Update app.py

app.py

@@ -1,3 +1,4 @@
+"""
 import os 
 import gradio as gr
 
@@ -13,22 +14,14 @@ def get_question(context, answer):
     #encoding = question_tokenizer.encode_plus(text, max_length=max_len, padding='max_length', truncation=True, return_tensors="pt")
     encoding = question_tokenizer.encode_plus(text, return_tensors="pt")
     input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
-    #outs = question_model.generate(input_ids=input_ids,
-    #                                attention_mask=attention_mask,
-    #                                early_stopping=True,
-    #                                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=256) # Use a shorter max length to focus on generating more relevant questions
-
     outs = question_model.generate(input_ids=input_ids,
-                                attention_mask=attention_mask,
-                                early_stopping=True,)
-
-
-
+                                    attention_mask=attention_mask,
+                                    early_stopping=True,
+                                    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=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):
@@ -49,4 +42,46 @@ interface = gr.Interface(
     outputs=output_question
 )
 
+interface.launch()
+"""
+
+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')
+
+def get_question(sentence,answer):
+  text = "context: {} answer: {} </s>".format(sentence,answer)
+  print (text)
+  max_len = 256
+  encoding = question_tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=True, return_tensors="pt")
+
+  input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
+
+  outs = question_model.generate(input_ids=input_ids,
+                                  attention_mask=attention_mask,
+                                  early_stopping=True,
+                                  num_beams=5,
+                                  num_return_sequences=1,
+                                  no_repeat_ngram_size=2,
+                                  max_length=200)
+
+
+  dec = [question_tokenizer.decode(ids) for ids in outs]
+
+  Question = dec[0].replace("question:","")
+  Question= Question.strip()
+  return Question
+
+input_context = gr.Textbox()
+input_answer = gr.Textbox()
+output_question = gr.Textbox()
+
+interface = gr.Interface(
+    fn=get_question,
+    inputs=[input_context, input_answer],
+    outputs=output_question
+)
+
 interface.launch()