Tabular data Train and Testing for Breast Cancer dataset
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commit
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from django.db import models
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from django.db import models
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414
base/static/css/style.css
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414
base/static/css/style.css
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@import url("https://fonts.googleapis.com/css2?family=Poppins:wght@200;300;400;500;600;700&display=swap");
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.container-fluid {
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padding-bottom: 100px;
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}
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* {
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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font-family: "Poppins", sans-serif;
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}
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nav {
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position: fixed;
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z-index: 99;
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width: 100%;
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background: #242526;
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}
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nav .wrapper {
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margin-bottom: 10px;
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position: relative;
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max-width: 1300px;
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padding: 0px 30px;
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height: 70px;
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line-height: 70px;
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margin: auto;
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display: flex;
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align-items: center;
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justify-content: space-between;
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}
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.wrapper .logo a {
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color: #f2f2f2;
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font-size: 30px;
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font-weight: 600;
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text-decoration: none;
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}
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.wrapper .nav-links {
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display: inline-flex;
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}
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.nav-links li {
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list-style: none;
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}
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.nav-links li a {
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color: #f2f2f2;
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text-decoration: none;
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font-size: 18px;
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font-weight: 500;
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padding: 9px 15px;
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border-radius: 5px;
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transition: all 0.3s ease;
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}
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.nav-links li a:hover {
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background: #3a3b3c;
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}
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.nav-links .mobile-item {
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display: none;
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}
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.nav-links .drop-menu {
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position: absolute;
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background: #242526;
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width: 180px;
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line-height: 45px;
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top: 85px;
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opacity: 0;
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visibility: hidden;
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box-shadow: 0 6px 10px rgba(0, 0, 0, 0.15);
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}
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.nav-links li:hover .drop-menu,
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.nav-links li:hover .mega-box {
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transition: all 0.3s ease;
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top: 70px;
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opacity: 1;
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visibility: visible;
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}
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.drop-menu li a {
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width: 100%;
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display: block;
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padding: 0 0 0 15px;
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font-weight: 400;
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border-radius: 0px;
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}
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.mega-box {
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position: absolute;
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left: 0;
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width: 100%;
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padding: 0 30px;
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top: 85px;
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opacity: 0;
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visibility: hidden;
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}
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.mega-box .content {
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background: #242526;
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padding: 25px 20px;
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display: flex;
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width: 100%;
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justify-content: space-between;
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box-shadow: 0 6px 10px rgba(0, 0, 0, 0.15);
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}
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.mega-box .content .row {
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width: calc(25% - 30px);
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line-height: 45px;
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}
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.content .row img {
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width: 100%;
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height: 100%;
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object-fit: cover;
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}
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.content .row header {
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color: #f2f2f2;
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font-size: 20px;
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font-weight: 500;
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}
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.content .row .mega-links {
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margin-left: -40px;
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border-left: 1px solid rgba(255, 255, 255, 0.09);
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}
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.row .mega-links li {
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padding: 0 20px;
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}
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.row .mega-links li a {
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padding: 0px;
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padding: 0 20px;
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color: #d9d9d9;
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font-size: 17px;
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display: block;
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}
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.row .mega-links li a:hover {
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color: #f2f2f2;
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}
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.wrapper .btn {
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color: #fff;
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font-size: 20px;
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cursor: pointer;
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display: none;
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}
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.wrapper .btn.close-btn {
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position: absolute;
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right: 30px;
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top: 10px;
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}
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@media screen and (max-width: 970px) {
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.wrapper .btn {
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display: block;
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}
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.wrapper .nav-links {
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position: fixed;
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height: 100vh;
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width: 100%;
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max-width: 350px;
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top: 0;
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left: -100%;
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background: #171f27;
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display: block;
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padding: 50px 10px;
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line-height: 50px;
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overflow-y: auto;
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box-shadow: 0px 15px 15px rgba(0, 0, 0, 0.18);
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transition: all 0.3s ease;
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}
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/* custom scroll bar */
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::-webkit-scrollbar {
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width: 10px;
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}
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::-webkit-scrollbar-track {
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background: #242526;
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}
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::-webkit-scrollbar-thumb {
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background: #3a3b3c;
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}
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#menu-btn:checked ~ .nav-links {
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left: 0%;
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}
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#menu-btn:checked ~ .btn.menu-btn {
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display: none;
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}
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#close-btn:checked ~ .btn.menu-btn {
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display: block;
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}
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.nav-links li {
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margin: 15px 10px;
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}
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.nav-links li a {
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padding: 0 20px;
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display: block;
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font-size: 20px;
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}
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.nav-links .drop-menu {
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position: static;
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opacity: 1;
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top: 65px;
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visibility: visible;
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padding-left: 20px;
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width: 100%;
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max-height: 0px;
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overflow: hidden;
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box-shadow: none;
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transition: all 0.3s ease;
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}
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#showDrop:checked ~ .drop-menu,
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#showMega:checked ~ .mega-box {
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max-height: 100%;
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}
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.nav-links .desktop-item {
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display: none;
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}
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.nav-links .mobile-item {
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display: block;
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color: #f2f2f2;
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font-size: 20px;
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font-weight: 500;
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padding-left: 20px;
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cursor: pointer;
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border-radius: 5px;
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transition: all 0.3s ease;
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}
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.nav-links .mobile-item:hover {
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background: #3a3b3c;
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}
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.drop-menu li {
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margin: 0;
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}
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.drop-menu li a {
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border-radius: 5px;
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font-size: 18px;
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}
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.mega-box {
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position: static;
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top: 65px;
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opacity: 1;
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visibility: visible;
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padding: 0 20px;
