Update app.py

app.py

@@ -38,8 +38,7 @@ device= "cuda:0"
 adequacy_threshold = 0.90
 fluency_threshold = 0.90 # Fluency (Is the paraphrase fluent English?)
 diversity_ranker="levenshtein"
-do_diverse=False # Diversity (Lexical / Phrasal / Syntactical) (How much has the paraphrase changed the original sentence?)
-#num_beam_groups=num_beams, diversity_penalty=0.5
+do_diverse=True # Diversity (Lexical / Phrasal / Syntactical) (How much has the paraphrase changed the original sentence?)
 #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
 
@@ -51,8 +50,8 @@ model_pegasus = PegasusForConditionalGeneration.from_pretrained(model_name).to(t
 def get_max_str(lst):
     return max(lst, key=len)
 def get_response(input_text,num_return_sequences=10,num_beams=10):
-    batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=10,return_tensors='pt').to(torch_device)
-    translated = model_pegasus.generate(**batch,max_length=10,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
+    batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=90, return_tensors='pt').to(torch_device)
+    translated = model_pegasus.generate(**batch,max_length=90,num_beams=num_beams, num_return_sequences=num_return_sequences, num_beam_groups=num_beams, diversity_penalty=0.5, temperature=1.5)
     tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
     try:
         adequacy_filtered_phrases = adequacy_score.filter(input_text,tgt_text, adequacy_threshold, device)