Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Job-coach-document-testing-site.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1ECyP45v5tfkn0I-0W24qWGQotWyqPIQS
+"""
+
+! pip install gradio
+! pip install transformers
+import gradio as gr
+
+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+from transformers import pipeline
+
+def QA_function(context, queries):
+    #tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+
+    model = "bert-large-uncased-whole-word-masking-finetuned-squad"
+
+    question_answerer = pipeline("question-answering", model = model)
+    Answered=[]
+    NotAnswered=[]
+    queries=queries.split("?")
+    queries.pop(-1)
+    for query in queries:
+      query.strip()
+      result = question_answerer(question = query, context=context)
+      if result['score'] > 0.01:
+        Answered.append(query +"?   Answer: " + result['answer'])
+      else:
+        NotAnswered.append(query)
+
+    #print("Question Answered: ")
+    #for answer in Answered:
+      #print(answer)
+    # print("Question Not Answered: ")
+    # for answer in NotAnswered:
+    #   print(answer)
+    result1=''',
+    '''.join(Answered) 
+    result1='''Question Answered: 
+    '''+ result1
+    result2=''',
+    '''.join(NotAnswered) 
+    result2='''
+    Question Not Answered: 
+    '''+ result2
+
+    return result1 + result2
+
+title = "Testing demo"
+description = "A testing site for Job Coach Documents"
+
+gradio_ui = gr.Interface(QA_function, [gr.inputs.Textbox(lines=10, label="Context"), gr.inputs.Textbox(lines=10, label="Question")], gr.outputs.Textbox(label="Answer"))
+gradio_ui.launch(debug=True)