├── 1040_right.jpeg ├── 1051_right.jpeg ├── DR.ipynb ├── Diabetic_retinopathy_detection.ipynb ├── Mild ├── 10150_right.jpeg ├── 10169_right.jpeg ├── 1020_left.jpeg ├── 1020_right.jpeg ├── 1027_left.jpeg ├── 1043_left.jpeg ├── 10468_right.jpeg ├── 10594_right.jpeg ├── 10672_left.jpeg ├── 114_left.jpeg ├── 114_right.jpeg ├── 1266_left.jpeg ├── 1266_right.jpeg ├── 1283_left.jpeg ├── 1343_left.jpeg ├── 1343_right.jpeg ├── 1369_left.jpeg ├── 1369_right.jpeg ├── 1374_right.jpeg ├── 1409_left.jpeg ├── 1520_left.jpeg ├── 1520_right.jpeg ├── 1544_right.jpeg ├── 15_left.jpeg ├── 17_right.jpeg ├── 1833_right.jpeg ├── 1867_left.jpeg ├── 1867_right.jpeg ├── 1999_left.jpeg ├── 2168_left.jpeg ├── 227_left.jpeg ├── 2294_left.jpeg ├── 2294_right.jpeg ├── 2318_right.jpeg ├── 240_right.jpeg ├── 2423_left.jpeg ├── 2423_right.jpeg ├── 2433_right.jpeg ├── 2488_left.jpeg ├── 2488_right.jpeg ├── 2525_left.jpeg └── 2691_left.jpeg ├── Moderate ├── 10109_left.jpeg ├── 10109_right.jpeg ├── 10112_left.jpeg ├── 10135_left.jpeg ├── 10135_right.jpeg ├── 10150_left.jpeg ├── 10156_left.jpeg ├── 10156_right.jpeg ├── 10159_left.jpeg ├── 10160_left.jpeg ├── 10160_right.jpeg ├── 1030_left.jpeg ├── 1030_right.jpeg ├── 1034_left.jpeg ├── 1034_right.jpeg ├── 1140_left.jpeg ├── 1140_right.jpeg ├── 1142_right.jpeg ├── 1152_right.jpeg ├── 1178_left.jpeg ├── 1178_right.jpeg ├── 1233_left.jpeg ├── 1233_right.jpeg ├── 1259_left.jpeg ├── 1259_right.jpeg ├── 129_left.jpeg ├── 129_right.jpeg ├── 1367_left.jpeg ├── 1367_right.jpeg ├── 1407_left.jpeg ├── 1407_right.jpeg ├── 1412_left.jpeg ├── 1419_left.jpeg ├── 1419_right.jpeg ├── 1541_left.jpeg ├── 1541_right.jpeg ├── 1544_left.jpeg ├── 1552_left.jpeg ├── 1552_right.jpeg ├── 155_right.jpeg ├── 15_right.jpeg ├── 1632_left.jpeg ├── 1632_right.jpeg ├── 1640_right.jpeg ├── 1675_left.jpeg ├── 1675_right.jpeg ├── 172_left.jpeg └── 172_right.jpeg ├── Normal ├── 10003_left.jpeg ├── 10003_right.jpeg ├── 10007_left.jpeg ├── 10007_right.jpeg ├── 1012_left.jpeg ├── 1021_left.jpeg ├── 1021_right.jpeg ├── 1027_right.jpeg ├── 1040_left.jpeg ├── 1040_right.jpeg ├── 1043_right.jpeg ├── 1044_left.jpeg ├── 1044_right.jpeg ├── 1051_left.jpeg ├── 1051_right.jpeg ├── 1056_left.jpeg ├── 1056_right.jpeg ├── 1057_left.jpeg ├── 1057_right.jpeg ├── 1096_left.jpeg ├── 1096_right.jpeg ├── 10_left.jpeg ├── 1113_left.jpeg ├── 1113_right.jpeg ├── 1123_left.jpeg ├── 1123_right.jpeg ├── 1127_left.jpeg ├── 1127_right.jpeg ├── 1136_left.jpeg ├── 1136_right.jpeg ├── 1142_left.jpeg ├── 1146_right.jpeg ├── 1152_left.jpeg ├── 1161_left.jpeg └── 1161_right.jpeg ├── Proliferative ├── 10017_left.jpeg ├── 10017_right.jpeg ├── 1012_right.jpeg ├── 10321_left.jpeg ├── 1052_left.jpeg ├── 1052_right.jpeg ├── 10785_left.jpeg ├── 10904_left.jpeg ├── 10904_right.jpeg ├── 10_right.jpeg ├── 111_left.jpeg ├── 111_right.jpeg ├── 1120_left.jpeg ├── 1120_right.jpeg ├── 1131_left.jpeg ├── 1131_right.jpeg ├── 1138_left.jpeg ├── 1138_right.jpeg ├── 11417_right.jpeg ├── 1146_left.jpeg ├── 11730_right.jpeg ├── 11854_left.jpeg ├── 11854_right.jpeg ├── 12108_left.jpeg ├── 12108_right.jpeg ├── 12652_left.jpeg ├── 12995_left.jpeg ├── 13022_right.jpeg ├── 13038_right.jpeg ├── 13066_right.jpeg ├── 13387_right.jpeg ├── 13588_right.jpeg ├── 13651_right.jpeg ├── 13664_left.jpeg ├── 13664_right.jpeg ├── 13811_left.jpeg ├── 13811_right.jpeg ├── 13819_left.jpeg ├── 13819_right.jpeg ├── 14495_right.jpeg ├── 14844_left.jpeg ├── 14844_right.jpeg ├── 15038_left.jpeg ├── 15459_left.jpeg ├── 2016_left.jpeg ├── 2031_right.jpeg ├── 294_left.jpeg ├── 294_right.jpeg ├── 3064_left.jpeg ├── 3064_right.jpeg └── 3088_left.jpeg ├── README.md ├── Severe ├── 1008_left.jpeg ├── 10153_left.jpeg ├── 10153_right.jpeg ├── 10159_right.jpeg ├── 11031_left.jpeg ├── 11031_right.jpeg ├── 11035_left.jpeg ├── 11035_right.jpeg ├── 11206_right.jpeg ├── 11211_left.jpeg ├── 11546_left.jpeg ├── 11546_right.jpeg ├── 11697_right.jpeg ├── 1170_left.jpeg ├── 1170_right.jpeg ├── 1177_left.jpeg ├── 1177_right.jpeg ├── 11871_left.jpeg ├── 11889_left.jpeg ├── 11889_right.jpeg ├── 1196_left.jpeg ├── 1196_right.jpeg ├── 12000_left.jpeg ├── 12306_left.jpeg ├── 12612_left.jpeg ├── 12612_right.jpeg ├── 12652_right.jpeg ├── 12667_left.jpeg ├── 12667_right.jpeg ├── 13716_left.jpeg ├── 13716_right.jpeg ├── 14109_left.jpeg ├── 14109_right.jpeg ├── 14129_left.jpeg ├── 14129_right.jpeg ├── 1471_left.jpeg ├── 1471_right.jpeg ├── 1639_left.jpeg ├── 1639_right.jpeg ├── 1919_left.jpeg ├── 2132_right.jpeg ├── 2275_left.jpeg ├── 23426_left.jpeg ├── 23426_right.jpeg ├── 23485_left.jpeg ├── 23604_right.jpeg ├── 2862_left.jpeg ├── 2862_right.jpeg ├── 3096_left.jpeg ├── 3096_right.jpeg └── 3192_left.jpeg ├── app1.py ├── app2.py ├── dataset ├── label_image.py ├── requirements.txt ├── retrain.py ├── retrained_labels.txt ├── static ├── css │ ├── main1.css │ ├── style.css │ └── style1.css ├── js │ ├── main.js │ └── main1.js ├── main1.js ├── style.css └── vendor │ ├── animate.css │ ├── animate.compat.css │ ├── animate.css │ └── animate.min.css │ ├── aos │ ├── aos.css │ └── aos.js │ ├── bootstrap-icons │ ├── bootstrap-icons.css │ ├── bootstrap-icons.json │ ├── fonts │ │ ├── bootstrap-icons.woff │ │ └── bootstrap-icons.woff2 │ └── index.html │ ├── bootstrap │ ├── css │ │ ├── bootstrap-grid.css │ │ ├── bootstrap-grid.css.map │ │ ├── bootstrap-grid.min.css │ │ ├── bootstrap-grid.min.css.map │ │ ├── bootstrap-grid.rtl.css │ │ ├── bootstrap-grid.rtl.css.map │ │ ├── bootstrap-grid.rtl.min.css │ │ ├── bootstrap-grid.rtl.min.css.map │ │ ├── bootstrap-reboot.css │ │ ├── bootstrap-reboot.css.map │ │ ├── bootstrap-reboot.min.css │ │ ├── bootstrap-reboot.min.css.map │ │ ├── bootstrap-reboot.rtl.css │ │ ├── bootstrap-reboot.rtl.css.map │ │ ├── bootstrap-reboot.rtl.min.css │ │ ├── bootstrap-reboot.rtl.min.css.map │ │ ├── bootstrap-utilities.css │ │ ├── bootstrap-utilities.css.map │ │ ├── bootstrap-utilities.min.css │ │ ├── bootstrap-utilities.min.css.map │ │ ├── bootstrap-utilities.rtl.css │ │ ├── bootstrap-utilities.rtl.css.map │ │ ├── bootstrap-utilities.rtl.min.css │ │ ├── bootstrap-utilities.rtl.min.css.map │ │ ├── bootstrap.css │ │ ├── bootstrap.css.map │ │ ├── bootstrap.min.css │ │ ├── bootstrap.min.css.map │ │ ├── bootstrap.rtl.css │ │ ├── bootstrap.rtl.css.map │ │ ├── bootstrap.rtl.min.css │ │ └── bootstrap.rtl.min.css.map │ └── js │ │ ├── bootstrap.bundle.js │ │ ├── bootstrap.bundle.js.map │ │ ├── bootstrap.bundle.min.js │ │ ├── bootstrap.bundle.min.js.map │ │ ├── bootstrap.esm.js │ │ ├── bootstrap.esm.js.map │ │ ├── bootstrap.esm.min.js │ │ ├── bootstrap.esm.min.js.map │ │ ├── bootstrap.js │ │ ├── bootstrap.js.map │ │ ├── bootstrap.min.js │ │ └── bootstrap.min.js.map │ ├── boxicons │ ├── css │ │ ├── animations.css │ │ ├── boxicons.css │ │ ├── boxicons.min.css │ │ └── transformations.css │ └── fonts │ │ ├── boxicons.eot │ │ ├── boxicons.svg │ │ ├── boxicons.ttf │ │ ├── boxicons.woff │ │ └── boxicons.woff2 │ ├── glightbox │ ├── css │ │ ├── glightbox.css │ │ └── glightbox.min.css │ └── js │ │ ├── glightbox.js │ │ └── glightbox.min.js │ ├── isotope-layout │ ├── isotope.pkgd.js │ └── isotope.pkgd.min.js │ ├── php-email-form │ └── validate.js │ └── swiper │ ├── swiper-bundle.min.css │ └── swiper-bundle.min.js └── templates ├── base.html ├── chart.html ├── first.html ├── index.html └── login.html /1040_right.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/picoders1/AI-Driven-DR-Detection-System/9d64e022f1af1b4bacfb5196c94762078ac87bda/1040_right.jpeg -------------------------------------------------------------------------------- /1051_right.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/picoders1/AI-Driven-DR-Detection-System/9d64e022f1af1b4bacfb5196c94762078ac87bda/1051_right.jpeg -------------------------------------------------------------------------------- /Mild/10150_right.jpeg: 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-------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # AI-Driven-DR-Detection-System 2 | 3 | AI-driven Detection & Classification of Diabetic Retinopathy is a cutting-edge approach that combines artificial intelligence (AI) with medical image analysis to identify and categorize a specific eye disease called Diabetic Retinopathy (DR). 4 | 5 | Diabetic retinopathy is a serious and advancing ocular disease that impacts people who have diabetes, especially individuals with poorly managed glucose levels over an extended period. It ranks among the primary reason for blindness among adults & elderly individuals worldwide. This condition is a serious and widespread complication of diabetes that affects the retina, leading to vision impairment or even blindness if left untreated. 6 | 7 | The proposed AI-based approach leverages deep learning algorithms, primarily convnets, to automatically detect and classify DR lesions in retinal fundus images. The method involves several essential steps, including Data Collection, Preprocessing, Augmentation, and modeling, culminating in fundus image categorization into severity levels such as No DR, Mild, Moderate, Severe, and Proliferative DR. Through the utilization of GPUs during model training, computation time is significantly reduced, resulting in an outstanding roughly achieving an accuracy of 85% on a dataset of 35,126 publicly released retinal images from eyePACS on Kaggle. 8 | 9 | This accessibility is particularly beneficial for regions with limited access to specialized ophthalmologists, enabling early detection and intervention for individuals at risk of diabetic retinopathy. Through leveraging the capabilities of deep learning and image analysis, this approach offers accurate and efficient detection of DR lesions, aiding healthcare professionals in making informed decisions and ultimately improving the outcomes for patients with diabetes. 10 | 11 | 12 | ## Dataset 13 | 14 | The dataset comprises a total of 35,126 images, which presents a notable class imbalance, notably with class 0 (No DR) representing a significant majority at 73.47%. To overcome this imbalance and ensure effective model training, data augmentation techniques are implemented, which involve augmenting the existing data to balance the distribution across all classes. For a comprehensive breakdown investigating the count of images in each class, refer below. The pivotal role of data augmentation becomes apparent in enhancing the model's ability to learn from diverse samples and ultimately improve the precision and robustness of the diabetic retinopathy detection system. 15 | 16 | ## Dataset: Class Count in DR 17 | No DR - 25810 | 18 | Mild NPDR - 2443 || 19 | Moderate NPDR - 5292 || 20 | Severe NPDR - 873 || 21 | Proliferative - 708 22 | 23 | 24 | ## Implementation Result 25 | 26 | ## Figure 1: Login into the DR System through User Credentials 27 | 28 | ![image](https://github.com/picoders1/AI-Driven-DR-Detection-System/assets/87698874/493d2b68-d85b-434f-823d-6359a67a3c1e) 29 | 30 | ## Figure 2: Home screen of the Diabetic Retinopathy Diagnosis system 31 | 32 | ![image](https://github.com/picoders1/AI-Driven-DR-Detection-System/assets/87698874/7d2b4238-80c8-4181-969d-8a3a7ee625fd) 33 | 34 | ## Figure 3: Upload Input as Fundus Image 35 | 36 | ![image](https://github.com/picoders1/AI-Driven-DR-Detection-System/assets/87698874/ddb2eb00-dff2-477a-85c5-24b63949b74b) 37 | 38 | ## Figure 4: Assessing the patient's condition through the Automated Neural Network Model 39 | 40 | ![image](https://github.com/picoders1/AI-Driven-DR-Detection-System/assets/87698874/fe3ee4d7-2a06-4272-b717-c83f57f2e61a) 41 | 42 | 43 | The system has the potential to revolutionize the healthcare industry by improving patient outcomes, increasing access to care, and reducing costs. 44 | 45 | The system is concerned about accuracy and patient privacy and represents an important and promising area of innovation in healthcare. 46 | 47 | 48 | ## ----- Future Enhancement ----- 49 | 50 | ## 1. Telemedicine and Remote Screening: 51 | 52 | Telemedicine platforms and remote screening solutions will become more prevalent, allowing individuals with diabetes in remote or underserved areas to access diabetic retinopathy detection services without the need for in-person visits to specialized eye clinics. 53 | 54 | ## 2. Mobile Applications and Self-monitoring: 55 | 56 | Mobile applications and self-monitoring tools may empower individuals with diabetes to regularly assess their eye health at home. With the integration of smartphone cameras and AI-based algorithms, these applications could enable self-screening for diabetic retinopathy and prompt users to seek a professional evaluation if necessary. 57 | 58 | 59 | ## Thank You !! 60 | -------------------------------------------------------------------------------- /Severe/1008_left.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/picoders1/AI-Driven-DR-Detection-System/9d64e022f1af1b4bacfb5196c94762078ac87bda/Severe/1008_left.jpeg -------------------------------------------------------------------------------- /Severe/10153_left.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/picoders1/AI-Driven-DR-Detection-System/9d64e022f1af1b4bacfb5196c94762078ac87bda/Severe/10153_left.jpeg -------------------------------------------------------------------------------- /Severe/10153_right.jpeg: 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load_image(file_path) 25 | result = result.title() 26 | d = {"no dr":"✓",'Mild':"❌","Moderate":"❌","Severe":"❌","proliferative dr":"❌"} 27 | result = result+d[result] 28 | #result = [result] 29 | print(result) 30 | os.remove(file_path) 31 | return result 32 | return None 33 | 34 | if __name__ == '__main__': 35 | app.run() -------------------------------------------------------------------------------- /app2.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, render_template, request, url_for, Markup, jsonify 2 | import pickle 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn.preprocessing import MinMaxScaler 6 | from werkzeug.utils import secure_filename 7 | import pickle 8 | from flask import * 9 | import os 10 | from werkzeug.utils import secure_filename 11 | import label_image 12 | 13 | def load_image(image): 14 | text = label_image.main(image) 15 | return text 16 | 17 | 18 | 19 | app = Flask(__name__) #Initialize the flask App 20 | 21 | 22 | 23 | 24 | @app.route('/') 25 | @app.route('/first') 26 | def first(): 27 | return render_template('first.html') 28 | 29 | 30 | 31 | 32 | @app.route('/login') 33 | def login(): 34 | return render_template('login.html') 35 | @app.route('/chart') 36 | def chart(): 37 | return render_template('chart.html') 38 | 39 | 40 | @app.route('/index') 41 | def index(): 42 | return render_template('index.html') 43 | 44 | @app.route('/predict', methods=['GET', 'POST']) 45 | def upload(): 46 | if request.method == 'POST': 47 | # Get the file from post request 48 | f = request.files['file'] 49 | file_path = secure_filename(f.filename) 50 | f.save(file_path) 51 | # Make prediction 52 | result = load_image(file_path) 53 | result = result.title() 54 | d = {"Normal":"✓",'Mild':"❌","Moderate":"❌","Severe":"❌","Proliferative":"❌"} 55 | result = result+d[result] 56 | #result = [result] 57 | print(result) 58 | os.remove(file_path) 59 | return result 60 | return None 61 | 62 | if __name__ == '__main__': 63 | app.run(debug=False,host='0.0.0.0') -------------------------------------------------------------------------------- /dataset: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /label_image.