qg

app.py

@@ -9,7 +9,6 @@ import pandas as pd
 from question_generation.pipelines import pipeline
 import docx2txt
 
-qa_list = []
 def process_file(Notes):
 
   nlp = pipeline("question-generation", model="valhalla/t5-small-qg-prepend", qg_format="prepend")
@@ -36,17 +35,11 @@ def process_file(Notes):
     if content == True:
       processed_sentence_list.append(i.lstrip())
 
-  qa_list.extend(nlp(" ".join(processed_sentence_list)))
+  qa_list = nlp(" ".join(processed_sentence_list))
   formatted_questions = "\n".join([str(idx+1) + ". " + i["question"] for idx, i in enumerate(qa_list)])
   formatted_answers = "\n".join([str(idx+1) + ". " + i["answer"] for idx, i in enumerate(qa_list)])
   return [formatted_questions, formatted_answers]
 
-def reveal_answer():
-  global qa_list
-  
-  qa_list = []
-  return formatted_answers
-
 io = gr.Interface(process_file, "file", outputs=
                   [gr.Textbox(lines=1, label="Questions"), 
                    gr.Textbox(lines=1, label="Answers")])