Update app.py

app.py

@@ -260,6 +260,7 @@ def highlight_entities(article_content,summary_output):
     for entity in unmatched_entities:
         summary_output = summary_output.replace(entity, markdown_start_red + entity + markdown_end)
     soup = BeautifulSoup(summary_output, features="html.parser")
+    st.write(soup,unsafe_allow_html=True)
     return HTML_WRAPPER.format(soup)
 
 
@@ -274,26 +275,6 @@ def render_svg(svg_file):
         html = r'<img src="data:image/svg+xml;base64,%s"/>' % b64
         return html
 
-
-def generate_abstractive_summary(text, type, min_len=120, max_len=512, **kwargs):
-    text = text.strip().replace("\n", " ")
-    if type == "top_p":
-        text = summarization_model(text, min_length=min_len,
-                                   max_length=max_len,
-                                   top_k=50, top_p=0.95, clean_up_tokenization_spaces=True, truncation=True, **kwargs)
-    elif type == "greedy":
-        text = summarization_model(text, min_length=min_len,
-                                   max_length=max_len, clean_up_tokenization_spaces=True, truncation=True, **kwargs)
-    elif type == "top_k":
-        text = summarization_model(text, min_length=min_len, max_length=max_len, top_k=50,
-                                   clean_up_tokenization_spaces=True, truncation=True, **kwargs)
-    elif type == "beam":
-        text = summarization_model(text, min_length=min_len,
-                                   max_length=max_len,
-                                   clean_up_tokenization_spaces=True, truncation=True, **kwargs)
-    summary = text[0]['summary_text'].replace("<n>", " ")
-    return summary
-
 def clean_text(text,doc=False,plain_text=False,url=False):
     """Return clean text from the various input sources"""
 
@@ -319,7 +300,6 @@ def clean_text(text,doc=False,plain_text=False,url=False):
         return None, clean_text
         
 
-  
 @st.experimental_singleton(suppress_st_warning=True)
 def get_spacy():
     nlp = en_core_web_lg.load()
@@ -486,7 +466,7 @@ if summarize:
     
     with st.spinner("Calculating and matching entities, this takes a few seconds..."):
     
-        entity_match_html = highlight_entities(' '.join(cleaned_text[0]),summarized_text)
+        entity_match_html = highlight_entities(' '.join(text_to_summarize),summarized_text)
         st.subheader("Summarized text with matched entities in Green and mismatched entities in Red relative to the Original Text")
         st.markdown("####")