Добавлена гистограма.

app.py

@@ -13,9 +13,9 @@ transformers.utils.logging.disable_progress_bar()
 os.system("python3 -m spacy download en")
 
 st.markdown("""### TL;DR: give me the keywords!
-Here you can get the keywords and topic of the article based on it's title or abstract.
+Здесь вы можете получить отранжированный список ключевых слов по названию и аннотации статьи.
 
-The only supported language is English.""")
+Единственным поддерживаемым языком является английский.""")
 
 st.markdown("<p style=\"text-align:center\"><img width=100% src='https://c.tenor.com/IKt-6tAk9CUAAAAd/thats-a-lot-of-words-lots-of-words.gif'></p>", unsafe_allow_html=True)
 
@@ -24,8 +24,8 @@ st.markdown("<p style=\"text-align:center\"><img width=100% src='https://c.tenor
 #pipe = pipeline("ner", "Davlan/distilbert-base-multilingual-cased-ner-hrl")
 
 #st.markdown("#### Title:")
-title = st.text_area("Title:", value="How to cook a neural network", height=16, help="Title of the article")
-abstract = st.text_area("Abstract:",
+title = st.text_area("Заголовок:", value="How to cook a neural network", height=16, help="Заголовок статьи")
+abstract = st.text_area("Аннотация:",
     value="""My dad fits hellish models in general.
 Well, this is about an average recipe, because there are a lot of variations.
 The model is taken, it is not finetuned, finetuning is not about my dad.
@@ -41,7 +41,7 @@ Kindly offers me sometimes, but I refuse.
 Do I need to talk about what the wildest overfitting then?
 The overfitting is such that the val loss peels off the walls.
     """,
-    height=512, help="Abstract of the article")
+    height=512, help="Аннотация статьи")
 
 # Spacy
 
@@ -81,7 +81,10 @@ if not text is None and len(text) > 0:
 
     # График важности слов.
     fig, ax = plt.subplots(figsize=(8, 16))
-    ax.set_title("Ключевые слова в порядке важности")
+    ax.set_title("95% самых важных ключевых слов")
+    ax.grid(color='#000000', alpha=0.15, linestyle='-', linewidth=1, which='major')
+    ax.grid(color='#000000', alpha=0.1, linestyle='-', linewidth=0.5, which='minor')
+    ax.xaxis.set_minor_locator(locmin)
 
     bar_width = 0.75
     indexes = -np.arange(len(labels))