Проблема с индексацией при работе с ИИ

import tensorflow as tf
import pandas as pd
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Embedding, Flatten
 
dataset = pd.read_csv('pythonProject\porttfolio\dataset.csv') 
 
# 2. Предварительно обработать данные:
 
inputs = dataset['Input']
labels = dataset['Diagnosis']
 
sequences = []
def add_blank_spaces(sequence, target_length):
    while len(sequence) < target_length:
        sequence.append('')
    
    return sequence
 
 
for seq in inputs:
    seq_list = add_blank_spaces(list(seq.split(' ')), 100)
    sequences.append(seq_list) 
    
tokenizer = Tokenizer()
tokenizer.fit_on_texts(inputs)
sequences = tokenizer.texts_to_sequences(inputs)
data = pad_sequences(sequences, maxlen=100)
 
labels = to_categorical(labels)
 
print(len(tokenizer.word_index))
# 3. Создать модель нейронной сети:
 
model = Sequential()
model.add(Embedding(input_dim=len(tokenizer.word_index)+1, input_length=115, output_dim=9))
model.add(Flatten())
model.add(Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.01)))
model.add(Dense(7, activation='softmax'))
 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
 
model.summary()
 
# 4. Обучить модель и сохранить ее для дальнейшего использования:
 
 
model.fit(data, labels, epochs=100, batch_size=8)
 
model.save('neural_disease_recognition.krs')

y = y.reshape(-1)
if not num_classes:
    num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical