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| 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) |