upload app.py

app.py

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+from flask import Flask, request, jsonify, render_template, redirect, url_for
+from keras.models import load_model
+from PIL import Image
+from keras.preprocessing import image as keras_image
+from PIL import Image
+import numpy as np
+from datetime import datetime
+import io
+import cv2
+from werkzeug.utils import secure_filename
+from PIL import ImageOps
+import os
+
+app = Flask(__name__)
+
+# Muat model dari file .h5
+model = load_model('model/klasifikasi-Parkinson-93.13.h5')
+
+UPLOAD_FOLDER = 'static/upload/'
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif', 'tiff', 'webp', 'jfif'}
+MIN_FILE_SIZE_KB = 20
+MAX_FILE_SIZE_MB = 5
+
+def allowed_file(filename):
+    return (
+        '.' in filename and 
+        filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS and 
+        request.content_length >= MIN_FILE_SIZE_KB * 1024 and
+        request.content_length <= MAX_FILE_SIZE_MB * 1024 * 1024
+    )
+
+@app.route('/', methods=['GET', 'POST'])
+def home():
+    return render_template('home.html', judul='Home')
+
+@app.route('/informasi/', methods=['GET', 'POST'])
+def informasi():
+    return render_template('informasi.html', judul='Informasi')
+
+@app.route('/klasifikasi', methods=['GET', 'POST'])
+def klasifikasi():
+    return render_template('klasifikasi.html', judul='Klasifikasi')
+
+@app.route('/submit', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        resp = jsonify({'message': 'No image in the request'})
+        resp.status_code = 400
+        return resp
+
+    files = request.files.getlist('file')
+    filename = "temp_image.png"
+    errors = {}
+    success = False
+
+    for file in files:
+        if file and allowed_file(file.filename):
+            file.save(os.path.join('static/upload/', filename))
+            success = True
+        elif file and not allowed_file(file.filename):
+            errors["message"] = 'File size of {} exceeds the maximum allowed size of {} MB or below the minimum allowed size of {} KB'.format(file.filename, MAX_FILE_SIZE_MB, MIN_FILE_SIZE_KB)
+            success = False
+
+    if not success:
+        resp = jsonify(errors)
+        resp.status_code = 400
+        return resp
+
+    img_url = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+
+    img = Image.open(img_url).convert('RGB')
+    
+    # Convert image to numpy array
+    img_array = np.array(img)
+    
+    # Preprocessing image using OpenCV
+    blur = cv2.GaussianBlur(img_array, (5, 5), 0)
+    gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+    kernel = np.ones((3, 3), np.uint8)
+    edges = cv2.erode(gray, kernel, iterations=1)
+    _, imfill = cv2.threshold(edges, 220, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    imgResize = cv2.resize(imfill, (224, 224))
+    
+    # Save preprocessed image
+    # now = datetime.now()
+    # predict_image_path = 'static/upload/' + now.strftime("%d%m%y-%H%M%S") + ".png"
+    predict_image_path = 'static/upload/process_image.png'
+    cv2.imwrite(predict_image_path, imgResize)
+    
+    img.close()
+
+    img = keras_image.load_img(predict_image_path, target_size=(224, 224, 3))
+    x = keras_image.img_to_array(img)
+    x = x / 127.5 - 1
+    x = np.expand_dims(x, axis=0)
+    images = np.vstack([x])
+
+    prediction_tremor = model.predict(images)
+
+    class_names = {0: 'non tremor', 1: 'tremor'}
+    predicted_class_index = np.argmax(prediction_tremor)
+    predicted_class_name = class_names[predicted_class_index]
+
+    if predicted_class_index == 0:
+        saran = "Stay healthy and exercise often, avoid alcohol-containing drinks"
+    else:
+        saran = "Immediately consult a Neurologist for further treatment."
+
+    predict_image_path = 'static/upload/process_image.png'
+
+    return render_template("klasifikasi.html", img_path=img_url,
+                           predictiontremor=predicted_class_name,
+                           confidencetremor='{:.2%}'.format(np.max(prediction_tremor)),
+                           saran=saran,
+                           predict_image_path=predict_image_path)
+
+@app.route('/refresh', methods=['GET', 'POST'])
+def refresh():
+    return redirect(url_for('klasifikasi'))
+
+if __name__=='__main__':
+    app.run(debug=True)