Neuralink_code/processor.py at main · kwamena98/Neuralink_code · GitHub

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from os import access
import random
from threading import active_count
from nltk import tag
from nltk.parse.nonprojectivedependencyparser import DemoScorer
from nltk.sem.relextract import descape_entity
import numpy as np
import json
import pickle
import nltk

from nltk.stem import WordNetLemmatizer
from tensorflow.keras import models
from tensorflow.keras import optimizers

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD
from tensorflow.python.keras import activations
from tensorflow.python.keras.engine import training
from tensorflow.python.keras.feature_column.dense_features_v2 import DenseFeatures


lemmatizer=WordNetLemmatizer()
intents= json.loads(open("intents.json").read())

words=[]
classes=[]
documents=[]

ignore_letters=['?','!','.',',']

for intent in intents['intents']:
    for pattern in intent['patterns']:
        word_list=nltk.word_tokenize(pattern)
        words.extend(word_list)
        documents.append((word_list, intent['tag']))

        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("words.pkl",'wb'))
pickle.dump(classes,open("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])

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(lr=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('chat_bot.model',hist)

print("done")