import pandas as pd

import numpy as np

from flask import Flask, request, render_template

import pickle

import nltk

from nltk.corpus import wordnet as wn 

from nltk.stem import WordNetLemmatizer 

from nltk.tokenize import word_tokenize

from nltk.corpus import stopwords

import string

import re

app = Flask(__name__)

model = pickle.load(open('bestmodel.pkl', 'rb'))

pca_model = pickle.load(open('word2vec.pkl', 'rb'))

w2v_model = pickle.load(open('w2v_model.pkl', 'rb'))

stopwords_list = set(stopwords.words("english"))

def input_process(data):

    input_clean = phrase_cleanse(data)

    w2v_model = pickle.load(open('w2v_model.pkl', 'rb'))

    input_clean = [input_clean.split(" ")]

    processed_input = word_avg_vect(input_clean, w2v_model, 100)

    pca_model = pickle.load(open('word2vec.pkl', 'rb')) 

    test = pca_model.transform(processed_input)

    return test

def phrase_cleanse(phrase):

    #Tokenize and divide phrase into separate words

    token_words = word_tokenize(phrase)

    # Convert all texts to lower cases

    words_step1 = []

    for word_1 in token_words:

        words_step1.append(word_1.lower())

    #Clear all punctuation

    words_step2 = [] 

    for word_2 in words_step1:

        word_cleaned = re.sub(r'[^\w\s]','',word_2)

        words_step2.append(word_cleaned)

    #Clean the text list

    words_step3 = []

    for word_3 in words_step2:

        # check if every characters are alphbets

        if word_3.isalpha():

            # get rid of stop words

            if word_3 not in list(stopwords_list):

                words_step3.append(word_3)

            else:

                continue

    #Lemmatization - group different forms of same word which has more than 2 characters into one word

    lem = nltk.stem.WordNetLemmatizer()

    lem_list = []

    for word_4 in words_step3:

        if(len(word_4) > 2):

            lem_list.append(lem.lemmatize(word_4))

    join_text = " ".join(lem_list)

    return join_text

def word_avg_vect(data, model, num_features):

    words = set(model.wv.index_to_key)

    X_vect = np.array([np.array([model.wv[i] for i in s if i in words]) for s in data])

    X_vect_avg = []

    for v in X_vect:

        if v.size:

            X_vect_avg.append(v.mean(axis = 0))

        else:

            X_vect_avg.append(np.zeros(num_features, dtype = float))

    df_vect_avg = pd.DataFrame(X_vect_avg)

    return df_vect_avg

@app.route('/')

def home():

    return render_template('index.html')

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

def predict():

    '''

    For rendering results on HTML GUI

    '''

    input =  request.form['medical diagonisis']

    final_features = input_process(input)

    prediction = model.predict(final_features)

    output = prediction[0]

    return render_template('index.html', prediction_text='Our predicted diagnosis is {}'.format(output))

if __name__ == "__main__":

    app.run(debug=True)