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import json
import spacy
import gensim
import streamlit as st
from transformers import pipeline
@st.cache_resource
def load_w2v(model_path):
_w2v_model = gensim.models.KeyedVectors.load_word2vec_format(model_path, binary=True)
return _w2v_model
@st.cache_resource
def load_spacy():
_nlp = spacy.load('es_core_news_lg')
return _nlp
@st.cache_resource
def load_bert():
return pipeline("fill-mask", model="a-v-white/bert-base-spanish-wwm-cased-finetuned-literature-pro")
nlp = load_spacy()
w2v_model_path = r'ALL_annot_all_pos_spell_g_h_new_phrases_s300_cw10_mc100_w4_negative5-075_mean_e10_shr.bin.gz'
# Upload minimums
a1_path, a1_target_set = r'lexical_minimums/A1_MINIMUM.txt', set()
a2_path, a2_target_set = r'lexical_minimums/A2_MINIMUM.txt', set()
b1_path, b1_target_set = r'lexical_minimums/B1_MINIMUM.txt', set()
b2_path, b2_target_set = r'lexical_minimums/B2_MINIMUM.txt', set()
c1_path, c1_target_set = r'lexical_minimums/C1_MINIMUM.txt', set()
c2_path, c2_target_set = r'lexical_minimums/C2_MINIMUM.txt', set()
minimums_paths = (a1_path, a2_path, b1_path, b2_path)
minimums_sets = (a1_target_set, a2_target_set, b1_target_set, b2_target_set, c1_target_set, c2_target_set)
for i in range(len(minimums_paths)):
with open(minimums_paths[i], 'r', encoding='utf-8') as read_file:
for line in read_file:
minimums_sets[i].add(line.strip())
a1_distractor_set = a1_target_set
a2_distractor_set = a2_target_set.union(a1_target_set)
b1_distractor_set = b1_target_set.union(a2_target_set)
b2_distractor_set = b2_target_set.union(b1_target_set)
c1_distractor_set = c1_target_set.union(b2_target_set)
c2_distractor_set = c2_target_set.union(c1_target_set)
with open('language_data/phrases.json', 'r', encoding='utf-8') as f:
PHRASES = set(json.load(f)['PHRASES'])
with open('language_data/fix_irregular_lemma.json', 'r', encoding='utf-8') as f:
FIX_LEMMA = json.load(f)
SIMILARITY_VALUES = {'A1': 1.0, 'A2': 1.0, 'B1': 1.0, 'B2': 1.0, 'C1': 1.0, 'C2': 1.0, 'Нет': 1.0}
SIMILARITY_VALUES_bert = {'A1': 1.0, 'A2': 1.0, 'B1': 1.0, 'B2': 1.0, 'C1': 1.0, 'C2': 1.0, 'Нет': 1.0}
BAD_USER_TARGET_WORDS = []
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