Update app.py

app.py

@@ -4,10 +4,11 @@ import gradio as gr
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 #question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
 #question_tokenizer = T5Tokenizer.from_pretrained('t5-base')
-question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
-question_tokenizer = T5Tokenizer.from_pretrained('t5-small')
+
 
 def get_question(context, answer):
+    question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
+    question_tokenizer = T5Tokenizer.from_pretrained('t5-small')
     text = "context: {} answer: {}".format(context, answer)
     #max_len = 512
     #encoding = question_tokenizer.encode_plus(text, max_length=max_len, padding='max_length', truncation=True, return_tensors="pt")
@@ -19,7 +20,7 @@ def get_question(context, answer):
                                     num_beams=3, # Use fewer beams to generate fewer but higher-quality questions
                                     num_return_sequences=3,
                                     no_repeat_ngram_size=3, # Allow some repetition to avoid generating nonsensical questions
-                                    max_length=128) # Use a shorter max length to focus on generating more relevant questions
+                                    max_length=256) # Use a shorter max length to focus on generating more relevant questions
     dec = [question_tokenizer.decode(ids) for ids in outs]
     questions = ""
     for i, question in enumerate(dec):