Update app.py

app.py

@@ -113,10 +113,10 @@ def keyphrase_generator(article_link, model_1, model_2, max_num_keywords):
   return keywords
 
 igen=gr.Interface(keyphrase_generator, 
-             inputs=[gr.inputs.Textbox(lines=3, placeholder="Provide article link here",default="", label="article link"),gr.inputs.Textbox(lines=1, placeholder="SBERT model",default="all-mpnet-base-v2", label="Model for TextRank (e.g. all-mpnet-base-v2)"),gr.inputs.Textbox(lines=1, placeholder="SBERT model",default="all-distilroberta-v1",label="Model for keyphrases (e.g. all-distilroberta-v1)"),gr.inputs.Slider(minimum=5, maximum=30, step=1, default=10, label="Max Keywords")], 
+             inputs=[gr.inputs.Textbox(lines=3, placeholder="Provide article link here", label="Article link"),gr.inputs.Textbox(lines=1, placeholder="SBERT model",default="all-mpnet-base-v2", label="SBERT model for TextRank (e.g. all-mpnet-base-v2)"),gr.inputs.Textbox(lines=1, placeholder="SBERT model",default="all-distilroberta-v1",label="SBERT model for Keyphrases (e.g. all-distilroberta-v1)"),gr.inputs.Slider(minimum=5, maximum=30, step=1, default=10, label="Max number of keyphrases to show")], 
              outputs="text", theme="huggingface", 
-             title="Scientific Article Keyphrase Generator", 
-             description="Generates the keyphrases from an article which best describes the article.",
+             title="Health Article Keyphrase Generator", 
+             description="Generates the keyphrases from an online health article which best describes the article.",
              article= "The work is based on a part of the paper <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking</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 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>."