Update app.py

app.py

@@ -35,16 +35,16 @@ adequacy_score = Adequacy()
 fluency_score  = Fluency()
 diversity_score= Diversity()
 device= "cuda:0"
-adequacy_threshold = 0.99
-fluency_threshold = 0.90 # Fluency (Is the paraphrase fluent English?)
-diversity_ranker="levenshtein"
-do_diverse=True # Diversity (Lexical / Phrasal / Syntactical) (How much has the paraphrase changed the original sentence?)
+adequacy_threshold = 0.90#  Adequacy of the English sentences
+fluency_threshold = 0.80 #  English Fluency
+diversity_ranker="levenshtein" # Diversity (Lexical / Phrasal / Syntactical) (How much has the paraphrase changed the original sentence?)
+do_diverse=False # Diverse The sentences formation 
 #num_beam_groups (int) — Number of groups to divide num_beams into in order to ensure diversity among different groups of beams
 # adding the model
 
 model_name = 'tuner007/pegasus_paraphrase'
 torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = PegasusTokenizer.from_pretrained(model_name)
+tokenizer = PegasusTokenizer.from_pretrained(model_name) #Pre-trained Model
 model_pegasus = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
 
 def get_max_str(lst):
@@ -81,7 +81,7 @@ def get_fun(Input_txt):
     txt_paraphrase=txt_paraphrase+' '+tmp
   return txt_paraphrase
 
-iface = gr.Interface(fn=get_fun, inputs="text", outputs="text", title = " Ai Re-Phraser Q'Hackday")
+iface = gr.Interface(fn=get_fun, inputs="text", outputs="text", title = "Ai Re-Phraser Q'Hackday")
 iface.launch(inline=False)
 
 """# New Section"""