sentiment Analysis App")
+    
+    # Create text input
+    user_input = st.text_area("Enter text for sentiment analysis:")
+    
+    # Analyze button
+    if st.button("Analyze Sentiment"):
+        if user_input:
+            # Load model
+            sentiment_model = load_model()
+            
+            # Perform sentiment analysis
+            result = sentiment_model(user_input)[0]
+            
+            # Display results
+            st.write("Sentiment:", result['label'])
+            st.write("Confidence Score:", f"{result['score']:.2%}")
+        else:
+            st.warning("Please enter some text to analyze.")
 
-# Get user input
-user_input = st.text_area("Enter text:", placeholder="Type your text here...")
-
-# Analyze and display results
-if st.button("Analyze"):
-    if user_input.strip():
-        result = classifier(user_input)
-        st.write(f"**Result:** {result[0]['label']} ({result[0]['score']:.2f})")
-    else:
-        st.warning("Please enter some text.")
+if __name__ == "__main__":
+    main()