....

style.css

@@ -127,3 +127,7 @@ section[tabindex="0"] .block-container {
   padding-top: 0px;
   padding-bottom: 0px;
 }
+
+.st-emotion-cache-12fmjuu {
+  height: 0rem;
+}

tabs/sentence_similarity_tab.py

@@ -22,15 +22,6 @@ sidebar_name = "Sentence Similarity"
 dataPath = st.session_state.DataPath
 
 
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
-embeddings = model.encode(sentences)
-st.write(embeddings)
-st.write("")
-st.write("")
-st.write("")
 
 '''
 with contextlib.redirect_stdout(open(os.devnull, "w")):
@@ -257,10 +248,11 @@ def proximite():
     plt.title(tr("Proximité des mots anglais avec leur traduction"), fontsize=30, color="green")
     plt.legend(loc='best');
     st.pyplot(fig)
-    
+'''    
 
 def run():
     
+    '''
     global max_lines, first_line, Langue
     global full_txt_en, full_corpus_en, full_txt_split_en, full_df_count_word_en,full_sent_len_en, vec_model_en 
     global full_txt_fr, full_corpus_fr, full_txt_split_fr, full_df_count_word_fr,full_sent_len_fr, vec_model_fr 
@@ -377,4 +369,15 @@ def run():
         )
         st.write("")
         proximite()
-'''
+    '''
+    sentences = ["This is an example sentence", "Each sentence is converted"]
+    sentences[0] = st.text_area(label=tr("Saisir le texte à traduire"), value="This is an example sentence")
+    sentences[1] = st.text_area(label=tr("Saisir le texte à traduire"), value="Each sentence is converted")
+
+
+    model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+    embeddings = model.encode(sentences)
+    st.write(embeddings)
+    st.write("")
+    st.write("")
+    st.write("")