from tensorflow.keras.models import load_model

from flask import Flask, jsonify, request

from flask_cors import CORS

import pickle

import numpy as np

import nltk

from nltk import LancasterStemmer

import pandas as pd

stemmer = LancasterStemmer()

app = Flask(__name__)

CORS(app)

model = load_model("chatbot_model.hdf5")

with open('labels.pkl', 'rb') as f:

    labels = pickle.load(f)

with open('words.pkl', 'rb') as f:

    words = pickle.load(f)

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=True):

    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))

@app.route('/')

def home_endpoint():

    return 'Hello there, welcome to Team Medic Minds'

@app.route('/api/predict', methods=['GET', 'POST'])

def classify():

    ERROR_THRESHOLD = 0.25

    sentence = request.json['sentence']

    input_data = pd.DataFrame([bow(sentence, words)], dtype=float, index=['input']).to_numpy()

    results = model.predict([input_data])[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({"intent": labels[r[0]], "probability": str(r[1])})

    response = jsonify(return_list)

    return response

if __name__ == '__main__':

    app.run(host='0.0.0.0', port=5000)

'''

def get_prediction():

    sentence = request.json['sentence']

    if flask.request.method == 'POST':

        data = flask.request.json  # Get data posted as a json

        if data == None:

            data = flask.request.args

        input = data.get('data')

        prediction = predictStringInput(chatbot_model,input)

    return prediction

'''