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | 6 | import argparse 7 | import sys 8 | import time 9 | 10 | import numpy as np 11 | import tensorflow.compat.v1 as tf 12 | tf.disable_v2_behavior() 13 | 14 | def load_graph(model_file): 15 | graph = tf.Graph() 16 | graph_def = tf.compat.v1.GraphDef() 17 | 18 | with open(model_file, "rb") as f: 19 | graph_def.ParseFromString(f.read()) 20 | with graph.as_default(): 21 | tf.import_graph_def(graph_def) 22 | 23 | return graph 24 | 25 | def read_tensor_from_image_file(file_name, input_height=299, input_width=299, 26 | input_mean=0, input_std=255): 27 | input_name = "file_reader" 28 | output_name = "normalized" 29 | file_reader = tf.io.read_file(file_name, input_name) 30 | if file_name.endswith(".png"): 31 | image_reader = tf.image.decode_png(file_reader, channels = 3, 32 | name='png_reader') 33 | elif file_name.endswith(".gif"): 34 | image_reader = tf.squeeze(tf.image.decode_gif(file_reader, 35 | name='gif_reader')) 36 | elif file_name.endswith(".bmp"): 37 | image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader') 38 | else: 39 | image_reader = tf.image.decode_jpeg(file_reader, channels = 3, 40 | name='jpeg_reader') 41 | float_caster = tf.cast(image_reader, tf.float32) 42 | dims_expander = tf.expand_dims(float_caster, 0); 43 | resized = tf.image.resize(dims_expander, [input_height, input_width]) 44 | normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std]) 45 | sess = tf.Session() 46 | result = sess.run(normalized) 47 | 48 | return result 49 | 50 | def load_labels(label_file): 51 | label = [] 52 | 53 | proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines() 54 | for l in proto_as_ascii_lines: 55 | label.append(l.rstrip()) 56 | 57 | return label 58 | 59 | 60 | 61 | def main(img): 62 | file_name = img 63 | model_file = "retrained_graph.pb" 64 | label_file = "retrained_labels.txt" 65 | input_height = 299 66 | input_width = 299 67 | input_mean = 128 68 | input_std = 128 69 | input_layer = "Mul" 70 | output_layer = "final_result" 71 | 72 | parser = argparse.ArgumentParser() 73 | parser.add_argument("--image", help="image to be processed") 74 | parser.add_argument("--graph", help="graph/model to be executed") 75 | parser.add_argument("--labels", help="name of file containing labels") 76 | parser.add_argument("--input_height", type=int, help="input height") 77 | parser.add_argument("--input_width", type=int, help="input width") 78 | parser.add_argument("--input_mean", type=int, help="input mean") 79 | parser.add_argument("--input_std", type=int, help="input std") 80 | parser.add_argument("--input_layer", help="name of input layer") 81 | parser.add_argument("--output_layer", help="name of output layer") 82 | args = parser.parse_args() 83 | 84 | if args.graph: 85 | model_file = args.graph 86 | if args.image: 87 | file_name = args.image 88 | if args.labels: 89 | label_file = args.labels 90 | if args.input_height: 91 | input_height = args.input_height 92 | if args.input_width: 93 | input_width = args.input_width 94 | if args.input_mean: 95 | input_mean = args.input_mean 96 | if args.input_std: 97 | input_std = args.input_std 98 | if args.input_layer: 99 | input_layer = args.input_layer 100 | if args.output_layer: 101 | output_layer = args.output_layer 102 | 103 | graph = load_graph(model_file) 104 | t = read_tensor_from_image_file(file_name, 105 | input_height=input_height, 106 | input_width=input_width, 107 | input_mean=input_mean, 108 | input_std=input_std) 109 | 110 | input_name = "import/" + input_layer 111 | output_name = "import/" + output_layer 112 | input_operation = graph.get_operation_by_name(input_name); 113 | output_operation = graph.get_operation_by_name(output_name); 114 | 115 | 116 | with tf.Session(graph=graph) as sess: 117 | start = time.time() 118 | results = sess.run(output_operation.outputs[0], 119 | {input_operation.outputs[0]: t}) 120 | end=time.time() 121 | results = np.squeeze(results) 122 | 123 | top_k = results.argsort()[-5:][::-1] 124 | labels = load_labels(label_file) 125 | 126 | 127 | 128 | 129 | 130 | 131 | for i in top_k: 132 | return labels[i] -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | tensorflow==1.14.0 3 | flask 4 | Werkzeug 5 | gunicorn 6 | 7 | python retrain.py --output_graph=retrained_graph.pb --output_labels=retrained_labels.txt --architecure=MobileNet_1.0_224 --image_dir=dataset -------------------------------------------------------------------------------- /retrained_labels.txt: -------------------------------------------------------------------------------- 1 | mild 2 | moderate 3 | normal 4 | proliferative 5 | severe 6 | -------------------------------------------------------------------------------- /static/css/main1.css: -------------------------------------------------------------------------------- 1 | 2 | .img-preview { 3 | width: 256px; 4 | height: 256px; 5 | position: relative; 6 | border: 5px solid #F8F8F8; 7 | box-shadow: 0px 2px 4px 0px rgba(0, 0, 0, 0.1); 8 | margin-top: 1em; 9 | margin-bottom: 1em; 10 | } 11 | 12 | .img-preview>div { 13 | width: 100%; 14 | height: 100%; 15 | background-size: 256px 256px; 16 | background-repeat: no-repeat; 17 | background-position: center; 18 | } 19 | 20 | input[type="file"] { 21 | display: none; 22 | } 23 | 24 | .upload-label{ 25 | display: inline-block; 26 | padding: 12px 30px; 27 | background: #39D2B4; 28 | color: #fff; 29 | font-size: 1em; 30 | transition: all .4s; 31 | cursor: pointer; 32 | } 33 | 34 | .upload-label:hover{ 35 | background: #34495E; 36 | color: #39D2B4; 37 | } 38 | 39 | .loader { 40 | border: 8px solid #f3f3f3; /* Light grey */ 41 | border-top: 8px solid #3498db; /* Blue */ 42 | border-radius: 50%; 43 | width: 50px; 44 | height: 50px; 45 | animation: spin 1s linear infinite; 46 | } 47 | 48 | @keyframes spin { 49 | 0% { transform: rotate(0deg); } 50 | 100% { transform: rotate(360deg); } 51 | } -------------------------------------------------------------------------------- /static/js/main.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Template Name: Restaurantly - v3.0.1 3 | * Template URL: https://bootstrapmade.com/restaurantly-restaurant-template/ 4 | * Author: BootstrapMade.com 5 | * License: https://bootstrapmade.com/license/ 6 | */ 7 | (function() { 8 | "use strict"; 9 | 10 | /** 11 | * Easy selector helper function 12 | */ 13 | const select = (el, all = false) => { 14 | el = el.trim() 15 | if (all) { 16 | return [...document.querySelectorAll(el)] 17 | } else { 18 | return document.querySelector(el) 19 | } 20 | } 21 | 22 | /** 23 | * Easy event listener function 24 | */ 25 | const on = (type, el, listener, all = false) => { 26 | let selectEl = select(el, all) 27 | if (selectEl) { 28 | if (all) { 29 | selectEl.forEach(e => e.addEventListener(type, listener)) 30 | } else { 31 | selectEl.addEventListener(type, listener) 32 | } 33 | } 34 | } 35 | 36 | /** 37 | * Easy on scroll event listener 38 | */ 39 | const onscroll = (el, listener) => { 40 | el.addEventListener('scroll', listener) 41 | } 42 | 43 | /** 44 | * Navbar links active state on scroll 45 | */ 46 | let navbarlinks = select('#navbar .scrollto', true) 47 | const navbarlinksActive = () => { 48 | let position = window.scrollY + 200 49 | navbarlinks.forEach(navbarlink => { 50 | if (!navbarlink.hash) return 51 | let section = select(navbarlink.hash) 52 | if (!section) return 53 | if (position >= section.offsetTop && position <= (section.offsetTop + section.offsetHeight)) { 54 | navbarlink.classList.add('active') 55 | } else { 56 | navbarlink.classList.remove('active') 57 | } 58 | }) 59 | } 60 | window.addEventListener('load', navbarlinksActive) 61 | onscroll(document, navbarlinksActive) 62 | 63 | /** 64 | * Scrolls to an element with header offset 65 | */ 66 | const scrollto = (el) => { 67 | let header = select('#header') 68 | let offset = header.offsetHeight 69 | 70 | let elementPos = select(el).offsetTop 71 | window.scrollTo({ 72 | top: elementPos - offset, 73 | behavior: 'smooth' 74 | }) 75 | } 76 | 77 | /** 78 | * Toggle .header-scrolled class to #header when