Create app.py

app.py

@@ -0,0 +1,51 @@
+from transformers import pipeline
+import wikipedia
+import random
+import gradio as gr
+model_name = "deepset/electra-base-squad2"
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+
+def get_wiki_article(topic):
+    topic=topic
+    try:
+        search = wikipedia.search(topic, results = 1)[0]
+    except wikipedia.DisambiguationError as e:
+        choices = [x for x in e.options if ('disambiguation' not in x) and ('All pages' not in x) and (x!=topic)]
+        search = random.choice(choices)
+    try:
+        p = wikipedia.page(search)
+    except wikipedia.exceptions.DisambiguationError as e:
+        choices = [x for x in e.options if ('disambiguation' not in x) and ('All pages' not in x) and (x!=topic)]
+        s = random.choice(choices)
+        p = wikipedia.page(s)
+    return p.content, p.url
+
+def get_answer(topic, question):
+    w_art, w_url=get_wiki_article(topic)
+    qa = {'question': question, 'context': w_art}
+    res = nlp(qa)
+    return res['answer'], w_url, {'confidence':res['score']}
+
+
+inputs = [
+          gr.inputs.Textbox(lines=5, label="Topic"),
+          gr.inputs.Textbox(lines=5, label="Question")
+]
+outputs = [
+            gr.outputs.Textbox(type='str',label="Answer"),
+            gr.outputs.Textbox(type='str',label="Wikipedia Reference Article"),
+            gr.outputs.Label(type="confidences",label="Confidence in answer (assuming the correct wikipedia article)"),
+]
+
+title = "AI Wikipedia Search"
+description = 'Contextual Question and Answer'
+article = ''
+examples = [
+    ['State of Minnesota', 'What birds and mammals are in Minnesota?'],
+    ['Syncope', 'What are possible causes of it?'],
+    ['Marvel', 'Who are the artists?'],   
+    ['DC Comics', 'Who are the artists?'],
+    ['Artwork', 'What are the top paintings sold at auction?']     
+]
+
+gr.Interface(get_answer, inputs, outputs, title=title, description=description, article=article, examples=examples, flagging_options=["strongly related","related", "neutral", "unrelated", "strongly unrelated"]).launch(share=False,enable_queue=False)