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