Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Job-coach-testing-site-zeroshot.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/github/vanderbilt-data-science/job-coach-question-answering/blob/111-create-a-gradio-hf-space-to-test-whether-specific-information-is-in-a-coaching-document/job-coach-testing-zeroshot.ipynb
+"""
+
+import gradio as gr
+
+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+from transformers import pipeline
+
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+question_answerer = pipeline("question-answering", model = "bert-large-uncased-whole-word-masking-finetuned-squad")
+def zshot(context, queries):
+    answered=[]
+    res=[]
+    notAnswered=[]
+    queries=queries.split("?")
+    queries.pop(-1)
+    for query in queries:
+      query.strip()
+      result = question_answerer(question = query, context=context)
+      answered.append([query,result['answer']])
+    
+    for item in answered:
+      result = ([text, classifier(text, item[0])])
+      if result[1]['scores'][0] > 0.01:
+        res.append(item[0] +"?   Answer: " + item[1])
+      else:
+        notAnswered.append(item[0])
+    result1=''',
+    '''.join(res) 
+    result1='''Information Included in the Document: 
+    '''+ result1
+    result2=''',
+    '''.join(notAnswered) 
+    result2='''
+    Information not included in the document, on no clearly stated: 
+    '''+ result2
+
+    return result1 + result2
+    
+
+app = gr.Interface(
+    zshot,
+    inputs=[
+        gr.Textbox(label="Context", value=""),
+        gr.Textbox(label="Queries", value=""),
+    ],
+    outputs=["text"],
+)
+app.launch()
+
+"""#### Examples"""