formating

app.py

@@ -12,42 +12,56 @@ bi_encoder = 'Bi-Encoder'
 cross_encoder = 'Cross-Encoder'
 levenshtein_distance = 'Levenshtein Distance'
 tf_idf = 'TF-IDF'
-random_forest = 'RandomForest'
+random_forest = 'Random Forest'
 title = 'Sentence Similarity with Transformers'
-tfidf_vectorizer = TfidfVectorizer()
-cross_encoder_trasformer = CrossEncoder(model_save_path)
-bi_encoder_trasformer = SentenceTransformer(model_save_path)
-random_forest_model = joblib.load('trained_model_random_forest.joblib')
+st.set_page_config(page_title=title, layout='wide', initial_sidebar_state='auto')
+@st.cache_data
+def cache_variables():
+    tfidf_vectorizer = TfidfVectorizer()
+    cross_encoder_trasformer = CrossEncoder(model_save_path)
+    bi_encoder_trasformer = SentenceTransformer(model_save_path)
+    random_forest_model = joblib.load('trained_model_random_forest.joblib')
+    return tfidf_vectorizer, cross_encoder_trasformer, bi_encoder_trasformer, random_forest_model
+
 @st.cache_data
 def compute_similarity(sentence_1, sentence_2, comparison):
     if comparison == bi_encoder:
         return cosine_similarity([bi_encoder_trasformer.encode(sentence_1)], [bi_encoder_trasformer.encode(sentence_2)])[0][0]
     return cross_encoder_trasformer.predict([sentence_1, sentence_2])
 
-st.set_page_config(page_title=title, layout = 'wide', initial_sidebar_state = 'auto')
+tfidf_vectorizer, cross_encoder_trasformer, bi_encoder_trasformer, random_forest_model = cache_variables()
+
 st.title(title)
 st.write("This app takes two sentences and outputs their similarity score using a fine-tuned transformer model.")
 
 # Example sentences section
 test_samples = get_samples()
 st.sidebar.header("Example Sentences")
-example_1 = st.sidebar.radio("Sentence 1", test_samples['sentence1'].values.tolist())
-example_2 = st.sidebar.radio("Sentence 2", test_samples['sentence2'].values.tolist())
+example_1 = st.sidebar.radio(
+    "Sentence 1", test_samples['sentence1'].values.tolist())
+example_2 = st.sidebar.radio(
+    "Sentence 2", test_samples['sentence2'].values.tolist())
 
 # Input fields
 sentence_1 = st.text_input("Enter Sentence 1:", example_1)
 sentence_2 = st.text_input("Enter Sentence 2:", example_2)
-comparison = st.selectbox("Comparicon:", [bi_encoder, cross_encoder, levenshtein_distance, tf_idf, random_forest])
+comparison = st.selectbox("Comparicon:", [
+                          bi_encoder, cross_encoder, levenshtein_distance, tf_idf, random_forest])
 
 if st.button("Compare"):
     # Compute similarity
     if comparison in [bi_encoder, cross_encoder]:
         similarity = compute_similarity(sentence_1, sentence_2, comparison)
     elif comparison == levenshtein_distance:
-        similarity = textdistance.levenshtein.normalized_similarity(sentence_1, sentence_2)
+        similarity = textdistance.levenshtein.normalized_similarity(
+            sentence_1, sentence_2)
     elif comparison == tf_idf:
-        similarity = cosine_similarity(tfidf_vectorizer.fit_transform([sentence_1, sentence_2]))[0][1]
+        similarity = cosine_similarity(
+            tfidf_vectorizer.fit_transform([sentence_1, sentence_2]))[0][1]
     elif comparison == random_forest:
-        similarity = random_forest_model.predict(encode_sentences(bi_encoder_trasformer, sentence_1, sentence_2))[0]
-    st.write(f"Similarity Score: {similarity:.4f}")
-    st.write("A higher score indicates greater similarity. The score ranges from 0 to 1.")
+        similarity = random_forest_model.predict(encode_sentences(
+            bi_encoder_trasformer, sentence_1, sentence_2))[0]
+    st.markdown(
+        f"<b style='font-size: 1.5em'>{comparison}</b> similarity score: <b style='font-size: 1.5em'>:red[{similarity:.4f}]</b>", unsafe_allow_html=True)
+    st.write(
+        "A higher score indicates greater similarity. The score ranges from 0 to 1.")