page is scrolled 79 | */ 80 | let selectHeader = select('#header') 81 | let selectTopbar = select('#topbar') 82 | if (selectHeader) { 83 | const headerScrolled = () => { 84 | if (window.scrollY > 100) { 85 | selectHeader.classList.add('header-scrolled') 86 | if (selectTopbar) { 87 | selectTopbar.classList.add('topbar-scrolled') 88 | } 89 | } else { 90 | selectHeader.classList.remove('header-scrolled') 91 | if (selectTopbar) { 92 | selectTopbar.classList.remove('topbar-scrolled') 93 | } 94 | } 95 | } 96 | window.addEventListener('load', headerScrolled) 97 | onscroll(document, headerScrolled) 98 | } 99 | 100 | /** 101 | * Back to top button 102 | */ 103 | let backtotop = select('.back-to-top') 104 | if (backtotop) { 105 | const toggleBacktotop = () => { 106 | if (window.scrollY > 100) { 107 | backtotop.classList.add('active') 108 | } else { 109 | backtotop.classList.remove('active') 110 | } 111 | } 112 | window.addEventListener('load', toggleBacktotop) 113 | onscroll(document, toggleBacktotop) 114 | } 115 | 116 | /** 117 | * Mobile nav toggle 118 | */ 119 | on('click', '.mobile-nav-toggle', function(e) { 120 | select('#navbar').classList.toggle('navbar-mobile') 121 | this.classList.toggle('bi-list') 122 | this.classList.toggle('bi-x') 123 | }) 124 | 125 | /** 126 | * Mobile nav dropdowns activate 127 | */ 128 | on('click', '.navbar .dropdown > a', function(e) { 129 | if (select('#navbar').classList.contains('navbar-mobile')) { 130 | e.preventDefault() 131 | this.nextElementSibling.classList.toggle('dropdown-active') 132 | } 133 | }, true) 134 | 135 | /** 136 | * Scrool with ofset on links with a class name .scrollto 137 | */ 138 | on('click', '.scrollto', function(e) { 139 | if (select(this.hash)) { 140 | e.preventDefault() 141 | 142 | let navbar = select('#navbar') 143 | if (navbar.classList.contains('navbar-mobile')) { 144 | navbar.classList.remove('navbar-mobile') 145 | let navbarToggle = select('.mobile-nav-toggle') 146 | navbarToggle.classList.toggle('bi-list') 147 | navbarToggle.classList.toggle('bi-x') 148 | } 149 | scrollto(this.hash) 150 | } 151 | }, true) 152 | 153 | /** 154 | * Scroll with ofset on page load with hash links in the url 155 | */ 156 | window.addEventListener('load', () => { 157 | if (window.location.hash) { 158 | if (select(window.location.hash)) { 159 | scrollto(window.location.hash) 160 | } 161 | } 162 | }); 163 | 164 | /** 165 | * Preloader 166 | */ 167 | let preloader = select('#preloader'); 168 | if (preloader) { 169 | window.addEventListener('load', () => { 170 | preloader.remove() 171 | }); 172 | } 173 | 174 | /** 175 | * Menu isotope and filter 176 | */ 177 | window.addEventListener('load', () => { 178 | let menuContainer = select('.menu-container'); 179 | if (menuContainer) { 180 | let menuIsotope = new Isotope(menuContainer, { 181 | itemSelector: '.menu-item', 182 | layoutMode: 'fitRows' 183 | }); 184 | 185 | let menuFilters = select('#menu-flters li', true); 186 | 187 | on('click', '#menu-flters li', function(e) { 188 | e.preventDefault(); 189 | menuFilters.forEach(function(el) { 190 | el.classList.remove('filter-active'); 191 | }); 192 | this.classList.add('filter-active'); 193 | 194 | menuIsotope.arrange({ 195 | filter: this.getAttribute('data-filter') 196 | }); 197 | menuIsotope.on('arrangeComplete', function() { 198 | AOS.refresh() 199 | }); 200 | }, true); 201 | } 202 | 203 | }); 204 | 205 | /** 206 | * Initiate glightbox 207 | */ 208 | const glightbox = GLightbox({ 209 | selector: '.glightbox' 210 | }); 211 | 212 | /** 213 | * Events slider 214 | */ 215 | new Swiper('.events-slider', { 216 | speed: 600, 217 | loop: true, 218 | autoplay: { 219 | delay: 5000, 220 | disableOnInteraction: false 221 | }, 222 | slidesPerView: 'auto', 223 | pagination: { 224 | el: '.swiper-pagination', 225 | type: 'bullets', 226 | clickable: true 227 | } 228 | }); 229 | 230 | /** 231 | * Testimonials slider 232 | */ 233 | new Swiper('.testimonials-slider', { 234 | speed: 600, 235 | loop: true, 236 | autoplay: { 237 | delay: 5000, 238 | disableOnInteraction: false 239 | }, 240 | slidesPerView: 'auto', 241 | pagination: { 242 | el: '.swiper-pagination', 243 | type: 'bullets', 244 | clickable: true 245 | }, 246 | breakpoints: { 247 | 320: { 248 | slidesPerView: 1, 249 | spaceBetween: 20 250 | }, 251 | 252 | 1200: { 253 | slidesPerView: 3, 254 | spaceBetween: 20 255 | } 256 | } 257 | }); 258 | 259 | /** 260 | * Initiate gallery lightbox 261 | */ 262 | const galleryLightbox = GLightbox({ 263 | selector: '.gallery-lightbox' 264 | }); 265 | 266 | /** 267 | * Animation on scroll 268 | */ 269 | window.addEventListener('load', () => { 270 | AOS.init({ 271 | duration: 1000, 272 | easing: 'ease-in-out', 273 | once: true, 274 | mirror: false 275 | }) 276 | }); 277 | 278 | })() -------------------------------------------------------------------------------- /static/js/main1.js: -------------------------------------------------------------------------------- 1 | $(document).ready(function () { 2 | // Init 3 | $('.image-section').hide(); 4 | $('.loader').hide(); 5 | $('#result').hide(); 6 | 7 | // Upload Preview 8 | function readURL(input) { 9 | if (input.files && input.files[0]) { 10 | var reader = new FileReader(); 11 | reader.onload = function (e) { 12 | $('#imagePreview').css('background-image', 'url(' + e.target.result + ')'); 13 | $('#imagePreview').hide(); 14 | $('#imagePreview').fadeIn(650); 15 | } 16 | reader.readAsDataURL(input.files[0]); 17 | } 18 | } 19 | $("#imageUpload").change(function () { 20 | $('.image-section').show(); 21 | $('#btn-predict').show(); 22 | $('#result').text(''); 23 | $('#result').hide(); 24 | readURL(this); 25 | }); 26 | 27 | // Predict 28 | $('#btn-predict').click(function () { 29 | var form_data = new FormData($('#upload-file')[0]); 30 | 31 | // Show loading animation 32 | $(this).hide(); 33 | $('.loader').show(); 34 | 35 | // Make prediction by calling api /predict 36 | $.ajax({ 37 | type: 'POST', 38 | url: '/predict', 39 | data: form_data, 40 | contentType: false, 41 | cache: false, 42 | processData: false, 43 | async: true, 44 | success: function (data) { 45 | // Get and display the result 46 | $('.loader').hide(); 47 | $('#result').fadeIn(600); 48 | $('#result').text(' Result: ' + data); 49 | console.log('Success!'); 50 | }, 51 | }); 52 | }); 53 | 54 | }); -------------------------------------------------------------------------------- /static/main1.js: -------------------------------------------------------------------------------- 1 | $(document).ready(function () { 2 | // Init 3 | $('.image-section').hide(); 4 | $('.loader').hide(); 5 | $('#result').hide(); 6 | 7 | // Upload Preview 8 | function readURL(input) { 9 | if (input.files && input.files[0]) { 10 | var reader = new FileReader(); 11 | reader.onload = function (e) { 12 | $('#imagePreview').css('background-image', 'url(' + e.target.result + ')'); 13 | $('#imagePreview').hide(); 14 | $('#imagePreview').fadeIn(650); 15 | } 16 | reader.readAsDataURL(input.files[0]); 17 | } 18 | } 19 | $("#imageUpload").change(function () { 20 | $('.image-section').show(); 21 | $('#btn-predict').show(); 22 | $('#result').text(''); 23 | 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17 | } 18 | } 19 | 20 | body { 21 | margin: 0; 22 | font-family: system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", "Liberation Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; 23 | font-size: 1rem; 24 | font-weight: 400; 25 | line-height: 1.5; 26 | color: #212529; 27 | background-color: #fff; 28 | -webkit-text-size-adjust: 100%; 29 | -webkit-tap-highlight-color: rgba(0, 0, 0, 0); 30 | } 31 | 32 | [tabindex="-1"]:focus:not(:focus-visible) { 33 | outline: 0 !important; 34 | } 35 | 36 | hr { 37 | margin: 1rem 0; 38 | color: inherit; 39 | background-color: currentColor; 40 | border: 0; 41 | opacity: 0.25; 42 | } 43 | 44 | hr:not([size]) { 45 | height: 1px; 46 | } 47 | 48 | h6, h5, h4, h3, h2, h1 { 49 | margin-top: 0; 50 | margin-bottom: 0.5rem; 51 | font-weight: 500; 52 | line-height: 1.2; 53 | } 54 | 55 | h1 { 56 | font-size: calc(1.375rem + 1.5vw); 57 | } 58 | @media (min-width: 1200px) { 59 | h1 { 60 | 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| line-height: inherit; 118 | } 119 | 120 | ol, 121 | ul { 122 | padding-left: 2rem; 123 | } 124 | 125 | ol, 126 | ul, 127 | dl { 128 | margin-top: 0; 129 | margin-bottom: 1rem; 130 | } 131 | 132 | ol ol, 133 | ul ul, 134 | ol ul, 135 | ul ol { 136 | margin-bottom: 0; 137 | } 138 | 139 | dt { 140 | font-weight: 700; 141 | } 142 | 143 | dd { 144 | margin-bottom: 0.5rem; 145 | margin-left: 0; 146 | } 147 | 148 | blockquote { 149 | margin: 0 0 1rem; 150 | } 151 | 152 | b, 153 | strong { 154 | font-weight: bolder; 155 | } 156 | 157 | small { 158 | font-size: 0.875em; 159 | } 160 | 161 | mark { 162 | padding: 0.2em; 163 | background-color: #fcf8e3; 164 | } 165 | 166 | sub, 167 | sup { 168 | position: relative; 169 | font-size: 0.75em; 170 | line-height: 0; 171 | vertical-align: baseline; 172 | } 173 | 174 | sub { 175 | bottom: -0.25em; 176 | } 177 | 178 | sup { 179 | top: -0.5em; 180 | } 181 | 182 | a { 183 | color: #0d6efd; 184 | text-decoration: underline; 185 | } 186 | a:hover { 187 | color: #0a58ca; 188 | } 189 | 190 | a:not([href]):not([class]), a:not([href]):not([class]):hover { 191 | color: inherit; 192 | text-decoration: none; 193 | } 194 | 195 | pre, 196 | code, 197 | kbd, 198 | samp { 199 | font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; 200 | font-size: 1em; 201 | direction: ltr /* rtl:ignore */; 202 | unicode-bidi: bidi-override; 203 | } 204 | 205 | pre { 206 | display: block; 207 | margin-top: 0; 208 | margin-bottom: 1rem; 209 | overflow: auto; 210 | font-size: 0.875em; 211 | } 212 | pre code { 213 | font-size: inherit; 214 | color: inherit; 215 | word-break: normal; 216 | } 217 | 218 | code { 219 | font-size: 0.875em; 220 | color: #d63384; 221 | word-wrap: break-word; 222 | } 223 | a > code { 224 | color: inherit; 225 | } 226 | 227 | kbd { 228 | padding: 0.2rem 0.4rem; 229 | font-size: 0.875em; 230 | color: #fff; 231 | background-color: #212529; 232 | border-radius: 0.2rem; 233 | } 234 | kbd kbd { 235 | padding: 0; 236 | font-size: 1em; 237 | font-weight: 700; 238 | } 239 | 240 | figure { 241 | margin: 0 0 1rem; 242 | } 243 | 244 | img, 245 | svg { 246 | vertical-align: middle; 247 | } 248 | 249 | table { 250 | caption-side: bottom; 251 | border-collapse: collapse; 252 | } 253 | 254 | caption { 255 | padding-top: 0.5rem; 256 | padding-bottom: 0.5rem; 257 | color: #6c757d; 258 | text-align: left; 259 | } 260 | 261 | th { 262 | text-align: inherit; 263 | text-align: -webkit-match-parent; 264 | } 265 | 266 | thead, 267 | tbody, 268 | tfoot, 269 | tr, 270 | td, 271 | th { 272 | border-color: inherit; 273 | border-style: solid; 274 | border-width: 0; 275 | } 276 | 277 | label { 278 | display: inline-block; 279 | } 280 | 281 | button { 282 | border-radius: 0; 283 | } 284 | 285 | button:focus:not(:focus-visible) { 286 | outline: 0; 287 | } 288 | 289 | input, 290 | button, 291 | select, 292 | optgroup, 293 | textarea { 294 | margin: 0; 295 | font-family: inherit; 296 | font-size: inherit; 297 | line-height: inherit; 298 | } 299 | 300 | button, 301 | select { 302 | text-transform: none; 303 | } 304 | 305 | [role=button] { 306 | cursor: pointer; 307 | } 308 | 309 | select { 310 | word-wrap: normal; 311 | } 312 | 313 | [list]::-webkit-calendar-picker-indicator { 314 | display: none; 315 | } 316 | 317 | button, 318 | [type=button], 319 | [type=reset], 320 | [type=submit] { 321 | -webkit-appearance: button; 322 | } 323 | button:not(:disabled), 324 | [type=button]:not(:disabled), 325 | [type=reset]:not(:disabled), 326 | [type=submit]:not(:disabled) { 327 | cursor: pointer; 328 | } 329 | 330 | ::-moz-focus-inner { 331 | padding: 0; 332 | border-style: none; 333 | } 334 | 335 | textarea { 336 | resize: vertical; 337 | } 338 | 339 | fieldset { 340 | min-width: 0; 341 | padding: 0; 342 | margin: 0; 343 | border: 0; 344 | } 345 | 346 | legend { 347 | float: left; 348 | width: 100%; 349 | padding: 0; 350 | margin-bottom: 0.5rem; 351 | font-size: calc(1.275rem + 0.3vw); 352 | line-height: inherit; 353 | } 354 | @media (min-width: 1200px) { 355 | legend { 356 | font-size: 1.5rem; 357 | } 358 | } 359 | legend + * { 360 | clear: left; 361 | } 362 | 363 | ::-webkit-datetime-edit-fields-wrapper, 364 | ::-webkit-datetime-edit-text, 365 | ::-webkit-datetime-edit-minute, 366 | ::-webkit-datetime-edit-hour-field, 367 | ::-webkit-datetime-edit-day-field, 368 | ::-webkit-datetime-edit-month-field, 369 | ::-webkit-datetime-edit-year-field { 370 | padding: 0; 371 | } 372 | 373 | ::-webkit-inner-spin-button { 374 | height: auto; 375 | } 376 | 377 | [type=search] { 378 | outline-offset: -2px; 379 | -webkit-appearance: textfield; 380 | } 381 | 382 | /* rtl:raw: 383 | [type="tel"], 384 | [type="url"], 385 | [type="email"], 386 | [type="number"] { 387 | direction: ltr; 388 | } 389 | */ 390 | ::-webkit-search-decoration { 391 | -webkit-appearance: none; 392 | } 393 | 394 | ::-webkit-color-swatch-wrapper { 395 | padding: 0; 396 | } 397 | 398 | ::file-selector-button { 399 | 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(https://github.com/necolas/normalize.css/blob/master/LICENSE.md) 7 | */*,::after,::before{box-sizing:border-box}@media (prefers-reduced-motion:no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans","Liberation Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;background-color:#fff;-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:transparent}[tabindex="-1"]:focus:not(:focus-visible){outline:0!important}hr{margin:1rem 0;color:inherit;background-color:currentColor;border:0;opacity:.25}hr:not([size]){height:1px}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1{font-size:calc(1.375rem + 1.5vw)}@media (min-width:1200px){h1{font-size:2.5rem}}h2{font-size:calc(1.325rem + .9vw)}@media (min-width:1200px){h2{font-size:2rem}}h3{font-size:calc(1.3rem + 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-------------------------------------------------------------------------------- 1 | /*! 2 | * Bootstrap Reboot v5.0.0-beta2 (https://getbootstrap.com/) 3 | * Copyright 2011-2021 The Bootstrap Authors 4 | * Copyright 2011-2021 Twitter, Inc. 5 | * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) 6 | * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md) 7 | */ 8 | *, 9 | *::before, 10 | *::after { 11 | box-sizing: border-box; 12 | } 13 | 14 | @media (prefers-reduced-motion: no-preference) { 15 | :root { 16 | scroll-behavior: smooth; 17 | } 18 | } 19 | 20 | body { 21 | margin: 0; 22 | font-family: system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", "Liberation Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; 23 | font-size: 1rem; 24 | font-weight: 400; 25 | line-height: 1.5; 26 | color: #212529; 27 | background-color: #fff; 28 | 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@media (min-width: 1200px) { 86 | h4 { 87 | font-size: 1.5rem; 88 | } 89 | } 90 | 91 | h5 { 92 | font-size: 1.25rem; 93 | } 94 | 95 | h6 { 96 | font-size: 1rem; 97 | } 98 | 99 | p { 100 | margin-top: 0; 101 | margin-bottom: 1rem; 102 | } 103 | 104 | abbr[title], 105 | abbr[data-bs-original-title] { 106 | text-decoration: underline; 107 | -webkit-text-decoration: underline dotted; 108 | text-decoration: underline dotted; 109 | cursor: help; 110 | -webkit-text-decoration-skip-ink: none; 111 | text-decoration-skip-ink: none; 112 | } 113 | 114 | address { 115 | margin-bottom: 1rem; 116 | font-style: normal; 117 | line-height: inherit; 118 | } 119 | 120 | ol, 121 | ul { 122 | padding-right: 2rem; 123 | } 124 | 125 | ol, 126 | ul, 127 | dl { 128 | margin-top: 