145 lines
3.7 KiB
Python
145 lines
3.7 KiB
Python
from flask import Flask, render_template, request, jsonify
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from sklearn.metrics import accuracy_score
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing import image
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import numpy as np
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import base64
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import io
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import uuid
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import tempfile
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import os
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app = Flask(__name__)
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dic = {0: 'GIANT', 1: 'LEUKOSIT', 2: 'NORMAL'}
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# with custom_object_scope({'error_rate': error_rate}):
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# model = load_model('static/assets/models/Klasifikasi-Et Exception-96.92.h5')
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def error_rate(y_true, y_pred):
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incorrect = tf.not_equal(tf.argmax(y_true, axis=-1), tf.argmax(y_pred, axis=-1))
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return tf.reduce_mean(tf.cast(incorrect, tf.float32))
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def load_custom_model(model_path):
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with tf.keras.utils.custom_object_scope({'error_rate': error_rate}):
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model = load_model(model_path)
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return model
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model_path = 'static/assets/models/Klasifikasi-Et Exception-97.69.h5'
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loaded_model = load_custom_model(model_path)
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def predict_label(img_path):
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i = image.load_img(img_path, target_size=(224, 224))
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i = image.img_to_array(i) / 255.0
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i = i.reshape(1, 224, 224, 3)
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p = loaded_model.predict(i)
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predicted_class = np.argmax(p, axis=-1)
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highest_probability = max(p[0]) * 100
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probabilities = (p[0] * 100).tolist()
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print(probabilities)
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other_probabilities = {}
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for index, prob in enumerate(probabilities):
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if index != predicted_class[0]:
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other_probabilities[dic[index]] = prob
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return {
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'label': dic[predicted_class[0]],
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'highest_probability': highest_probability,
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'other_probabilities': other_probabilities
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}
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@app.route("/", methods=['GET'])
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def main():
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return render_template("index.html")
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@app.route("/giant", methods=['GET'])
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def giant():
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return render_template("giant.html")
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@app.route("/leukosit", methods=['GET'])
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def leukosit():
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return render_template("leukosit.html")
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@app.route("/normal", methods=['GET'])
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def normal():
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return render_template("normal.html")
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@app.route("/classification", methods=['GET', 'POST'])
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def classification():
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temp_dir = "static/assets/temp/"
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try:
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for filename in os.listdir(temp_dir):
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file_path = os.path.join(temp_dir, filename)
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if os.path.isfile(file_path):
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os.remove(file_path)
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# Setelah menghapus gambar, tampilkan halaman classification.html
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return render_template("classification.html")
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except Exception as e:
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# Tangani kesalahan jika terjadi
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return f'Terjadi kesalahan: {str(e)}'
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@app.route("/submit", methods=['POST'])
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def get_output():
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if request.method == 'POST':
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base64data = request.form['image']
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img_data = base64.b64decode(base64data)
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img_filename = str(uuid.uuid4()) + ".jpg"
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img_path = "static/assets/temp/" + img_filename
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with open(img_path, "wb") as f:
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f.write(img_data)
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result = predict_label(img_path)
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return jsonify(
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label=result['label'],
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highest_probability=result['highest_probability'],
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other_probabilities=result['other_probabilities'],
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img_path=img_path
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)
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@app.route("/delete_image", methods=['POST'])
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def delete_image():
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temp_dir = "static/assets/temp/"
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try:
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for filename in os.listdir(temp_dir):
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file_path = os.path.join(temp_dir, filename)
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if os.path.isfile(file_path):
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os.remove(file_path)
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return jsonify(success=True, message='Semua gambar berhasil dihapus.')
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except Exception as e:
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return jsonify(success=False, message=f'Terjadi kesalahan: {str(e)}')
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if __name__ == '__main__':
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app.run(debug=True)
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