Create app.py

app.py

@@ -0,0 +1,53 @@
+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 = "Question Answering with ELECTRA and Wikipedia"
+description = 'Please note that topics with long articles may take around a minute. If you get an error, please try double checking spelling, or try a more specific topic (e.g. George H. Bush instead of George Bush).'
+article = ''
+examples = [
+    ["Unabomber","What radicalized him?"],
+    ["George H. Bush","Did he pursue higher education?"],
+    ["Jayson Tatum","How was he percieved coming out of high school?"],
+
+]
+
+gr.Interface(get_answer, inputs, outputs, title=title, description=description, article=article, 
+ theme="darkdefault", examples=examples, flagging_options=["strongly related","related", "neutral", "unrelated", "stongly unrelated"]).launch(share=True,enable_queue=False)