Upload app.py

app.py

@@ -1,7 +1,11 @@
 import streamlit as st
 from transformers import pipeline
+from sentence_transformers import SentenceTransformer
 
-# Load the sentiment analysis model
+# Load the Sentence Transformers model
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# Load the website classification model (replace 'Suphawan/website_classification' with your actual model)
 classifier = pipeline("sentiment-analysis", model="Suphawan/website_classification")
 
 def main():
@@ -17,9 +21,13 @@ def main():
             classification_results = classifier([text])
             category = classification_results[0]['label']
             confidence = classification_results[0]['score']
+            
+            # Compute sentence embeddings
+            sentence_embeddings = model.encode([text])
 
             st.write("Category:", category)
             st.write("Confidence:", confidence)
+            st.write("Sentence Embeddings:", sentence_embeddings)
 
 if __name__ == "__main__":
     main()