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| import spacy | |
| import wikipediaapi | |
| import wikipedia | |
| from wikipedia.exceptions import DisambiguationError | |
| from transformers import TFAutoModel, AutoTokenizer | |
| import numpy as np | |
| import pandas as pd | |
| import faiss | |
| import gradio as gr | |
| try: | |
| nlp = spacy.load("en_core_web_sm") | |
| except: | |
| spacy.cli.download("en_core_web_sm") | |
| nlp = spacy.load("en_core_web_sm") | |
| wh_words = ['what', 'who', 'how', 'when', 'which'] | |
| def get_concepts(text): | |
| text = text.lower() | |
| doc = nlp(text) | |
| concepts = [] | |
| for chunk in doc.noun_chunks: | |
| if chunk.text not in wh_words: | |
| concepts.append(chunk.text) | |
| return concepts | |
| def get_passages(text, k=100): | |
| doc = nlp(text) | |
| passages = [] | |
| passage_len = 0 | |
| passage = "" | |
| sents = list(doc.sents) | |
| for i in range(len(sents)): | |
| sen = sents[i] | |
| passage_len+=len(sen) | |
| if passage_len >= k: | |
| passages.append(passage) | |
| passage = sen.text | |
| passage_len = len(sen) | |
| continue | |
| elif i==(len(sents)-1): | |
| passage+=" "+sen.text | |
| passages.append(passage) | |
| passage = "" | |
| passage_len = 0 | |
| continue | |
| passage+=" "+sen.text | |
| return passages | |
| def get_dicts_for_dpr(concepts, n_results=20, k=100): | |
| dicts = [] | |
| for concept in concepts: | |
| wikis = wikipedia.search(concept, results=n_results) | |
| print(concept, "No of Wikis: ",len(wikis)) | |
| for wiki in wikis: | |
| try: | |
| html_page = wikipedia.page(title = wiki, auto_suggest = False) | |
| except DisambiguationError: | |
| continue | |
| htmlResults=html_page.content | |
| passages = get_passages(htmlResults, k=k) | |
| for passage in passages: | |
| i_dicts = {} | |
| i_dicts['text'] = passage | |
| i_dicts['title'] = wiki | |
| dicts.append(i_dicts) | |
| return dicts | |
| passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2") | |
| query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2") | |
| p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2") | |
| q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2") | |
| def get_title_text_combined(passage_dicts): | |
| res = [] | |
| for p in passage_dicts: | |
| res.append(tuple((p['title'], p['text']))) | |
| return res | |
| def extracted_passage_embeddings(processed_passages, max_length=156): | |
| passage_inputs = p_tokenizer.batch_encode_plus( | |
| processed_passages, | |
| add_special_tokens=True, | |
| truncation=True, | |
| padding="max_length", | |
| max_length=max_length, | |
| return_token_type_ids=True | |
| ) | |
| passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']), | |
| np.array(passage_inputs['attention_mask']), | |
| np.array(passage_inputs['token_type_ids'])], | |
| batch_size=64, | |
| verbose=1) | |
| return passage_embeddings | |
| def extracted_query_embeddings(queries, max_length=64): | |
| query_inputs = q_tokenizer.batch_encode_plus( | |
| queries, | |
| add_special_tokens=True, | |
| truncation=True, | |
| padding="max_length", | |
| max_length=max_length, | |
| return_token_type_ids=True | |
| ) | |
| query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']), | |
| np.array(query_inputs['attention_mask']), | |
| np.array(query_inputs['token_type_ids'])], | |
| batch_size=1, | |
| verbose=1) | |
| return query_embeddings | |
| #Wikipedia API: | |
| def get_pagetext(page): | |
| s=str(page).replace("/t","") | |
| return s | |
| def get_wiki_summary(search): | |
| wiki_wiki = wikipediaapi.Wikipedia('en') | |
| page = wiki_wiki.page(search) | |
| isExist = page.exists() | |
| if not isExist: | |
| return isExist, "Not found", "Not found", "Not found", "Not found" | |
| pageurl = page.fullurl | |
| pagetitle = page.title | |
| pagesummary = page.summary[0:60] | |
| pagetext = get_pagetext(page.text) | |
| backlinks = page.backlinks | |
| linklist = "" | |
| for link in backlinks.items(): | |
| pui = link[0] | |
| linklist += pui + " , " | |
| a=1 | |
| categories = page.categories | |
| categorylist = "" | |
| for category in categories.items(): | |
| pui = category[0] | |
| categorylist += pui + " , " | |
| a=1 | |
| links = page.links | |
| linklist2 = "" | |
| for link in links.items(): | |
| pui = link[0] | |
| linklist2 += pui + " , " | |
| a=1 | |
| sections = page.sections | |
| ex_dic = { | |
| 'Entity' : ["URL","Title","Summary", "Text", "Backlinks", "Links", "Categories"], | |
| 'Value': [pageurl, pagetitle, pagesummary, pagetext, linklist,linklist2, categorylist ] | |
| } | |
| df = pd.DataFrame(ex_dic) | |
| return df | |
| def search(question): | |
| concepts = get_concepts(question) | |
| print("concepts: ",concepts) | |
| dicts = get_dicts_for_dpr(concepts, n_results=1) | |
| lendicts = len(dicts) | |
| print("dicts len: ", lendicts) | |
| if lendicts == 0: | |
| return pd.DataFrame() | |
| processed_passages = get_title_text_combined(dicts) | |
| passage_embeddings = extracted_passage_embeddings(processed_passages) | |
| query_embeddings = extracted_query_embeddings([question]) | |
| faiss_index = faiss.IndexFlatL2(128) | |
| faiss_index.add(passage_embeddings.pooler_output) | |
| prob, index = faiss_index.search(query_embeddings.pooler_output, k=lendicts) | |
| return pd.DataFrame([dicts[i] for i in index[0]]) | |
| # AI UI SOTA - Gradio blocks with UI formatting, and event driven UI | |
| with gr.Blocks() as demo: # Block documentation on event listeners, start here: https://gradio.app/blocks_and_event_listeners/ | |
| gr.Markdown("<h1><center>🍰 Ultimate Wikipedia AI 🎨</center></h1>") | |
| gr.Markdown("""<div align="center">Search and Find Anything Then Use in AI! <a href="https://www.mediawiki.org/wiki/API:Main_page">MediaWiki - API for Wikipedia</a>. <a href="https://paperswithcode.com/datasets?q=wikipedia&v=lst&o=newest">Papers,Code,Datasets for SOTA w/ Wikipedia</a>""") | |
| with gr.Row(): # inputs and buttons | |
| inp = gr.Textbox(lines=1, default="Syd Mead", label="Question") | |
| with gr.Row(): # inputs and buttons | |
| b3 = gr.Button("Search AI Summaries") | |
| b4 = gr.Button("Search Web Live") | |
| with gr.Row(): # outputs DF1 | |
| out = gr.Dataframe(label="Answers", type="pandas") | |
| with gr.Row(): # output DF2 | |
| out_DF = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate", datatype = ["markdown", "markdown"], headers=['Entity', 'Value']) | |
| inp.submit(fn=get_wiki_summary, inputs=inp, outputs=out_DF) | |
| b3.click(fn=search, inputs=inp, outputs=out) | |
| b4.click(fn=get_wiki_summary, inputs=inp, outputs=out_DF ) | |
| demo.launch(debug=True, show_error=True) |