Update app.py

app.py

@@ -278,7 +278,7 @@ igen_pubmed = gr.Interface(keyphrase_generator,
                                                  'sentence-transformers/sentence-t5-large',
                                                  'sentence-transformers/sentence-t5-base'], 
                                         type="value", 
-                                        default='sentence-transformers/stsb-roberta-base-v2', 
+                                        value='sentence-transformers/stsb-roberta-base-v2', 
                                         label="Select any SBERT model for TextRank from the list below"),
                      gr.components.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2',
                                                  'sentence-transformers/all-mpnet-base-v1',
@@ -295,21 +295,21 @@ igen_pubmed = gr.Interface(keyphrase_generator,
                                                  'sentence-transformers/paraphrase-MiniLM-L3-v2',
                                                  'sentence-transformers/all-MiniLM-L6-v2'], 
                                         type="value", 
-                                        default='sentence-transformers/all-mpnet-base-v1', 
+                                        value='sentence-transformers/all-mpnet-base-v1', 
                                         label="Select any SBERT model for keyphrases from the list below"),                                                     
                      gr.components.Slider(minimum=5, maximum=20, step=1, default=10, label="Max Keywords"),
                      gr.components.Dropdown(choices=['cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
                                                  'cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token'],
                                         type="value", 
-                                        default='cambridgeltl/SapBERT-from-PubMedBERT-fulltext', 
+                                        value='cambridgeltl/SapBERT-from-PubMedBERT-fulltext', 
                                         label="Select any SapBERT model for clustering from the list below"),
-                     gr.components.Slider(minimum=5, maximum=15, step=1, default=10, label="PubMed Max Abstracts"),
+                     gr.components.Slider(minimum=5, maximum=15, step=1, value=10, label="PubMed Max Abstracts"),
                      gr.components.Dropdown(choices=['pritamdeka/S-Bluebert-snli-multinli-stsb',
                                                  'pritamdeka/S-BioBert-snli-multinli-stsb',
                                                  'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
                                                  'sentence-transformers/all-mpnet-base-v2'], 
                                         type="value", 
-                                        default='sentence-transformers/all-mpnet-base-v2', 
+                                        value='sentence-transformers/all-mpnet-base-v2', 
                                         label="Select any SBERT model for abstracts from the list below")], 
              #outputs=gr.outputs.Dataframe(type="auto", label="Retrieved Results from PubMed",max_cols=2, overflow_row_behaviour="paginate"), 
              outputs=gr.outputs.JSON(label="Title and Abstracts"),