0; 129 | margin-bottom: 1rem; 130 | } 131 | 132 | ol ol, 133 | ul ul, 134 | ol ul, 135 | ul ol { 136 | margin-bottom: 0; 137 | } 138 | 139 | dt { 140 | font-weight: 700; 141 | } 142 | 143 | dd { 144 | margin-bottom: 0.5rem; 145 | margin-right: 0; 146 | } 147 | 148 | blockquote { 149 | margin: 0 0 1rem; 150 | } 151 | 152 | b, 153 | strong { 154 | font-weight: bolder; 155 | } 156 | 157 | small { 158 | font-size: 0.875em; 159 | } 160 | 161 | mark { 162 | padding: 0.2em; 163 | background-color: #fcf8e3; 164 | } 165 | 166 | sub, 167 | sup { 168 | position: relative; 169 | font-size: 0.75em; 170 | line-height: 0; 171 | vertical-align: baseline; 172 | } 173 | 174 | sub { 175 | bottom: -0.25em; 176 | } 177 | 178 | sup { 179 | top: -0.5em; 180 | } 181 | 182 | a { 183 | color: #0d6efd; 184 | text-decoration: underline; 185 | } 186 | a:hover { 187 | color: #0a58ca; 188 | } 189 | 190 | a:not([href]):not([class]), a:not([href]):not([class]):hover { 191 | color: inherit; 192 | text-decoration: none; 193 | } 194 | 195 | pre, 196 | code, 197 | kbd, 198 | samp { 199 | font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; 200 | font-size: 1em; 201 | direction: ltr ; 202 | unicode-bidi: bidi-override; 203 | } 204 | 205 | pre { 206 | display: block; 207 | margin-top: 0; 208 | margin-bottom: 1rem; 209 | overflow: auto; 210 | font-size: 0.875em; 211 | } 212 | pre code { 213 | font-size: inherit; 214 | color: inherit; 215 | word-break: normal; 216 | } 217 | 218 | code { 219 | font-size: 0.875em; 220 | color: #d63384; 221 | word-wrap: break-word; 222 | } 223 | a > code { 224 | color: inherit; 225 | } 226 | 227 | kbd { 228 | padding: 0.2rem 0.4rem; 229 | font-size: 0.875em; 230 | color: #fff; 231 | background-color: #212529; 232 | border-radius: 0.2rem; 233 | } 234 | kbd kbd { 235 | padding: 0; 236 | font-size: 1em; 237 | font-weight: 700; 238 | } 239 | 240 | figure { 241 | margin: 0 0 1rem; 242 | } 243 | 244 | img, 245 | svg { 246 | vertical-align: middle; 247 | } 248 | 249 | table { 250 | caption-side: bottom; 251 | border-collapse: collapse; 252 | } 253 | 254 | caption { 255 | padding-top: 0.5rem; 256 | padding-bottom: 0.5rem; 257 | color: #6c757d; 258 | text-align: right; 259 | } 260 | 261 | th { 262 | text-align: inherit; 263 | text-align: -webkit-match-parent; 264 | } 265 | 266 | thead, 267 | tbody, 268 | tfoot, 269 | tr, 270 | td, 271 | th { 272 | border-color: inherit; 273 | border-style: solid; 274 | border-width: 0; 275 | } 276 | 277 | label { 278 | display: inline-block; 279 | } 280 | 281 | button { 282 | border-radius: 0; 283 | } 284 | 285 | button:focus:not(:focus-visible) { 286 | outline: 0; 287 | } 288 | 289 | input, 290 | button, 291 | select, 292 | optgroup, 293 | textarea { 294 | margin: 0; 295 | font-family: inherit; 296 | font-size: inherit; 297 | line-height: inherit; 298 | } 299 | 300 | button, 301 | select { 302 | text-transform: none; 303 | } 304 | 305 | [role=button] { 306 | cursor: pointer; 307 | } 308 | 309 | select { 310 | word-wrap: normal; 311 | } 312 | 313 | [list]::-webkit-calendar-picker-indicator { 314 | display: none; 315 | } 316 | 317 | button, 318 | [type=button], 319 | [type=reset], 320 | [type=submit] { 321 | -webkit-appearance: button; 322 | } 323 | button:not(:disabled), 324 | [type=button]:not(:disabled), 325 | [type=reset]:not(:disabled), 326 | [type=submit]:not(:disabled) { 327 | cursor: pointer; 328 | } 329 | 330 | ::-moz-focus-inner { 331 | padding: 0; 332 | border-style: none; 333 | } 334 | 335 | textarea { 336 | resize: vertical; 337 | } 338 | 339 | fieldset { 340 | min-width: 0; 341 | padding: 0; 342 | margin: 0; 343 | border: 0; 344 | } 345 | 346 | legend { 347 | float: right; 348 | width: 100%; 349 | padding: 0; 350 | margin-bottom: 0.5rem; 351 | font-size: calc(1.275rem + 0.3vw); 352 | line-height: inherit; 353 | } 354 | @media (min-width: 1200px) { 355 | legend { 356 | font-size: 1.5rem; 357 | } 358 | } 359 | legend + * { 360 | clear: right; 361 | } 362 | 363 | ::-webkit-datetime-edit-fields-wrapper, 364 | ::-webkit-datetime-edit-text, 365 | ::-webkit-datetime-edit-minute, 366 | ::-webkit-datetime-edit-hour-field, 367 | ::-webkit-datetime-edit-day-field, 368 | ::-webkit-datetime-edit-month-field, 369 | ::-webkit-datetime-edit-year-field { 370 | padding: 0; 371 | } 372 | 373 | ::-webkit-inner-spin-button { 374 | height: auto; 375 | } 376 | 377 | [type=search] { 378 | outline-offset: -2px; 379 | -webkit-appearance: textfield; 380 | } 381 | 382 | [type="tel"], 383 | [type="url"], 384 | [type="email"], 385 | [type="number"] { 386 | direction: ltr; 387 | } 388 | ::-webkit-search-decoration { 389 | -webkit-appearance: none; 390 | } 391 | 392 | ::-webkit-color-swatch-wrapper { 393 | padding: 0; 394 | } 395 | 396 | ::file-selector-button { 397 | font: inherit; 398 | } 399 | 400 | ::-webkit-file-upload-button { 401 | font: inherit; 402 | -webkit-appearance: button; 403 | } 404 | 405 | output { 406 | display: inline-block; 407 | } 408 | 409 | iframe { 410 | border: 0; 411 | } 412 | 413 | summary { 414 | display: list-item; 415 | cursor: pointer; 416 | } 417 | 418 | progress { 419 | vertical-align: baseline; 420 | } 421 | 422 | [hidden] { 423 | display: none !important; 424 | } 425 | /*# sourceMappingURL=bootstrap-reboot.rtl.css.map */ -------------------------------------------------------------------------------- /static/vendor/bootstrap/css/bootstrap-reboot.rtl.min.css: -------------------------------------------------------------------------------- 1 | /*! 2 | * Bootstrap Reboot v5.0.0-beta2 (https://getbootstrap.com/) 3 | * Copyright 2011-2021 The Bootstrap Authors 4 | * Copyright 2011-2021 Twitter, Inc. 5 | * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) 6 | * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md) 7 | */*,::after,::before{box-sizing:border-box}@media (prefers-reduced-motion:no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans","Liberation Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color 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1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:#6c757d;text-align:right}th{text-align:inherit;text-align:-webkit-match-parent}tbody,td,tfoot,th,thead,tr{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}[list]::-webkit-calendar-picker-indicator{display:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled),button:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:right;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + .3vw);line-height:inherit}@media (min-width:1200px){legend{font-size:1.5rem}}legend+*{clear:right}::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-text,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:textfield}[type=email],[type=number],[type=tel],[type=url]{direction:ltr}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none!important} 8 | /*# sourceMappingURL=bootstrap-reboot.rtl.min.css.map */ -------------------------------------------------------------------------------- /static/vendor/boxicons/css/animations.css: -------------------------------------------------------------------------------- 1 | @-webkit-keyframes spin 2 | { 3 | 0% 4 | { 5 | -webkit-transform: rotate(0); 6 | transform: rotate(0); 7 | } 8 | 100% 9 | { 10 | -webkit-transform: rotate(359deg); 11 | transform: rotate(359deg); 12 | } 13 | } 14 | @keyframes spin 15 | { 16 | 0% 17 | { 18 | -webkit-transform: rotate(0); 19 | transform: rotate(0); 20 | } 21 | 100% 22 | { 23 | -webkit-transform: rotate(359deg); 24 | transform: rotate(359deg); 25 | } 26 | } 27 | @-webkit-keyframes burst 28 | { 29 | 0% 30 | { 31 | -webkit-transform: scale(1); 32 | transform: scale(1); 33 | 34 | opacity: 1; 35 | } 36 | 90% 37 | { 38 | -webkit-transform: scale(1.5); 39 | transform: scale(1.5); 40 | 41 | opacity: 0; 42 | } 43 | } 44 | @keyframes burst 45 | { 46 | 0% 47 | { 48 | -webkit-transform: