def analize_text(text: str, *, exact_words: bool = False) -> tuple:
sentences = array(split_into_sentences(text, True))
if (not len(sentences)):
print("Nothing found")
return []
tags = pos_tag_sents(map(word_tokenize, sentences))
if (not exact_words):
lemmatized = lemmatize_sents(tags)
else:
lemmatized = tags
chunker = RegexpParser("AC: {(<CD>?<TO|IN>?<CD>)+}\n "
"AN: {(<NPP>+<DT|NPP|JJ>*)+}\n "
"}<DT>+{\n "
"PH: {<[B-Z]+>+}\n "
"}<DT|CC|PRP|EX|WDT>+{")
chunked = list(chunker.parse_sents(lemmatized))
return (*setup_search_structure(chunked, tuple), sentences)
def main():
with open("test.txt", 'r', encoding="utf-8") as f:
text = f.read()
if (False):
sentences = array(split_into_sentences(text, True))
if (not len(sentences)):
print("Nothing found")
exit(-1)
tags = pos_tag_sents(map(word_tokenize, sentences))
lemmatized = lemmatize_sents(
deepcopy(tags)) #Only for aesthetics reasons
chunker = RegexpParser("AC: {(<CD>?<TO|IN>?<CD>)+}\n "
"AN: {(<NPP>+<DT|NPP|JJ>*)+}\n "
"}<DT>+{\n "
"PH: {<[B-Z]+>+}\n "
"}<DT|CC|PRP|EX|WDT>+{")
chunked = list(chunker.parse_sents(lemmatized))
droped = setup_search_structure(chunked, tuple)
if (True):
num_print = input("Full data of:[None] ")
if (num_print):
num_print = int(num_print)
print()
for num_print in range(num_print, num_print + 10):
print(sentences[num_print])
print()
print(tags[num_print])
print()
print(lemmatized[num_print])
print()
#chunks = ne_chunk_sents(tags)
#iob = [tree2conlltags(chunk) for chunk in chunks]
#iob = tree2conlltags(chunks)
#print(iob[num_print])
#print()
#tree = [conlltags2tree(i) for i in iob]
#print(tree[num_print])
#print()
#"NP: {<IN|TO>?((<IN>?<DT>?<JJ.?>*<CD>?<NN.*>+<POS>?)+<CD>?<FW>?)+}\n "
#"VP: {((<WP>?<PRP>?<MD>?<VB.?>?<JJ>*<TO>?<VB.?>+<RB>*(<JJ>*<TO>?)*)+<CC>?)+}\n "
print(chunked[num_print])
print("\n###\n")
print(droped[0][num_print])
print()
if (input(f"({num_print}) ?> ")): break
### Search params
to_search = input("Search: ") or "work"
tag = {
'1': 'n',
'2': 'v',
'3': 'a',
'4': 'r'
}.get(
input(f"\nWhat '{to_search}'?\n"
"[1]: Noun\n"
"[2]: Verb\n"
"[3]: Adjective\n"
"[4]: Adverb\n\n"
"> "), None)
syn = 'y' in input("\nFind related words too? ").lower()
exact = 'y' in input("\nFind exact word? ").lower()
print()
_, ph_num_ls, sentences = analize_text(text, exact_words=exact)
num = 1000000
num2 = 10
if (to_search):
if (syn):
w_rel = words_related(to_search, tag)
else:
w_rel = to_search
ph_nums = find(w_rel, ph_num_ls)
print()
if (not len(ph_nums)):
print(f"{to_search} not in text.")
exit(0)
if (False):
print(f"Looking for \"{to_search}\" {num} times...\n")
print(timeit.timeit("find(w_rel, ph_num_ls)",
number=num,
globals={
**globals(),
**locals()
}),
end=' seconds\n\n')
if (False):
print(f"{num2} times text setup...\n")
print(timeit.timeit("analize_text(text)",
number=num2,
globals={
**globals(),
**locals()
}),
end=' seconds \n')
if ("y" in input("Show found instances?[No] ")):
from colorama import init as color_init
color_init()
print()
if (not ph_nums is None): # Unnecessary, but clean
for ph in ph_nums:
print(_color_sent(sentences[ph], w_rel))
print()
else:
print("You did not specify any search param")
