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app.py

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+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")
+
+    with st.form("text_field"):
+        text = st.text_area('Enter some text:')
+        # clicked will be True only when the button is clicked
+        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])
+
+            st.write("Sentiment:", sentiment)
+            st.write("Confidence:", confidence)
+            st.write("Sentence Embeddings:", sentence_embeddings)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+torch
+transformers
+altair<5