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from flask import Flask, render_template, request
import random
import json
from keras.models import load_model
import numpy as np
import pickle
from nltk.stem import WordNetLemmatizer
import nltk
nltk.download('popular')
lemmatizer = WordNetLemmatizer()
model = load_model('model.h5')
intents = json.loads(open('data.json').read())
words = pickle.load(open('texts.pkl', 'rb'))
classes = pickle.load(open('labels.pkl', 'rb'))
def clean_up_sentence(sentence):
# tokenize the pattern - split words into array
sentence_words = nltk.word_tokenize(sentence)
# stem each word - create short form for word
sentence_words = [lemmatizer.lemmatize(
word.lower()) for word in sentence_words]
return sentence_words
# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def bow(sentence, words, show_details=True):
# tokenize the pattern
sentence_words = clean_up_sentence(sentence)
# bag of words - matrix of N words, vocabulary matrix
bag = [0]*len(words)
for s in sentence_words:
for i, w in enumerate(words):
if w == s:
# assign 1 if current word is in the vocabulary position
bag[i] = 1
if show_details:
print("found in bag: %s" % w)
return (np.array(bag))
def predict_class(sentence, model):
# filter out predictions below a threshold
p = bow(sentence, words, show_details=False)
res = model.predict(np.array([p]))[0]
ERROR_THRESHOLD = 0.25
results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
# sort by strength of probability
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
for r in results:
return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
return return_list
def getResponse(ints, intents_json):
tag = ints[0]['intent']
list_of_intents = intents_json['intents']
for i in list_of_intents:
if (i['tag'] == tag):
result = random.choice(i['responses'])
break
return result
def chatbot_response(msg):
ints = predict_class(msg, model)
res = getResponse(ints, intents)
return res
app = Flask(__name__)
app.static_folder = 'static'
@app.route("/")
def home():
return render_template("index.html")
@app.route("/get")
def get_bot_response():
userText = request.args.get('msg')
return chatbot_response(userText)
# if __name__ == "__main__":
# app.run()
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