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import os
import sys
import pathlib
import spacy
import pandas as pd
from ast import literal_eval
from nltk.tokenize import sent_tokenize
folder_path = pathlib.Path().parent.resolve()
sys.path.append(os.path.join(folder_path, "../"))
from utils import load_subs
class NamedEntityRecognizer:
def __init__(self):
self.model = self.load_model()
def load_model(self):
nlp = spacy.load("en_core_web_trf")
return nlp
def get_chars_inference(self, script):
script_sents = sent_tokenize(script)
chars = []
for sent in script_sents:
doc = self.model(sent)
char = set()
for entity in doc.ents:
if entity.label_ == "PERSON":
name = entity.text.strip().split(" ")[0]
char.add(name)
chars.append(char)
return chars
def get_chars(self, dataset_path, save_path=None):
if save_path and not save_path.endswith(".csv"):
save_path += "chars.csv"
if save_path and os.path.exists(save_path):
df = pd.read_csv(save_path)
df["chars"] = df["chars"].apply(lambda x: literal_eval(x) if isinstance(x, str) else x)
return df
df = load_subs(dataset_path)
df["chars"] = df["script"].apply(self.get_chars_inference)
if save_path:
df.to_csv(save_path, index=False)
return df |