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("

🍰 Ultimate Wikipedia AI 🎨

") gr.Markdown("""
Search and Find Anything Then Use in AI! MediaWiki - API for Wikipedia. Papers,Code,Datasets for SOTA w/ Wikipedia""") 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)