Update app.py

app.py

@@ -53,7 +53,7 @@ def PICO_Tagger(text):
 title = "Utilizing BioBERT for PICO Evidence Summarization"
 description = "The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized the dataset from the paper published in IEEE Journal of Biomedical and Health Informatics <a href=\"https://ieeexplore.ieee.org/document/9056501\">Aceso: PICO-Guided Evidence Summarization on Medical Literature</a>"
 
-text1 = (
+text_fotter = (
     "<center> Author: Owais Ahmad ©2022 Data Scientist at <b> Thoucentric </b> <a href=\"https://www.linkedin.com/in/owaiskhan9654/\">Visit Profile</a> <br></center>"
     
     "<center> Model Trained Kaggle Kernel <a href=\"https://www.kaggle.com/code/owaiskhan9654/utilizing-biobert-for-pico-evidence-summarization\">Link</a> <br></center>"
@@ -70,6 +70,7 @@ PICO_APP = gr.Interface(PICO_Tagger,
     analytics_enabled=False,    
     title=title,
 	description=description,
-    article=text1,)
+    article=text_fotter,)
+
 
 PICO_APP.launch()