adapt code to one sentence list

move to one sentence and created cosine similarity matrix

app.py

@@ -10,25 +10,19 @@ st.write(dataset['train'][0])
 from sentence_transformers import SentenceTransformer, util
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
-# Two lists of sentences
-sentences1 = ['The cat sits outside',
-             'A man is playing guitar',
-             'The new movie is awesome']
+# Sentences from the data set
+sentences = dataset['train'][:10]
 
-sentences2 = ['The dog plays in the garden',
-              'A woman watches TV',
-              'The new movie is so great']
-
-#Compute embedding for both lists
-embeddings1 = model.encode(sentences1, convert_to_tensor=True)
-embeddings2 = model.encode(sentences2, convert_to_tensor=True)
+#Compute embedding 
+embeddings1 = model.encode(sentences, convert_to_tensor=True)
 
 #Compute cosine-similarities
-cosine_scores = util.cos_sim(embeddings1, embeddings2)
+cosine_scores = util.cos_sim(embeddings, embeddings)
 
 #Output the pairs with their score
-for i in range(len(sentences1)):
-    st.write("{} \t\t {} \t\t Score: {:.4f}".format(sentences1[i], sentences2[i], cosine_scores[i][i]))
+for i in range(len(sentences)):
+    for j in range(i):
+        st.write("{} \t\t {} \t\t Score: {:.4f}".format(sentences[i], sentences[j], cosine_scores[i][j]))
 
 
 #-------------------------------------------------------------