Upload app.py

app.py

@@ -1,15 +1,11 @@
 import streamlit as st
 from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-
-# Load the Sentence Transformers model
-model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
 
 # Load the sentiment analysis model
 classifier = pipeline("sentiment-analysis", model="Suphawan/website_classification")
 
 def main():
-    st.title("Website classification")
+    st.title("Website Classification")
 
     with st.form("text_field"):
         text = st.text_area('Enter some text:')
@@ -17,17 +13,13 @@ def main():
         clicked = st.form_submit_button("Submit text")
 
         if clicked:
-            # Perform sentiment analysis
-            sentiment_results = classifier([text])
-            sentiment = sentiment_results[0]['label']
-            confidence = sentiment_results[0]['score']
-            
-            # Compute sentence embeddings
-            sentence_embeddings = model.encode([text])
+            # Perform website classification
+            classification_results = classifier([text])
+            category = classification_results[0]['label']
+            confidence = classification_results[0]['score']
 
-            st.write("Sentiment:", sentiment)
+            st.write("Category:", category)
             st.write("Confidence:", confidence)
-            st.write("Sentence Embeddings:", sentence_embeddings)
 
 if __name__ == "__main__":
     main()