scale(1); 49 | transform: scale(1); 50 | 51 | opacity: 1; 52 | } 53 | 90% 54 | { 55 | -webkit-transform: scale(1.5); 56 | transform: scale(1.5); 57 | 58 | opacity: 0; 59 | } 60 | } 61 | @-webkit-keyframes flashing 62 | { 63 | 0% 64 | { 65 | opacity: 1; 66 | } 67 | 45% 68 | { 69 | opacity: 0; 70 | } 71 | 90% 72 | { 73 | opacity: 1; 74 | } 75 | } 76 | @keyframes flashing 77 | { 78 | 0% 79 | { 80 | opacity: 1; 81 | } 82 | 45% 83 | { 84 | opacity: 0; 85 | } 86 | 90% 87 | { 88 | opacity: 1; 89 | } 90 | } 91 | @-webkit-keyframes fade-left 92 | { 93 | 0% 94 | { 95 | -webkit-transform: translateX(0); 96 | transform: translateX(0); 97 | 98 | opacity: 1; 99 | } 100 | 75% 101 | { 102 | -webkit-transform: translateX(-20px); 103 | transform: translateX(-20px); 104 | 105 | opacity: 0; 106 | } 107 | } 108 | @keyframes fade-left 109 | { 110 | 0% 111 | { 112 | -webkit-transform: translateX(0); 113 | transform: translateX(0); 114 | 115 | opacity: 1; 116 | } 117 | 75% 118 | { 119 | -webkit-transform: translateX(-20px); 120 | transform: translateX(-20px); 121 | 122 | opacity: 0; 123 | } 124 | } 125 | @-webkit-keyframes fade-right 126 | { 127 | 0% 128 | { 129 | -webkit-transform: translateX(0); 130 | transform: translateX(0); 131 | 132 | opacity: 1; 133 | } 134 | 75% 135 | { 136 | -webkit-transform: translateX(20px); 137 | transform: translateX(20px); 138 | 139 | opacity: 0; 140 | } 141 | } 142 | @keyframes fade-right 143 | { 144 | 0% 145 | { 146 | -webkit-transform: translateX(0); 147 | transform: translateX(0); 148 | 149 | opacity: 1; 150 | } 151 | 75% 152 | { 153 | -webkit-transform: translateX(20px); 154 | transform: translateX(20px); 155 | 156 | opacity: 0; 157 | } 158 | } 159 | @-webkit-keyframes fade-up 160 | { 161 | 0% 162 | { 163 | -webkit-transform: translateY(0); 164 | transform: translateY(0); 165 | 166 | opacity: 1; 167 | } 168 | 75% 169 | { 170 | -webkit-transform: translateY(-20px); 171 | transform: translateY(-20px); 172 | 173 | opacity: 0; 174 | } 175 | } 176 | @keyframes fade-up 177 | { 178 | 0% 179 | { 180 | -webkit-transform: translateY(0); 181 | transform: translateY(0); 182 | 183 | opacity: 1; 184 | } 185 | 75% 186 | { 187 | -webkit-transform: translateY(-20px); 188 | transform: translateY(-20px); 189 | 190 | opacity: 0; 191 | } 192 | } 193 | @-webkit-keyframes fade-down 194 | { 195 | 0% 196 | { 197 | -webkit-transform: translateY(0); 198 | transform: translateY(0); 199 | 200 | opacity: 1; 201 | } 202 | 75% 203 | { 204 | -webkit-transform: translateY(20px); 205 | transform: translateY(20px); 206 | 207 | opacity: 0; 208 | } 209 | } 210 | @keyframes fade-down 211 | { 212 | 0% 213 | { 214 | -webkit-transform: translateY(0); 215 | transform: translateY(0); 216 | 217 | opacity: 1; 218 | } 219 | 75% 220 | { 221 | -webkit-transform: translateY(20px); 222 | transform: translateY(20px); 223 | 224 | opacity: 0; 225 | } 226 | } 227 | @-webkit-keyframes tada 228 | { 229 | from 230 | { 231 | -webkit-transform: scale3d(1, 1, 1); 232 | transform: scale3d(1, 1, 1); 233 | } 234 | 235 | 10%, 236 | 20% 237 | { 238 | -webkit-transform: scale3d(.95, .95, .95) rotate3d(0, 0, 1, -10deg); 239 | transform: scale3d(.95, .95, .95) rotate3d(0, 0, 1, -10deg); 240 | } 241 | 242 | 30%, 243 | 50%, 244 | 70%, 245 | 90% 246 | { 247 | -webkit-transform: scale3d(1, 1, 1) rotate3d(0, 0, 1, 10deg); 248 | transform: scale3d(1, 1, 1) rotate3d(0, 0, 1, 10deg); 249 | } 250 | 251 | 40%, 252 | 60%, 253 | 80% 254 | { 255 | -webkit-transform: scale3d(1, 1, 1) rotate3d(0, 0, 1, -10deg); 256 | transform: scale3d(1, 1, 1) rotate3d(0, 0, 1, -10deg); 257 | } 258 | 259 | to 260 | { 261 | -webkit-transform: scale3d(1, 1, 1); 262 | transform: scale3d(1, 1, 1); 263 | } 264 | } 265 | 266 | @keyframes tada 267 | { 268 | from 269 | { 270 | -webkit-transform: scale3d(1, 1, 1); 271 | transform: scale3d(1, 1, 1); 272 | } 273 | 274 | 10%, 275 | 20% 276 | { 277 | -webkit-transform: scale3d(.95, .95, .95) rotate3d(0, 0, 1, -10deg); 278 | transform: scale3d(.95, .95, .95) rotate3d(0, 0, 1, -10deg); 279 | } 280 | 281 | 30%, 282 | 50%, 283 | 70%, 284 | 90% 285 | { 286 | -webkit-transform: scale3d(1, 1, 1) rotate3d(0, 0, 1, 10deg); 287 | transform: scale3d(1, 1, 1) rotate3d(0, 0, 1, 10deg); 288 | } 289 | 290 | 40%, 291 | 60%, 292 | 80% 293 | { 294 | -webkit-transform: rotate3d(0, 0, 1, -10deg); 295 | transform: rotate3d(0, 0, 1, -10deg); 296 | } 297 | 298 | to 299 | { 300 | -webkit-transform: scale3d(1, 1, 1); 301 | transform: scale3d(1, 1, 1); 302 | } 303 | } 304 | .bx-spin 305 | { 306 | -webkit-animation: spin 2s linear infinite; 307 | animation: spin 2s linear infinite; 308 | } 309 | .bx-spin-hover:hover 310 | { 311 | -webkit-animation: spin 2s linear infinite; 312 | animation: spin 2s linear infinite; 313 | } 314 | 315 | .bx-tada 316 | { 317 | -webkit-animation: tada 1.5s ease infinite; 318 | animation: tada 1.5s ease infinite; 319 | } 320 | .bx-tada-hover:hover 321 | { 322 | -webkit-animation: tada 1.5s ease infinite; 323 | animation: tada 1.5s ease infinite; 324 | } 325 | 326 | .bx-flashing 327 | { 328 | -webkit-animation: flashing 1.5s infinite linear; 329 | animation: flashing 1.5s infinite linear; 330 | } 331 | .bx-flashing-hover:hover 332 | { 333 | -webkit-animation: flashing 1.5s infinite linear; 334 | animation: flashing 1.5s infinite linear; 335 | } 336 | 337 | .bx-burst 338 | { 339 | -webkit-animation: burst 1.5s infinite linear; 340 | animation: burst 1.5s infinite linear; 341 | } 342 | .bx-burst-hover:hover 343 | { 344 | -webkit-animation: burst 1.5s infinite linear; 345 | animation: burst 1.5s infinite linear; 346 | } 347 | .bx-fade-up 348 | { 349 | -webkit-animation: fade-up 1.5s infinite linear; 350 | animation: fade-up 1.5s infinite linear; 351 | } 352 | .bx-fade-up-hover:hover 353 | { 354 | -webkit-animation: fade-up 1.5s infinite linear; 355 | animation: fade-up 1.5s infinite linear; 356 | } 357 | .bx-fade-down 358 | { 359 | -webkit-animation: fade-down 1.5s infinite linear; 360 | animation: fade-down 1.5s infinite linear; 361 | } 362 | .bx-fade-down-hover:hover 363 | { 364 | -webkit-animation: fade-down 1.5s infinite linear; 365 | animation: fade-down 1.5s infinite linear; 366 | } 367 | .bx-fade-left 368 | { 369 | -webkit-animation: fade-left 1.5s infinite linear; 370 | animation: fade-left 1.5s infinite linear; 371 | } 372 | .bx-fade-left-hover:hover 373 | { 374 | -webkit-animation: fade-left 1.5s infinite linear; 375 | animation: fade-left 1.5s infinite linear; 376 | } 377 | .bx-fade-right 378 | { 379 | -webkit-animation: fade-right 1.5s infinite linear; 380 | animation: fade-right 1.5s infinite linear; 381 | } 382 | .bx-fade-right-hover:hover 383 | { 384 | -webkit-animation: fade-right 1.5s infinite linear; 385 | animation: fade-right 1.5s infinite linear; 386 | } -------------------------------------------------------------------------------- /static/vendor/boxicons/css/transformations.css: -------------------------------------------------------------------------------- 1 | .bx-rotate-90 2 | { 3 | transform: rotate(90deg); 4 | 5 | -ms-filter: 'progid:DXImageTransform.Microsoft.BasicImage(rotation=1)'; 6 | } 7 | .bx-rotate-180 8 | { 9 | transform: rotate(180deg); 10 | 11 | -ms-filter: 'progid:DXImageTransform.Microsoft.BasicImage(rotation=2)'; 12 | } 13 | .bx-rotate-270 14 | { 15 | transform: rotate(270deg); 16 | 17 | -ms-filter: 'progid:DXImageTransform.Microsoft.BasicImage(rotation=3)'; 18 | } 19 | .bx-flip-horizontal 20 | { 21 | transform: scaleX(-1); 22 | 23 | -ms-filter: 'progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)'; 24 | } 25 | .bx-flip-vertical 26 | { 27 | transform: scaleY(-1); 28 | 29 | -ms-filter: 'progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)'; 30 | } 31 | -------------------------------------------------------------------------------- /static/vendor/boxicons/fonts/boxicons.eot: -------------------------------------------------------------------------------- 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gslideInRight{from{opacity:0;visibility:visible;-webkit-transform:translate3d(60%,0,0);transform:translate3d(60%,0,0)}to{-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0);opacity:1}}@keyframes gslideInRight{from{opacity:0;visibility:visible;-webkit-transform:translate3d(60%,0,0);transform:translate3d(60%,0,0)}to{-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0);opacity:1}}@-webkit-keyframes gslideOutRight{from{opacity:1;visibility:visible;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}to{-webkit-transform:translate3d(60%,0,0);transform:translate3d(60%,0,0);opacity:0}}@keyframes gslideOutRight{from{opacity:1;visibility:visible;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}to{-webkit-transform:translate3d(60%,0,0);transform:translate3d(60%,0,0);opacity:0}}@-webkit-keyframes gzoomIn{from{opacity:0;-webkit-transform:scale3d(.3,.3,.3);transform:scale3d(.3,.3,.3)}to{opacity:1}}@keyframes 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.ginner-container.desc-left .gslide-image{-webkit-box-ordinal-group:2;-ms-flex-order:1;order:1}.gslide-image img{max-height:97vh;max-width:100%}.gslide-image img.zoomable{cursor:-webkit-zoom-in;cursor:zoom-in}.zoomed .gslide-image img.zoomable{cursor:-webkit-grab;cursor:grab}.gslide-inline{max-height:95vh}.gslide-external{max-height:100vh}.gslide-description.description-left,.gslide-description.description-right{max-width:275px}.glightbox-open{height:auto}.goverlay{background:rgba(0,0,0,.92)}.glightbox-clean .gslide-media{-webkit-box-shadow:1px 2px 9px 0 rgba(0,0,0,.65);box-shadow:1px 2px 9px 0 rgba(0,0,0,.65)}.glightbox-clean .description-left .gdesc-inner,.glightbox-clean .description-right .gdesc-inner{position:absolute;height:100%;overflow-y:auto}.glightbox-clean .gclose,.glightbox-clean .gnext,.glightbox-clean .gprev{background-color:rgba(0,0,0,.32)}.glightbox-clean .gclose:hover,.glightbox-clean .gnext:hover,.glightbox-clean .gprev:hover{background-color:rgba(0,0,0,.7)}.glightbox-clean .gprev{top:45%}.glightbox-clean .gnext{top:45%}}@media (min-width:992px){.glightbox-clean .gclose{opacity:.7;right:20px}}@media screen and (max-height:420px){.goverlay{background:#000}} -------------------------------------------------------------------------------- /static/vendor/php-email-form/validate.js: -------------------------------------------------------------------------------- 1 | /** 2 | * PHP Email Form Validation - v3.0 3 | * URL: https://bootstrapmade.com/php-email-form/ 4 | * Author: BootstrapMade.com 5 | */ 6 | (function () { 7 | "use strict"; 8 | 9 | let forms = document.querySelectorAll('.php-email-form'); 10 | 11 | forms.forEach( function(e) { 12 | e.addEventListener('submit', function(event) { 13 | event.preventDefault(); 14 | 15 | let thisForm = this; 16 | 17 | let action = thisForm.getAttribute('action'); 18 | let recaptcha = thisForm.getAttribute('data-recaptcha-site-key'); 19 | 20 | if( ! action ) { 21 | displayError(thisForm, 'The form action property is not set!') 22 | return; 23 | } 24 | thisForm.querySelector('.loading').classList.add('d-block'); 25 | thisForm.querySelector('.error-message').classList.remove('d-block'); 26 | thisForm.querySelector('.sent-message').classList.remove('d-block'); 27 | 28 | let formData = new FormData( thisForm ); 29 | 30 | if ( recaptcha ) { 31 | if(typeof grecaptcha !== "undefined" ) { 32 | grecaptcha.ready(function() { 33 | try { 34 | grecaptcha.execute(recaptcha, {action: 'php_email_form_submit'}) 35 | .then(token => { 36 | formData.set('recaptcha-response', token); 37 | php_email_form_submit(thisForm, action, formData); 38 | }) 39 | } catch(error) { 40 | displayError(thisForm, error) 41 | } 42 | }); 43 | } else { 44 | displayError(thisForm, 'The reCaptcha javascript API url is not loaded!') 45 | } 46 | } else { 47 | php_email_form_submit(thisForm, action, formData); 48 | } 49 | }); 50 | }); 51 | 52 | function php_email_form_submit(thisForm, action, formData) { 53 | fetch(action, { 54 | method: 'POST', 55 | body: formData, 56 | headers: {'X-Requested-With': 'XMLHttpRequest'} 57 | }) 58 | .then(response => { 59 | if( response.ok ) { 60 | return response.text() 61 | } else { 62 | throw new Error(`${response.status} ${response.statusText} ${response.url}`); 63 | } 64 | }) 65 | .then(data => { 66 | thisForm.querySelector('.loading').classList.remove('d-block'); 67 | if (data.trim() == 'OK') { 68 | thisForm.querySelector('.sent-message').classList.add('d-block'); 69 | thisForm.reset(); 70 | } else { 71 | throw new Error(data ? data : 'Form submission failed and no error message returned from: ' + action); 72 | } 73 | }) 74 | .catch((error) => { 75 | displayError(thisForm, error); 76 | }); 77 | } 78 | 79 | function displayError(thisForm, error) { 80 | thisForm.querySelector('.loading').classList.remove('d-block'); 81 | thisForm.querySelector('.error-message').innerHTML = error; 82 | thisForm.querySelector('.error-message').classList.add('d-block'); 83 | } 84 | 85 | })(); 86 | -------------------------------------------------------------------------------- /static/vendor/swiper/swiper-bundle.min.css: -------------------------------------------------------------------------------- 1 | /** 2 | * Swiper 6.4.11 3 | * Most modern mobile touch slider and framework with hardware accelerated transitions 4 | * https://swiperjs.com 5 | * 6 | * Copyright 2014-2021 Vladimir Kharlampidi 7 | * 8 | * Released under the MIT License 9 | * 10 | * Released on: February 6, 2021 11 | */ 12 | 13 | @font-face{font-family:swiper-icons;src:url('data:application/font-woff;charset=utf-8;base64, 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AI Driven Diabetic Retinopathy Diagnosis using ANN & RCNN Classification

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AI Driven Diabetic Retinopathy Diagnosis using ANN & RCNN Classification

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Total Retinopathy Classes

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Accuracy Plot of F-RCNN

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Accuracy Plot

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AI Driven Diabetic Retinopathy Diagnosis using ANN & RCNN Classification

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Abstract

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120 | Diabetes is a metabolic disorder that results in a retinal complication 121 | called diabetic retinopathy (DR) which is one of the four main reasons for 122 | sightlessness all over the globe. DR usually has no clear symptoms before the 123 | onset, thus making disease identication a challenging task. The healthcare 124 | industry may face unfavorable consequences if the gap in identifying DR is not 125 | lled with effective automation. Thus, our objective is to develop an automatic 126 | and cost-effective method for classifying DR samples. In this work, we present 127 | a custom Faster-RCNN technique for the recognition and classication of 128 | DR lesions from retinal images. After pre-processing, we generate the annotations of the dataset which is required for model training. Then, introduce 129 | DenseNet-65 at the feature extraction level of Faster-RCNN to compute 130 | the representative set of key points. Finally, the ANN & Faster-RCNN combines localizes and 131 | classifies the input sample & detect the likelihood of having the disease, hence predict with DR class. 132 |

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©www.drdiagnosis.com
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DR Classifier

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AI Driven Diabetic Retinopathy Diagnosis using ANN & RCNN Classification

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