Create app.py

app.py

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+import streamlit as st
+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
+
+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)
+        st.write(f"{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)