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max-height: 0px;
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overflow: hidden;
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transition: all 0.3s ease;
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}
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.mega-box .content {
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box-shadow: none;
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flex-direction: column;
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padding: 20px 20px 0 20px;
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}
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.mega-box .content .row {
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width: 100%;
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margin-bottom: 15px;
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border-top: 1px solid rgba(255, 255, 255, 0.08);
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}
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.mega-box .content .row:nth-child(1),
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.mega-box .content .row:nth-child(2) {
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border-top: 0px;
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}
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.content .row .mega-links {
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border-left: 0px;
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padding-left: 15px;
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}
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.row .mega-links li {
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margin: 0;
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}
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.content .row header {
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font-size: 19px;
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}
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}
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nav input {
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display: none;
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}
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.body-text {
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position: absolute;
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top: 50%;
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left: 50%;
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transform: translate(-50%, -50%);
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width: 100%;
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text-align: center;
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padding: 0 30px;
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}
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.body-text div {
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font-size: 45px;
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font-weight: 600;
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}
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/* csv display */
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.scrollit {
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margin-top: 60px;
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overflow: auto;
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position: absolute;
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height: min-content;
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max-height: 40%;
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max-width: 35%;
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}
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.dataframe {
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font-size: 9pt;
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font-family: Arial;
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border-collapse: collapse;
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font-size: 0.9em;
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}
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.dataframe thead tr {
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text-align: left;
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font-weight: bold;
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}
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.dataframe th,
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.dataframe td {
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padding: 12px 15px;
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text-align: left;
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}
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.dataframe tbody tr {
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border-bottom: 1px solid #dddddd;
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}
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.dataframe tbody tr:nth-of-type(even) {
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background-color: #f3f3f3;
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}
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.dataframe tbody tr:last-of-type {
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border-bottom: 2px solid #009879;
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}
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/* plotly toolbar */
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.modebar {
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display: none !important;
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}
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/* button */
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/* CSS */
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.button-6 {
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align-items: center;
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background-color: #FFFFFF;
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border: 1px solid rgba(0, 0, 0, 0.1);
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border-radius: .25rem;
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box-shadow: rgba(0, 0, 0, 0.02) 0 1px 3px 0;
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box-sizing: border-box;
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color: rgba(0, 0, 0, 0.85);
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cursor: pointer;
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display: inline-flex;
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font-family: system-ui,-apple-system,system-ui,"Helvetica Neue",Helvetica,Arial,sans-serif;
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font-size: 16px;
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font-weight: 600;
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justify-content: center;
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line-height: 1.25;
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margin: 0;
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min-height: 3rem;
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padding: calc(.875rem - 1px) calc(1.5rem - 1px);
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position: relative;
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text-decoration: none;
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transition: all 250ms;
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user-select: none;
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-webkit-user-select: none;
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touch-action: manipulation;
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vertical-align: baseline;
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width: auto;
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}
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.button-6:hover,
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.button-6:focus {
