training.py

import random
import json
import pickle
import numpy as np

import nltk

from nltk.stem import WordNetLemmatizer

# type: ignore
from tensorflow.keras.models import Sequential
# type: ignore
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import SGD

# type: ignore
from repository import find_all_intents_for_train
from sqlalchemy.orm import Session


def train(db: Session):
    lemmatizer = WordNetLemmatizer()

    intents = find_all_intents_for_train(db)

    words = []
    classes = []
    documents = []
    ignore_letters = ['?', '!', '.', ',']

    for intent in intents:
        for pattern in intent.patterns:
            # tokenize = splits up sentences into words
            word_list = nltk.word_tokenize(pattern)
            words.extend(word_list)
            # (word_list) is a tuple. Tuple: stores multiple items into a single variable
            # (word_list) belongs to the category intent['tag']
            documents.append((word_list, intent.tag))

            # check if the class is in the classes list
            if intent.tag not in classes:
                classes.append(intent.tag)
    
    words = [lemmatizer.lemmatize(word)
             for word in words if word not in ignore_letters]
    words = sorted(set(words))

    classes = sorted(set(classes))

    pickle.dump(words, open('generated/words.pkl', 'wb'))
    pickle.dump(classes, open('generated/classes.pkl', 'wb'))

    training = []
    output_empty = [0] * len(classes)

    for document in documents:
        bag = []
        word_patterns = document[0]
        word_patterns = [lemmatizer.lemmatize(
            word.lower()) for word in word_patterns]
        for word in words:
            bag.append(1) if word in word_patterns else bag.append(0)

        output_row = list(output_empty)
        output_row[classes.index(document[1])] = 1

        training.append([bag, output_row])

    max_length = max(len(x) for x, y in training)

    for i, (x, y) in enumerate(training):
        padding_length = max_length - len(x)
        if padding_length > 0:
            x = x + [0] * padding_length
        padding_length = max_length - len(y)
        if padding_length > 0:
            y = y + [0] * padding_length
        # Update the training list with the padded elements
        training[i] = (x, y)

    random.shuffle(training)
    training = np.array(training)

    train_x = list(training[:, 0])
    train_y = list(training[:, 1])

    model = Sequential()
    model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(len(train_y[0]), activation='softmax'))

    sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

    hist = model.fit(np.array(train_x), np.array(train_y), epochs = 200, batch_size=5, verbose=1)
    model.save('generated/chatbotmodel.keras', hist)

    return {'message': 'Model trained successfully!'}