Changing the app: removing the slider and adding a section to find most likely topic of example

app.py

@@ -5,10 +5,16 @@ st.markdown('# Semantic search and topic classification (v1)')
 st.markdown(' - Author: hcontreras')
 st.markdown(' - Description: We want to classify sentences into a predefined set of topics. We use semantic search with a pre-trained transformer and we embed the input sentences and find the score relative to each topic')
 
+st.markdown('## A quick test')
+st.markdown('As a test we can create an embedding for a movie title and explore each component with the slider. Have fun!')
+
 from sentence_transformers import SentenceTransformer
 model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 
-input_sentence = st.text_input('Movie title', 'Life of Brian')
+input_sentence = st.text_input('Sentence', 'This is a test for a news article')
+input_topic = st.selectbox(
+    'Topic',
+    ('Space', 'Transportation', 'Health'))
 #st.write('The current movie title is', title)
 
 #Sentences we want to encode. Example:
@@ -16,14 +22,17 @@ sentence = ['This framework generates embeddings for each input sentence']
 
 
 #Sentences are encoded by calling model.encode()
-embedding = model.encode([input_sentence])
+embedding_sentence = model.encode([input_sentence])
+embedding_topics = model.encode(['Space','Transportation','Health'])
 
-x = st.slider('Select a value')
+#x = st.slider('Select a value')
 #embedding = model.encode(input_sentence)
 #st.write(x, 'squared is', x * x, 'embedding', embedding[0][0])
-st.write('The embedding of', '"' + input_sentence + '"', 'at position',x,'is',embedding[0][int(x)])
+#st.write('The embedding of', '"' + input_sentence + '"', 'at position',x,'is',embedding[0][int(x)])
 
+st.write('Score for topic', input_topic, ':')
 
+st.markdown('## ')
 uploaded_file1 = st.file_uploader("Choose a file: sentence list")
 if uploaded_file1 is not None:
     #read csv