Update app.py

app.py

@@ -2,21 +2,23 @@
 import streamlit as st
 from transformers import pipeline
 from textblob import TextBlob
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
 
-pipe = pipeline('sentiment-analysis')
-st.title("Hugging Face Sentiment Analysis Spaces Example")
-st.subheader("What framework would you like to use for Sentiment Analysis")
-#Picking what NLP task you want to do
-option = st.selectbox('Framework',('Transformers', 'TextBlob')) #option is stored in this variable
-#Textbox for text user is entering
-st.subheader("Enter the text you'd like to analyze.")
-text = st.text_input('Enter text') #text is stored in this variable
-
-if option == 'Transformers':
-    out = pipe(text)
-else:
-    out = TextBlob(text)
-    out = out.sentiment
-st.write("Sentiment of Text: ")
-st.write(out)
+sentences = \
+    [
+     "I have a bigger house than you", 
+     "You have a bigger house than me"
+     ]
+     
+sentence_embeddings = model.encode(sentences)
 
+for sentence, embedding in zip(sentences, sentence_embeddings):
+    print("Sentence:", sentence)
+    print("Embedding:", embedding)
+    print("")
+    
+print('Similarity between {} and {} is {}'.format(sentences[0],
+       sentences[1],
+       cosine_similarity(sentence_embeddings[0].reshape(1, -1),
+       sentence_embeddings[1].reshape(1, -1))[0][0]))