Update app.py

app.py

@@ -6,8 +6,8 @@ model_g = gensim.models.KeyedVectors.load_word2vec_format('v_glove_300d_2.0' , b
 
 #retrieve the most similar words
 
-def generate(text):
-    result= model_g.most_similar('together',topn=10)
+def generate(word):
+    result= model_g.most_similar(word,topn=10)
     return result
 
 
@@ -18,7 +18,7 @@ examples = [
 ]
 
 title = "Visually Grounded embeddings"
-description = 'Get the top 10 nearest neighbors from a visually grounded word embedding model described in [this paper](https://arxiv.org/abs/2206.08823).<br>'
+description = 'Get the top 10 nearest neighbors with cosine similarities from a visually grounded word embedding model described in [this paper](https://arxiv.org/abs/2206.08823).<br>'
 txt = gr.Textbox(lines=1, label="Query word", placeholder="muffin")
 out = gr.Textbox(lines=4, label="top 10 nearest neighbors")