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border-color: rgba(0, 0, 0, 0.15);
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box-shadow: rgba(0, 0, 0, 0.1) 0 4px 12px;
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color: rgba(0, 0, 0, 0.65);
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}
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.button-6:hover {
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transform: translateY(-1px);
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}
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.button-6:active {
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background-color: #F0F0F1;
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border-color: rgba(0, 0, 0, 0.15);
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box-shadow: rgba(0, 0, 0, 0.06) 0 2px 4px;
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color: rgba(0, 0, 0, 0.65);
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transform: translateY(0);
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}
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/* radio buttons */
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.radio-inputs {
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position: relative;
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display: flex;
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flex-wrap: wrap;
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border-radius: 0.5rem;
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background-color: #EEE;
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box-sizing: border-box;
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box-shadow: 0 0 0px 1px rgba(0, 0, 0, 0.06);
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padding: 0.25rem;
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width: 300px;
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font-size: 14px;
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}
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.radio-inputs .radio {
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flex: 1 1 auto;
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text-align: center;
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}
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.radio-inputs .radio input {
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display: none;
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}
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.radio-inputs .radio .name {
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display: flex;
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cursor: pointer;
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align-items: center;
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justify-content: center;
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border-radius: 0.5rem;
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border: none;
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padding: .5rem 0;
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color: rgba(51, 65, 85, 1);
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transition: all .15s ease-in-out;
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}
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.radio-inputs .radio input:checked + .name {
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background-color: #fff;
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font-weight: 600;
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}
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11
base/static/js/hide_seek.js
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base/static/js/hide_seek.js
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$(document).ready(function () {
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$('.trainTestButton').click(function() {
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if($("#train").is(':checked')){
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$("#train-me").show();
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$("#test-me").hide();
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}else{
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$("#train-me").hide();
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$("#test-me").show();
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}
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})
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});
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12
base/static/js/keep_scroll_on_load.js
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12
base/static/js/keep_scroll_on_load.js
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function setScreen() {
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var yScreen = localStorage.getItem("yPos");
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window.scrollTo(0, yScreen);
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}
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function setScroll() {
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var yScroll = window.pageYOffset;
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localStorage.setItem("yPos", yScroll);
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}
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function clearScreen() {
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localStorage.setItem("yPos", 0);
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window.scrollTo(0, 0);
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}
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@ -1,7 +0,0 @@
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$(document).ready( function()
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{
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$('#spinner').on('click', function()
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{
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$('body').addClass('busy');
|
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});
|
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});
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6
base/static/js/slider.js
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6
base/static/js/slider.js
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const value = document.querySelector("#value");
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const input = document.querySelector("#split_input");
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value.textContent = input.value;
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input.addEventListener("input", (event) => {
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value.textContent = event.target.value;
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});
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@ -1,25 +1,14 @@
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{% extends 'main.html' %}
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{% block content %}
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{% load static %}
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<div class="container-fluid">
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<div class="mx-auto p-4 text-center bg-white shadow-sm">
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<a href="/" >
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<h1>
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EXTREMUM
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</h1>
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</a>
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||||
</div>
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||||
|
||||
<br>
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||||
<br>
|
||||
|
||||
<div class="row">
|
||||
<div class="col d-flex justify-content-center">
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<div class="col-sm d-flex justify-content-center">
|
