Вторая попытка.

app.py

@@ -1,12 +1,17 @@
 import streamlit as st
 
-st.markdown("### TL;DR: give me the keywords!")
-st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
+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.""")
+
+st.markdown("<p style="text-align:center"><img width=200px src='https://c.tenor.com/IKt-6tAk9CUAAAAd/thats-a-lot-of-words-lots-of-words.gif'></p>", unsafe_allow_html=True)
+
 
 from transformers import pipeline
 
 pipe = pipeline("ner", "Davlan/distilbert-base-multilingual-cased-ner-hrl")
 
-text = st.text_area("TEXT HERE")
+#st.markdown("#### Title:")
+title = st.text_area("Title:")
+abstract = st.text_area("abstract:")
 
-st.markdown(f"{pipe(text)}")
+#st.markdown(f"{pipe(text)}")

requirements.txt

@@ -1,3 +1,7 @@
 transformers
 torch
 sklearn
+spacy
+numpy
+scipy
+re