app.py

import random
import numpy as np
import pickle
import json
import os
from flask import Flask, render_template, request
from flask_ngrok import run_with_ngrok
import nltk
from keras.models import load_model
from nltk.stem import WordNetLemmatizer
print('imports are done')
lemmatizer = WordNetLemmatizer()
print('lemmatizer is inititntintintitn')

# dirname = os.path.dirname(__file__)
# data_filename = os.path.join(dirname, 'intents.json')

# file path inits because my fp system is broken
# ----------------------------------------------
dirname = os.path.dirname(__file__)  # This can be used throughout the file
modelpath = os.path.join(dirname, 'chatbot_model.h5')
intentspath = os.path.join(dirname, 'intents.json')
wordspath = os.path.join(dirname, 'words.pkl')
classespath = os.path.join(dirname, 'classes.pkl')
# ----------------------------------------------

# init chat
model = load_model(modelpath)
intents = json.loads(open(intentspath).read())
words = pickle.load(open(wordspath, "rb"))
classes = pickle.load(open(classespath, "rb"))

app = Flask(__name__)
# run_with_ngrok(app) -Use this option if you have ngrok and you want to expose your chatbot to the real world


# app = Flask(__name__)
# # run_with_ngrok(app) 

@app.route("/")
def home():
    return render_template("index.html")


@app.route("/get", methods=["POST"])
def chatbot_response():
    msg = request.form["msg"]
    if msg.startswith('my name is'):
        name = msg[11:]
        ints = predict_class(msg, model)
        res1 = getResponse(ints, intents)
        return res1.replace("{n}",name)
    elif msg.startswith('hi my name is'):
        name = msg[14:]
        ints = predict_class(msg, model)
        res1 = getResponse(ints, intents)
        return res1.replace("{n}",name)
    else:
        ints = predict_class(msg, model)
        return getResponse(ints, intents)


# chat functionalities
def clean_up_sentence(sentence):
    sentence_words = nltk.word_tokenize(sentence)
    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 [{"intent": classes[r[0]], "probability": str(r[1])} for r in results]


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


if __name__ == "__main__":
    app.run()