trainPy.py

import nltk
from nltk.stem.lancaster import LancasterStemmer
import numpy as np
import tensorflow as tf
import random
import json
import sys
import pickle

stemmer = LancasterStemmer()

jsonFiles = ["intents.json", "loanIntents.json"]
pickleFiles = ["data.pickle", "data.pickle.loan"]
models = ["modelClass", "modelLoan"]

for i in range(0, len(jsonFiles)):

    with open(jsonFiles[i]) as file:
        data = json.load(file)

    # print("This works just fine dont worry")
    try:
        #If you add something in the intents.json file then deliberately create an error here
        x
        with open(pickleFiles[i], "rb") as f:
            words, labels, training, output = pickle.load(f)
    except:
        words = []
        labels = []
        docs_x = []
        docs_y = []

        for intent in data["intents"]:
            for pattern in intent["patterns"]:
                wrds = nltk.word_tokenize(pattern)
                words.extend(wrds)
                docs_x.append(wrds)
                docs_y.append(intent["tag"])

            if(intent["tag"] not in labels):
                labels.append(intent["tag"])


        words = [stemmer.stem(w.lower()) for w in words]
        words = sorted(list(set(words)))

        labels = sorted(labels)

        training = []
        output = []

        out_empty = [0 for _ in range(len(labels))]

        for x, doc in enumerate(docs_x):
            bag = []
            wrds = [stemmer.stem(w) for w in doc if w != "?"]

            #One hot encoding or something similar sounding
            for w in words:
                if(w in wrds):
                    bag.append(1)
                else:
                    bag.append(0)

            output_row = out_empty[:]
            output_row[labels.index(docs_y[x])] = 1

            training.append(bag)
            output.append(output_row)

        training = np.array(training)
        output = np.array(output)

        with open(pickleFiles[i], "wb") as f:
            pickle.dump((words, labels, training, output), f)


    model = tf.keras.Sequential([
        tf.keras.layers.Dense(8, input_shape=[len(training[0])]),
        tf.keras.layers.Dense(8),
        tf.keras.layers.Dense(len(output[0]), activation="softmax"),
    ])

    model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])

    model.fit(training, output, epochs=300, batch_size=8)
    model.save(models[i] + ".h5")