app.py

from flask import Flask, render_template,request
from tensorflow.keras.preprocessing.sequence import pad_sequences
import tensorflow as tf
import pickle
import os
import sys
import re
import string




def predict(new_metin):

    new_sayi = 40	
    model = tf.keras.models.load_model("saved_model.h5")
    with open('tokenizer.pickle', 'rb') as handle: 
        tokenizer = pickle.load(handle)


    max_sequence_len =143

    for x in range(new_sayi):

        token_list = tokenizer.texts_to_sequences([new_metin])[0]
        token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
        predicted = model.predict_classes(token_list, verbose=0)

        output_word = ""
        for word,index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                break

        new_metin += " "+output_word

    return new_metin
    
    
    
app = Flask(__name__)




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


@app.route('/output', methods =["POST"]) 
def output(): 
	if request.method == "POST":		       
		user_input = request.form.get("start-poem")
		if user_input != "":

			lower = str.maketrans("ABCÇDEFGĞHIİJKLMNOÖPRŞSTUÜVYZ", "abcçdefgğhıijklmnoöprşstuüvyz")
			user_input = user_input.translate(lower)
			user_input = predict(user_input.translate(str.maketrans('', '', string.punctuation)))
			
		else: user_input = "Lütfen bir önceki sayfaya gidip, bir cümle giriniz."
		return render_template("output.html", output = user_input)
   






if __name__ == '__main__':
   app.run(debug=True)