Update app.py

app.py

@@ -271,12 +271,6 @@ def keyphrase_generator(article_link, model_1, model_2, max_num_keywords, model_
   return df_final
   
 
-examples = [
-  ["https://www.medicalnewstoday.com/articles/alzheimers-addressing-sleep-disturbance-may-alleviate-symptoms"],
-  ["https://www.medicalnewstoday.com/articles/omicron-what-do-we-know-about-the-stealth-variant"],
-  ["https://www.cancer.news/2022-02-04-doctors-testifying-covid-vaccines-causing-cancer-aids.html#",
-  ["https://www.cancer.news/2021-12-22-mrna-vaccines-weaken-immune-system-cause-cancer.html"]] 
-
 igen_pubmed = gr.Interface(keyphrase_generator, 
              inputs=[gr.inputs.Textbox(lines=1, placeholder="Provide article web link here (Can be chosen from examples below)",default="", label="Article web link"),
                      gr.inputs.Dropdown(choices=['sentence-transformers/all-mpnet-base-v2',
@@ -325,7 +319,10 @@ igen_pubmed = gr.Interface(keyphrase_generator,
              outputs=gr.outputs.Dataframe(type="auto", label="Retrieved Results from PubMed",max_cols=None, overflow_row_behaviour="paginate"), 
              theme="dark-peach", 
              title="PubMed Abstract Retriever", description="Retrieves relevant PubMed abstracts for an online article which can be used as further references. Please note that it may take sometime for the models to load.",
-             examples=examples,
+             examples=[["https://www.medicalnewstoday.com/articles/alzheimers-addressing-sleep-disturbance-may-alleviate-symptoms"],
+                       ["https://www.medicalnewstoday.com/articles/omicron-what-do-we-know-about-the-stealth-variant"],
+                       ["https://www.cancer.news/2022-02-04-doctors-testifying-covid-vaccines-causing-cancer-aids.html#",
+                       ["https://www.cancer.news/2021-12-22-mrna-vaccines-weaken-immune-system-cause-cancer.html"]],
              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 UMLS based BERT model, <a href=https://arxiv.org/abs/2010.11784>SapBERT</a> to cluster the keyphrases using K-means clustering method and finally create a boolean query. After that the top 10 titles and abstracts are retrieved from PubMed database and displayed according to relevancy. The SapBERT models can be changed as per the list provided. "