extractor/_utils.py

import nmslib
import numpy as np
import streamlit as st
# import inflect
import torch

# p = inflect.engine()

class FewDocumentsError(Exception):
  def __init__(self, documents, size, msg):
    self.documents = documents
    self.size = size
    self.msg = msg

  def __str__(self):
    return repr(self.msg)

def document_extraction(dataset, query, keywords, min_document_size, min_just_one_paragraph_size):
  # TODO: compare inflected forms
  # word_in_text = lambda word, text: any([p.compare(word, w) for w in text.split()])
  word_in_text = lambda word, text: word in set(text.split())
  lower_dataset = [document.lower() for document in dataset]
  lower_query = query.lower()
  lower_keywords = [keyword.lower() for keyword in keywords]

  documents = {}

  documents['QUERY'] = [
    dataset[lower_dataset.index(document)] for document in lower_dataset
    if (word_in_text(lower_query, document))
    and (len(document.split()) > min_document_size)
    and any(len(paragraph.split()) > min_just_one_paragraph_size for paragraph in document.splitlines())
  ]
  
  documents['AND'] = [
    dataset[lower_dataset.index(document)] for document in lower_dataset
    if all(word_in_text(keyword, document) for keyword in lower_keywords)
    and (len(document.split()) > min_document_size)
    and any(len(paragraph.split()) > min_just_one_paragraph_size for paragraph in document.splitlines())
  ]

  documents['OR'] = [
    dataset[lower_dataset.index(document)] for document in lower_dataset
    if any(word_in_text(keyword, document) for keyword in lower_keywords)
    and (len(document.split()) > min_document_size)
    and any(len(paragraph.split()) > min_just_one_paragraph_size for paragraph in document.splitlines())
  ]

  empty = {
    'QUERY': len(documents['QUERY']) == 0,
    'AND': len(documents['AND']) == 0,
    'OR': len(documents['OR']) == 0
  }

  sizes = {
    'QUERY': len(documents['QUERY']),
    'AND': len(documents['AND']),
    'OR': len(documents['OR'])
  }

  if all(empty.values()):
        # TODO: throw error
        st.info(empty.values())
        st.warning(f'No document found for the query "{query}", please try with another query')
        st.stop()

  if sizes['QUERY'] >= 10:
      extracted_documents = documents['QUERY']
  elif sizes['AND'] >= 10:
      extracted_documents = documents['AND']
  elif sizes['OR'] >= 10:
      extracted_documents = documents['OR']
  else:
      number_of_documents = sizes['OR']
      raise FewDocumentsError(documents['OR'], number_of_documents, 
        f'Only {number_of_documents} documents found for the query "{query}"\n\
        Please select continue to proceed with {number_of_documents} documents or try again with another query'
      )

  return extracted_documents, empty, sizes

def paragraph_extraction(documents, min_paragraph_size):
  paragraphs = [
    documents[i].splitlines()[j] for i in range(len(documents)) for j in range(len(documents[i].splitlines()))
    if (len(documents[i].splitlines()[j].split()) > min_paragraph_size)
  ]

  return paragraphs

def semantic_search(model, query, files, number_of_similar_files):
  encoded_query = model.encode(query)
  encoded_files = model.encode(files)

  model_index = nmslib.init(method='hnsw', space='angulardist')
  model_index.addDataPointBatch(encoded_files)
  model_index.createIndex({'post': 2})

  ids, distances = model_index.knnQuery(encoded_query, k=number_of_similar_files)

  selected_files = [files[index] for index in ids]

  distances = 180*distances/np.pi

  return selected_files, distances;