from tensorflow.keras.models import load_model, Sequential, Model
from models import Models
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score
from text_preprocessing import TextPreprocessing
import json
import os
from csv_reader import CSVReader
from datetime import datetime
from tensorflow.keras import backend as K
from sklearn.metrics import classification_report
textPreProcessing = TextPreprocessing()
if __name__ == "__main__":
embeddings = np.load('text_embedding.npy', allow_pickle=True)
texts = []
sentiments = []
good = CSVReader.dataframe_from_txt("WordNetAffectEmotionLists/joy.txt")
good = good.texts
texts = texts = np.append(texts, np.array(good))
sentiments = np.append(sentiments, [1] * len(good))
surprise = CSVReader.dataframe_from_txt(
"WordNetAffectEmotionLists/surprise.txt")
surprise = surprise.texts
texts = np.append(texts, np.array(surprise))
sentiments = np.append(sentiments, [2] * len(surprise))
sad = CSVReader.dataframe_from_txt("WordNetAffectEmotionLists/sadness.txt")
sad = sad.texts
texts = np.append(texts, np.array(sad))
sentiments = np.append(sentiments, [-1] * len(sad))
fear = CSVReader.dataframe_from_txt("WordNetAffectEmotionLists/fear.txt")
fear = fear.texts
texts = np.append(texts, np.array(fear))
