tensorflowProject/Chatbot.py at main · Mandeep0402/tensorflowProject · GitHub

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# -*- coding: utf-8 -*-
"""Untitled3.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/14rgoITZsQscz3bJ3iUksOdKt05OJuFbH
"""

!pip install tflearn

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

import tensorflow as tf
import numpy as np
import tflearn
import random
import json

from google.colab import files
files.upload()

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

words = []
classes = []
documents = []
ignore = ['?']

for intent in intents['intents']:
    for pattern in intent['patterns']:
        w = nltk.word_tokenize(pattern)
        words.extend(w)
        documents.append((w, intent['tag']))
        if intent['tag'] not in classes:
            classes.append(intent['tag'])

words = [stemmer.stem(w.lower()) for w in words if w not in ignore]
words = sorted(list(set(words)))

classes = sorted(list(set(classes)))

print (len(documents), "documents")
print (len(classes), "classes", classes)
print (len(words), "unique stemmed words", words)

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

for doc in documents:
    bag = []
    pattern_words = doc[0]
    pattern_words = [stemmer.stem(word.lower()) for word in pattern_words]
    for w in words:
        bag.append(1) if w in pattern_words else bag.append(0)

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

    training.append([bag, output_row])

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

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

from tensorflow.python.framework import ops
ops.reset_default_graph()

net = tflearn.input_data(shape=[None, len(train_x[0])])
net = tflearn.fully_connected(net, 10)
net = tflearn.fully_connected(net, 10)
net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
net = tflearn.regression(net)

model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')

model.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True)
model.save('model.tflearn')

import pickle
pickle.dump( {'words':words, 'classes':classes, 'train_x':train_x, 'train_y':train_y}, open( "training_data", "wb" ) )

data = pickle.load( open( "training_data", "rb" ) )
words = data['words']
classes = data['classes']
train_x = data['train_x']
train_y = data['train_y']

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

model.load('./model.tflearn')

def clean_up_sentence(sentence):
    sentence_words = nltk.word_tokenize(sentence)
    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
    return sentence_words

def bow(sentence, words, show_details=False):
    sentence_words = clean_up_sentence(sentence)
    bag = [0]*len(words)
    for s in sentence_words:
        for i,w in enumerate(words):
            if w == s:
                bag[i] = 1
                if show_details:
                    print ("found in bag: %s" % w)

    return(np.array(bag))

ERROR_THRESHOLD = 0.30
def classify(sentence):
    results = model.predict([bow(sentence, words)])[0]
    results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append((classes[r[0]], r[1]))
    return return_list

def response(sentence, userID='123', show_details=False):
    results = classify(sentence)
    if results:
        while results:
            for i in intents['intents']:
                if i['tag'] == results[0][0]:
                    return print("CovBot :",random.choice(i['responses']))

            results.pop(0)

flag=True
print("Hey, my name is CovBot, welcome to Chatbot. I will answer your queries. If you want to exit, type Bye!")
while(flag==True):
    user_response = input()
    user_response=user_response.lower()
    if(user_response!='bye'):
        if(user_response=='thanks' or user_response=='thank you' ):
            flag=False
            print("CovBot : You are welcome..")
        else:
            if(response(user_response)!=None):
                print("CovBot :",response(user_response))
    else:
        flag=False
        print("CovBot : Bye! take care..")