Update app.py

app.py

@@ -24,9 +24,9 @@ def generate_review(prompt):
 title="Turkish Review Generator: A GPT2 based Text Generator Trained with a Custom Dataset"
 description= """Generate a review in Turkish by providing a prompt or selecting an example prompt below. 
 Generation takes <b>15-20 seconds</b> on average.
-Enjoy!
+Enjoy!"""
 
-<p>NOTE: Examples can sometimes  generate ERROR. When you see ERROR on the screen <b>just click SUBMIT</b>. Model will generate text in 15-20 secs.</p> """
+#<p>NOTE: Examples can sometimes  generate ERROR. When you see ERROR on the screen <b>just click SUBMIT</b>. Model will generate text in 15-20 secs.</p> 
 article = """<p style='text-align: center'>On YouTube:</p>
             <p style='text-align: center'><a href='https://youtube.com/playlist?list=PLQflnv_s49v9d9w-L0S8XUXXdNks7vPBL' target='_blank'>How to Train a Hugging Face Causal Language Model from Scratch with a Custom Dataset and a Custom Tokenizer?</a></p> 
             <p style='text-align: center'><a href='https://youtube.com/playlist?list=PLQflnv_s49v8aajw6m9MRNbAAbL63flKD' target='_blank'>Hugging Face kütüphanesini kullanarak bir GPT2 Transformer Dil Modelini Kendi Veri Setimizle nasıl eğitip kullanabiliriz? (in Turkish)</a></p>
@@ -46,9 +46,9 @@ demo = gr.Interface(fn=generate_review,
                     description= description,
                     article = article,
                     #cache_examples = False
-                    #allow_flagging="manual",
-                    #flagging_options=["good","moderate", "non-sense", ]
-                    #flagging_dir='./flags'
+                    allow_flagging="manual",
+                    flagging_options=["good","moderate", "non-sense", ]
+                    flagging_dir='./flags'
                     )
 #demo.launch('share=True', 'enable_queue=True')
 demo.launch()