main.py

import nltk
nltk.download('punkt')
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()

import numpy
import tflearn
import tensorflow
import random
import json
import pickle

with open('intents.json') as file:
    data = json.load(file)

try: 
    with open("data.pickle","rb") as f:
        words,labels,training,output=pickle.load(f)
except:
    words = []
    labels =[]
    docs =[]
    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 if w != "?"]
    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]

        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 = numpy.array(training)
    output = numpy.array(output)
    
    with open("data.pickle", "wb") as f:
        pickle.dump((words, labels, training, output), f)

tensorflow.compat.v1.reset_default_graph()  # get rid of previous settings and stuffs(underlying data graphs)

net = tflearn.input_data(shape=[None,len(training[0])])
net = tflearn.fully_connected(net,8)
net = tflearn.fully_connected(net,8)
net = tflearn.fully_connected(net,len(output[0]),activation="softmax")
net = tflearn.regression(net)

model = tflearn.DNN(net)

try:
    model.load("model.tflearn")
except:
    model.fit(training,output,n_epoch=1000,batch_size=8,show_metric=True)
    model.save("model.tflearn")

# - Get some input from the user 
# – Convert it to a bag of words 
# – Get a prediction from the model 
# – Find the most probable class 
# – Pick a response from that class

# The bag_of_words function will transform our string input to a bag of words using our created words list.
# The chat function will handle getting a prediction from the model and grabbing an appropriate response from our JSON file of responses.

def bag_of_words(s,words):
    bag=[0 for _ in range(len(words))]

    s_words = nltk.word_tokenize(s)
    s_words = [stemmer.stem(word.lower()) for word in s_words]

    for se in s_words:
        for i,w in enumerate(words):
            if w == se:
                bag[i] = 1

    return numpy.array(bag)

def chat():
    print("Start talking with the bot (type quit to stop)!")
    while True:
        inp = input("You: ")
        if inp.lower() == "quit":
            break
        
        results = model.predict([bag_of_words(inp,words)])[0]
        results_index = numpy.argmax(results)
        tag = labels[results_index]

        if results[results_index] > 0.7:
            for tg in data["intents"]:
                if tg["tag"] == tag:
                    responses= tg['responses']
            print(random.choice(responses))
        else:
            print("I can not understand, try again")
chat()