app.py

from distutils.log import error
from flask import Flask, flash, request, redirect, url_for
from flask_cors import CORS, cross_origin

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

import numpy
import tflearn
import tensorflow as tf
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_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.lower()) 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.reset_default_graph()
tf.compat.v1.reset_default_graph()

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")

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(text):
    print("Start talking with the bot (type quit to stop)!")
    inp = text

    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']
    
        return(random.choice(responses))
    else:
        return("I didn't get that , try again.")    


app = Flask(__name__)
cors = CORS(app)

@app.route("/", methods=["GET","POST"])
def working():
    print(request)
    return "Working"
    


@app.route("/sendChat", methods=["POST"])
def hello_world():
    try:
        text=request.json
        print('text: ', text)
        # print(chat(text))
        result = chat(text['text'])
        res = {}
        res['result'] = result
        return res
    except(error):
        return error

@app.route("/texttoquestion", methods=["GET", "POST"])
def generate():
    if request.method == "POST":
        text = request.form.get('key')
        print(chat(text))
    return chat(text)