Update app.py

app.py

@@ -273,7 +273,7 @@ igen_pubmed = gr.Interface(keyphrase_generator,
              outputs=gr.outputs.Dataframe(type="auto", label="dataframe",max_cols=None, max_rows=10, overflow_row_behaviour="paginate"), 
              theme="dark-peach", 
              title="PubMed Abstract Retriever", description="Generates the keyphrases from an article which best describes the article.",
-             article= "The work is based the paper <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>provided here</a>." 
+             article= "This work is based on the paper <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>provided here</a>." 
              "\t It uses the TextRank algorithm with SBERT to first find the top sentences and then extracts the keyphrases from those sentences using scispaCy and SBERT."
              "\t The application then uses a <a href=https://arxiv.org/abs/2010.11784>UMLS based Bert model</a> to cluster the keyphrases using K-means clustering method and finally create a boolean query. After that the top 20 titles and abstracts are retrieved from PubMed database and displayed according to relevancy. "
              "\t The list of SBERT models required in the textboxes can be found in <a href=www.sbert.net/docs/pretrained_models.html>SBERT Pre-trained models hub</a>."