Add application file

app.py

@@ -0,0 +1,50 @@
+import os
+import gradio as gr
+import pandas as pd
+from question_generation.pipelines import pipeline
+import docx2txt
+
+qa_list = []
+def process_file(Notes):
+
+  os.system("pip install -U transformers==3.0.0")
+  os.system("python -m nltk.downloader punkt")
+  nlp = pipeline("question-generation", model="valhalla/t5-small-qg-prepend", qg_format="prepend")
+  
+  target_word_doc = Notes.name
+  raw_word_file = docx2txt.process(target_word_doc)
+
+  #remove empty lines
+  preprocessed_sentence_list = [i for i in raw_word_file.splitlines() if i != ""]
+
+  #grab content
+  processed_sentence_list = []
+  content = False
+  for i in preprocessed_sentence_list:
+    if "Outline" in i:
+      content = True
+      continue
+    if "Summary Learning Points" in i:
+      content = False
+      continue
+    if "Learning Activity" in i:
+      content = False
+      continue
+    if content == True:
+      processed_sentence_list.append(i.lstrip())
+
+  qa_list.extend(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")])
+io.launch()

question_generation

@@ -0,0 +1 @@
+Subproject commit c30e2976d65c4ef6200c7504097e8e07545fb240