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flaskr.py
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flaskr.py
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from flask import Flask, request, send_from_directory, redirect, render_template, flash, url_for, jsonify, \
make_response, abort
# things we need for NLP
import nltk
from nltk.stem.snowball import SnowballStemmer
stemmer = SnowballStemmer('english')
# things we need for Tensorflow
import numpy as np
import tflearn
import tensorflow as tf
import random
import datetime
currentTime = datetime.datetime.now()
# restore all of our data structures
import pickle
data = pickle.load( open( "/home/kusuma/PycharmProjects/bot/training_data", "rb" ) )
words = data['words']
classes = data['classes']
train_x = data['train_x']
train_y = data['train_y']
# import our chat-bot intents file
import json
with open('/home/kusuma/Desktop/status/ks.json') as json_data:
intents = json.load(json_data)
# Build neural network
net = tflearn.input_data(shape=[None, len(train_x[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
net = tflearn.regression(net)
# Define model and setup tensorboard
model = tflearn.DNN(net, tensorboard_dir='tflearn_logs',checkpoint_path=None)
def clean_up_sentence(sentence):
# tokenize the pattern
sentence_words = nltk.word_tokenize(sentence)
# stem each word
sentence_words = [stemmer.stem(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=False):
# tokenize the pattern
sentence_words = clean_up_sentence(sentence)
# bag of words
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))
# load our saved model
model.load('/home/kusuma/PycharmProjects/contextualbot/model/model.tflearn')
# create a data structure to hold user context
context = {}
ERROR_THRESHOLD = 0.25
def classify(sentence):
# generate probabilities from the model
results = model.predict([bow(sentence, words)])[0]
# filter out predictions below a threshold
results = [[i,r] for i,r in enumerate(results) 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((classes[r[0]], r[1]))
# return tuple of intent and probability
return return_list
if currentTime.hour < 12:
print('Good morning.how may help you')
elif 12 <= currentTime.hour < 18:
print('Good afternoon.how may help you')
else:
print('Good evening.how may help you')
def response(sentence, userID='123', show_details=False):
results = classify(sentence)
out=''
# if we have a classification then find the matching intent tag
if results:
# loop as long as there are matches to process
while results:
for i in intents['intents']:
# find a tag matching the first result
if i['tag'] == results[0][0]:
# set context for this intent if necessary
if 'context_set' in i:
if show_details: print ('context:', i['context_set'])
context[userID] = i['context_set']
# check if this intent is contextual and applies to this user's conversation
if not 'context_filter' in i or \
(userID in context and 'context_filter' in i and i['context_filter'] == context[userID]):
if show_details: print ('tag:', i['tag'])
# a random response from the intent
out=''+random.choice(i['responses'])
results.pop(0)
return out
'''ipk = raw_input("ask your question: ")
while (ipk != "bye"):
response(ipk)
ipk = raw_input("ask your question: ")'''
app = Flask(__name__)
app.config.from_object(__name__) # load config from this file , flaskr.py
# Load default config and override config from an environment variable
app.config.from_envvar('FLASKR_SETTINGS', silent=True)
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024
banking_bot_conversations = []
@app.route('/')
def home():
return render_template('home.html')
@app.route('/about')
def about():
return 'About Us'
@app.route('/chatbot_reply', methods=['POST', 'GET'])
def chatbot_reply():
if request.method == 'POST':
if 'sentence' not in request.form:
flash('No sentence post')
redirect(request.url)
elif request.form['sentence'] == '':
flash('No sentence')
redirect(request.url)
else:
sent = request.form['sentence']
banking_bot_conversations.append('YOU: ' + sent)
reply = response(sent)
banking_bot_conversations.append('BOT: ' + reply)
return render_template('chatbot_reply.html', conversations=banking_bot_conversations)
@app.errorhandler(404)
def not_found(error):
return make_response(jsonify({'error': 'Not found'}), 404)
def main():
app.secret_key = 'super secret key'
app.config['SESSION_TYPE'] = 'filesystem'
app.run(debug=True,port=8000)
if __name__ == '__main__':
main()