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text_generator
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from flask import Flask, render_template, request, jsonify, Response
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import load_model
import json
import tensorflow as tf

np.random.seed(42)
tf.random.set_seed(42)

lstm = load_model("model/model_lstm.h5")
bilstm = load_model("model/model_bilstm.h5")
gru = load_model("model/model_gru.h5")

app = Flask(__name__)

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

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

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

#option 1
@app.route('/classify', methods=['GET', 'POST'])
def classify():
    photo_x = "<svg xmlns='http://www.w3.org/2000/svg' fill='currentColor' class='bi bi-x larger-icon x' viewBox='0 0 16 16'><path d='M4.646 4.646a.5.5 0 0 1 .708 0L8 7.293l2.646-2.647a.5.5 0 0 1 .708.708L8.707 8l2.647 2.646a.5.5 0 0 1-.708.708L8 8.707l-2.646 2.647a.5.5 0 0 1-.708-.708L7.293 8 4.646 5.354a.5.5 0 0 1 0-.708z'/></svg>"
    photo_question_mark = "<svg xmlns='http://www.w3.org/2000/svg'fill='currentColor' class='bi bi-question-circle larger-icon questions' viewBox='0 0 16 16'><path d='M8 15A7 7 0 1 1 8 1a7 7 0 0 1 0 14zm0 1A8 8 0 1 0 8 0a8 8 0 0 0 0 16z'/><path d='M5.255 5.786a.237.237 0 0 0 .241.247h.825c.138 0 .248-.113.266-.25.09-.656.54-1.134 1.342-1.134.686 0 1.314.343 1.314 1.168 0 .635-.374.927-.965 1.371-.673.489-1.206 1.06-1.168 1.987l.003.217a.25.25 0 0 0 .25.246h.811a.25.25 0 0 0 .25-.25v-.105c0-.718.273-.927 1.01-1.486.609-.463 1.244-.977 1.244-2.056 0-1.511-1.276-2.241-2.673-2.241-1.267 0-2.655.59-2.75 2.286zm1.557 5.763c0 .533.425.927 1.01.927.609 0 1.028-.394 1.028-.927 0-.552-.42-.94-1.029-.94-.584 0-1.009.388-1.009.94z'/></svg>"
    photo_check = "<svg xmlns='http://www.w3.org/2000/svg' fill='currentColor' class='bi bi-check larger-icon check' viewBox='0 0 16 16'><path d='M10.97 4.97a.75.75 0 0 1 1.07 1.05l-3.99 4.99a.75.75 0 0 1-1.08.02L4.324 8.384a.75.75 0 1 1 1.06-1.06l2.094 2.093 3.473-4.425a.267.267 0 0 1 .02-.022z'/></svg>"
    try:
        text_ = request.form.get('text')
        
        file = "model/model_classification.h5"
        loaded_model = load_model(file)
        print(1)
    except:
        return render_template('classify.html', prediction_text="None", text="None", prediction_photo=photo_question_mark)
    

    try:
        new_data = [text_]
        tokenizer = Tokenizer(num_words=100, oov_token = "<OOV>")
        tokenizer.fit_on_texts(new_data)

        new_sequences = tokenizer.texts_to_sequences(new_data)
        new_padded = pad_sequences(new_sequences, maxlen=100, padding="post", truncating="post")

        predictions = loaded_model.predict(new_padded)
        predict_labels = [1 if values>0.5 else 0 for values in predictions]
        
        res = "Positive" if predict_labels[0]==1 else "Negative"
        icon = photo_check if predict_labels[0]==1 else photo_x

        return render_template('classify.html', prediction_text=res, text=text_, prediction_photo=icon)
    except ValueError:
        return render_template('classify.html', prediction_text='INVALID INPUT', text=text_, prediction_photo=photo_question_mark)
    except:
        return render_template('classify.html', prediction_text="None", text="None", prediction_photo=photo_question_mark)

@app.route('/generate', methods=['GET', 'POST'])
def generate():
    text_ = request.form.get('input')
    
    if text_ == None:
       return render_template('generate.html' ,res_lstm="",res_bilstm="",res_gru="")
        
    
    try:
        txt_lstm = predict(text_,5,lstm)
        txt_bilstm = predict(text_,5,bilstm)
        txt_gru = predict(text_,5,gru)
        
        return render_template('generate.html' ,res_lstm=txt_lstm,res_bilstm=txt_bilstm,res_gru=txt_gru)
    except :
        return render_template('generate.html' ,res_lstm="Something went wrong",res_bilstm="Something went wrong",res_gru="Something went wrong")

def predict(seed_text,next_words,model):
    #return "Detect change sleep deformed which worse it time all house in philippines"
    with open("static/dictionary.json", "r") as file:
        loaded_dictionary = json.load(file)

    with open('static/tokenizer.json', 'r') as json_file:
        tokenizer_json = json_file.read()
        tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_json)
    
    for _ in range(next_words):
        tokenizer.word_index = loaded_dictionary
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        token_list = pad_sequences([token_list], maxlen=51-1, padding='pre')
        
        predicted = np.argmax(model.predict(token_list), axis=-1)
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                seed_text += " " + output_word

    return seed_text

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