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README.md

@@ -1,8 +1,8 @@
 ---
-title: WikipediaAIDataScience
-emoji: 🐢
-colorFrom: blue
-colorTo: red
+title: WikipediaAIWithDataframeMemory
+emoji: 🏢
+colorFrom: gray
+colorTo: pink
 sdk: gradio
 sdk_version: 3.16.2
 app_file: app.py

app.py

@@ -0,0 +1,200 @@
+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) 

requirements.txt

@@ -0,0 +1,10 @@
+wikipedia
+spacy
+faiss-cpu
+pandas
+transformers
+tensorflow
+wikipedia-api
+beautifulsoup4
+gradio
+requests