from flask import Flask, render_template, request, redirect, url_for, session, flash
from flask import Flask, render_template, request, session, jsonify
from flask_mysqldb import MySQL
from keras.preprocessing import image as keras_image
from tensorflow.keras.applications.efficientnet import preprocess_input as efficientnet_preprocess_input
import tensorflow as tf
import bcrypt
import io
import os
import base64
import cv2
from flask import jsonify
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
from PIL import Image
from werkzeug.utils import secure_filename

app = Flask(__name__)
app.secret_key = "webarab"

mysql = MySQL(app)

# load
model = load_model('model70epoch2.h5')

# memeriksa ekstensi file
def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

UPLOAD_FOLDER = 'static/upload/'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif', 'tiff', 'webp', 'jfif'}

@app.route('/')
def index():
    return redirect(url_for('start'))

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

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

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

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

@app.route('/materi1')
def materi1():
    return render_template('materi1.html')

@app.route('/materi2')
def materi2():
    return render_template('materi2.html')

@app.route('/materi3')
def materi3():
    return render_template('materi3.html')

@app.route('/nextpage1')
def nextpage1():
    return render_template('nextpage1.html')

@app.route('/nextpage2')
def nextpage2():
    return render_template('nextpage2.html')

@app.route('/nextpage3')
def nextpage3():
    return render_template('nextpage3.html')

@app.route('/nextpagee1')
def nextpagee1():
    return render_template('nextpagee1.html')

@app.route('/nextpagee2')
def nextpagee2():
    return render_template('nextpagee2.html')

@app.route('/nextpagee3')
def nextpagee3():
    return render_template('nextpagee3.html')

@app.route('/nextpageee3')
def nextpageee3():
    return render_template('nextpageee3.html')

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

@app.route('/quizz2')
def quizz2():
    return render_template('quizz2.html')

@app.route('/quizz3')
def quizz3():
    return render_template('quizz3.html')

@app.route('/quizz4')
def quizz4():
    return render_template('quizz4.html')

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

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

@app.route('/materipindai2')
def materipindai2():
    return render_template('materipindai2.html')

@app.route('/materipindai3')
def materipindai3():
    return render_template('materipindai3.html')

@app.route('/pindai', methods=['GET', 'POST'])
def pindai():
    return render_template('pindai.html', judul='Pindai')

#untuk mengirimkan dan memproses gambar
@app.route('/submit', methods=['POST', 'GET'])
def predict():
    if request.method == 'GET':
        return redirect(url_for('pindai')) 

    if 'file' not in request.files:
        flash('Tidak ada gambar dalam permintaan', 'error')
        return redirect(request.url)

    file = request.files['file']
    if file.filename == '':
        flash('Tidak ada file yang dipilih', 'error')
        return redirect(request.url)

    if file and allowed_file(file.filename):
        filename = secure_filename(file.filename)
        file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(file_path)

        img = Image.open(file_path).convert('RGB')
        
        #preprocessing
        img = keras_image.load_img(file_path, target_size=(224, 224))  
        x = keras_image.img_to_array(img) #mengubah ke array
        x = np.expand_dims(x, axis=0) #dimensi tambahan di sumbu pertama untuk mempersiapkan gambar sebagai input batch untuk model
        x = efficientnet_preprocess_input(x)  #khusus efficient

        # menyimpan gambar
        processed_image_path = os.path.join(app.config['UPLOAD_FOLDER'], 'processed_image.png')
        img.save(processed_image_path)

        # mempersiapkan gambar ini untuk penggunaan dalam prediksi model
        img = keras_image.load_img(processed_image_path, target_size=(224, 224))
        x = keras_image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        images = np.vstack([x])

        # prediksi kelas yang telah diproses
        prediction_arab = model.predict(images)

        # mapping class
        class_names = ['الساعة', 'السباحة', 'حديقة', 'حذاء', 'سور', 'قلم', 
        'قَاعَة', 'كرة سلة', 'مصنع', 'ملابس', 'ملعب', 'مَكْتَبُ المُدَرِّسِيْن', 'مِرْحَاض', 'مِسْطَرَة', 'مِنْضَدَة']
        predicted_class_index = np.argmax(prediction_arab)
        predicted_class_name = class_names[predicted_class_index]

        # Return the prediction result to the web page
        return render_template("pindai.html", img_path=file_path,
                       predictionarab=predicted_class_name,
                       confidencearab='{:.2%}'.format(np.max(prediction_arab)))

    else:
        flash('Format file tidak diizinkan', 'error')
        return redirect(request.url)

@app.route('/refresh', methods=['GET', 'POST'])
def refresh():
    return redirect(url_for('pindai'))

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