Untitled

from flask import Flask, request, render_template, jsonify
import tensorflow as tf
import numpy as np
import os
import json

from tensorflow import keras

# Keras
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.models import load_model
import keras.utils as image
from werkzeug.utils import secure_filename


# Define Flask App
app = Flask(__name__)

# load model
model_path = './models/model7.h5'
model = tf.keras.models.load_model(model_path)


def model_predict(img_path, model):

# image = tf.keras.preprocessing.image.load_img('/content/tugujogja.jpg', target_size=(150, 150))
image = tf.keras.preprocessing.image.load_img(img_path, target_size=(150, 150))
image_array = tf.keras.preprocessing.image.img_to_array(image)
image_array = image_array / 255.0 # Normalize pixel values between 0 and 1
input_data = tf.expand_dims(image_array, axis=0) # Add batch dimension
predictions = model.predict(input_data)

return predictions

# img = image.load_img(img_path, target_size=(150, 150))

# # Preprocessing the image
# x = image.img_to_array(img)
# x = np.expand_dims(x, axis=0)
# x = preprocess_input(x, mode='caffe')

# preds = model.predict(x)
# return preds


# Halaman Home
@app.route('/', methods=['GET'])
def home():
return render_template('index.html')


@app.route('/', methods=['POST'])
def predicts():
if request.method == 'POST':
# Get file from request
imageCamera = request.files['imageCamera']

# Save the file to ./uploads
basepath = os.path.dirname(__file__)
file_path = os.path.join(
basepath, 'images', secure_filename(imageCamera.filename))
imageCamera.save(file_path)

predictions = model_predict(file_path, model)

# Get the predicted class index with highest probability

test_labels = [
'borobudur',
'jendral_sudirman',
'martapura',
'monas',
'monumen_lobar',
'monumen mataram metro',
'monumen_selamat_datang',
'monumen_surabaya',
'museum_tsunami',
'pantai_penyu',
'prambanan',
'pura_suranadi',
'rumah_aceh',
'sarinah_ mall',
'taman_sangkreang',
'tugu_jogja',
]

pred_class_index = np.argmax(predictions)
result = test_labels[pred_class_index]

response = {"name": result}

return jsonify(response)

# # Decode predictions
# pred_class = decode_predictions(preds, top=1) # ImageNet Decode
# result = str(pred_class[0][0][1]) # Convert to string
# return result


@app.route('/predict', methods=['POST'])
def predict():
data = request.json['data']
prediction = model_predict(data, model)
return {'prediction': prediction.tolist()}


if __name__ == '__main__':
app.run(port=8080, debug=True)