tester.py

# -*- coding: utf-8 -*-
"""Tester.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1PCPVQB72jy4biMy9D-d6kXdAUaSF1Fvx
"""

import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np

# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

# Load the saved model
model = tf.keras.models.load_model('/content/drive/MyDrive/detector-model.h5')

class_names = ['non-batagor', 'batagor']

def predict_image(image_path):
    img = image.load_img(image_path, target_size=(256, 256))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img /= 255.

    prediction = model.predict(img)
    predicted_class = np.argmax(prediction[0])
    class_label = class_names[predicted_class]
    confidence = prediction[0][predicted_class] * 100

    print("Predicted class:", class_label)
    print("Confidence: {:.3f} %\n\n".format(confidence))
    if class_label == "non-batagor":
      if confidence < 26:
        print("Batagor")
      else:
        print("Bukan Batagor")
    elif class_label == "batagor":
      if confidence > 26:
        print("Bukan Batagor")
      else:
        print("Batagor")

# Path gambar yang ingin diprediksi
image_path = "/content/batagor-udang.jpg"

# Melakukan prediksi pada gambar
prediction_result = predict_image(image_path)

# Menampilkan hasil prediksi
# print(prediction_result)