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<form action="{% url 'home' %}" id="csv" method="post" enctype="multipart/form-data">
|
||||
<label style="display:flex;
|
||||
flex-direction:column;
|
||||
align-items: center;">
|
||||
flex-direction:column;
|
||||
align-items: center;">
|
||||
<h3>
|
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<i class="fas fa-upload"></i> Import a file
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</h3>
|
||||
@ -36,11 +25,11 @@
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style="border: 1px solid green; padding:5px; border-radius: 2px; cursor: pointer;">
|
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</form>
|
||||
</div>
|
||||
<div class="col d-flex justify-content-center">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<form action="{% url 'home' %}" name="plot" method="POST" id="stats">
|
||||
<label style="display:flex;
|
||||
flex-direction:column;
|
||||
align-items: center;">
|
||||
flex-direction:column;
|
||||
align-items: center;">
|
||||
<h3>
|
||||
<i class="fa-solid fa-chart-simple"></i> Stats
|
||||
</h3>
|
||||
@ -66,62 +55,158 @@
|
||||
</form>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
<div class="row">
|
||||
<div class="col d-flex justify-content-center">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<div class="scrollit">
|
||||
{{ data_to_display|safe }}
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||||
</div>
|
||||
</div>
|
||||
<div class="col d-flex justify-content-center">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
{{ fig|safe }}
|
||||
</div>
|
||||
</div>
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||||
<br>
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||||
<br>
|
||||
<div class="row justify-content-center">
|
||||
<div class="col d-flex justify-content-center">
|
||||
<form action="{% url 'home' %}" method="POST">
|
||||
<label style="display:flex;
|
||||
flex-direction:column;
|
||||
align-items: center;">
|
||||
<h3>
|
||||
<i class="fa-solid fa-wand-magic-sparkles"></i> Training
|
||||
</h3>
|
||||
</div>
|
||||
<br>
|
||||
<br>
|
||||
<div class="container-fluid">
|
||||
<div class="row">
|
||||
<section>
|
||||
<label style="display:flex;
|
||||
flex-direction:column;
|
||||
align-items: center;">
|
||||
<h2>
|
||||
<i class="fas fa-cog"></i> Preprocessing
|
||||
</h2>
|
||||
</label>
|
||||
</section>
|
||||
</div>
|
||||
<br>
|
||||
<br>
|
||||
<div class="row">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<form action="{% url 'home' %}" method="POST" id="preprocess-form">
|
||||
{% csrf_token %}
|
||||
<div class="btn-toolbar" role="toolbar">
|
||||
<button type="submit" name="svm" style="margin:0;margin-left:16px;" class="button-6" role="button" >Support Vector Machine</button>
|
||||
<button type="submit" name="logit" style="margin:0;margin-left:16px;" class="button-6" role="button" >Logistic Regression</button>
|
||||
<button type="submit" name="xgb" style="margin:0;margin-left:16px;" class="button-6" role="button" >XGBoost</button>
|
||||
</div>
|
||||
<br>
|
||||
<div class="justify-content-center btn-toolbar" role="toolbar">
|
||||
<button type="submit" name="dt" style="margin:0;margin-left:16px;" class="button-6" role="button" >Decision Tree</button>
|
||||
<button type="submit" name="rt" style="margin:0;margin-left:16px;" class="button-6" role="button" >Random Forest</button>
|
||||
<div style="scale: 1.2;">
|
||||
<div class="form-check form-check-inline">
|
||||
<input class="form-check-input" type="checkbox" value="std" name="boxes" checked>
|
||||
<input class="form-check-input" type="checkbox" value="std" name="boxes">
|
||||
<label class="form-check-label" for="inlineCheckbox1">Standardization</label>
|
||||
</div>
|
||||
<div class="form-check form-check-inline">
|
||||
<input class="form-check-input" type="checkbox" value="onehot" name="boxes">
|
||||
<label class="form-check-label" for="inlineCheckbox2">One Hot Encoding</label>
|
||||
</div>
|
||||
<div class="form-check form-check-inline">
|
||||
<input class="form-check-input" type="checkbox" value="imp" name="boxes">
|
||||
<label class="form-check-label" for="inlineCheckbox3">Imputations</label>
|
||||
</div>
|
||||
</div>
|
||||
</form>
|
||||
</div>
|
||||
<div class="col d-flex justify-content-center">
|
||||
<div class="col d-flex justify-content-center multi-button">
|
||||
<form action="{% url 'home' %}" method="POST">
|
||||
{% csrf_token %}
|
||||
<label style="display:flex;
|
||||
</div>
|
||||
<br>
|
||||
<div class="row">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<button type="submit" class="button-6" role="button" name="preprocess" form="preprocess-form">Go!</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row">
|
||||
{% if pca %}
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
{{ pca|safe }}
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if tsne %}
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
{{ tsne|safe }}
|
||||
</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
</div>
|
||||
|
||||
<div class="container-fluid">
|
||||
<form action="{% url 'home' %}" method="POST" id="traintest-form">
|
||||
{% csrf_token %}
|
||||
<div class="row">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<label style="display:flex;
|
||||
flex-direction:column;
|
||||
align-items: center;">
|
||||
<h3>
|
||||
<i class="fas fa-cog"></i> Preprocessing
|
||||
</h3>
|
||||
</label>
|
||||
<div class="multi-button">
|
||||
<button type="submit" name="std" class="button-6" role="button" >Standardization</button>
|
||||
<button type="submit" name="onehot" style="margin:0;margin-left:16px;" class="button-6" role="button" >One Hot Encoding</button>
|
||||
<button type="submit" name="imp" style="margin:0;margin-left:16px;" class="button-6" role="button" >Imputations</button>
|
||||
</div>
|
||||
</form>
|
||||
<h2>
|
||||
<i class="fas fa-magic"></i>Pick a model
|
||||
</h2>
|
||||
<select id="model" name="model" style="scale: 1.2;">
|
||||
<option type="submit" value="svm">Support Vector Machine</option>
|
||||
<option type="submit" value="logit" selected>Logistic Regression</option>
|
||||
<option type="submit" value="xgb">XGBoost</option>
|
||||
<option type="submit" value="dt">Decision Tree</option>
|
||||
<option type="submit" value="rt">Random Forest</option>
|
||||
</select>
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<div class="row">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<div style="scale: 1.5;">
|
||||
<input type="radio" name="colorRadio" value="train" class="trainTestButton" id="train"/>
|
||||
<label for="radio1">Train </label>
|
||||
|
||||
<input type="radio" name="colorRadio" value="test" class="trainTestButton" id="test"/>
|
||||
<label for="radio2">Test </label>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<br>
|
||||
|
||||
<div id="train-me" style="display: none;">
|
||||
<br>
|
||||
<div class="row">
|
||||
<div class="col-md d-flex justify-content-center">
|
||||
<label style="display:flex;
|
||||
flex-direction:column;
|
||||
align-items: center;">
|
||||
<h3>
|
||||
Test set ratio
|
||||
</h3>
|
||||
<input name="split_input" id="split_input" type="range" min="0" max="1" step="0.1" style="width: 200px;"/>
|
||||
<output id="value"></output>
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<br>
|
||||
</form>
|
||||
<div class="row">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<button type="submit" class="button-6" role="button" name="traintest" form="traintest-form">Go!</button>
|
||||
</div>
|
||||
</div>
|
||||
<br>
|
||||
<div class="row">
|
||||
{% if fig2 %}
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
{{ fig2|safe }}
|
||||
</div>
|
||||
{% endif %}
|
||||
{% if clas_report %}
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<div class="scrollit">
|
||||
{{ clas_report|safe }}
|
||||
</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
</div>
|
||||
{% endblock content%}
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
from django.urls import path
|
||||
from django.urls import path, include
|
||||
from . import views
|
||||
from . import models
|
||||
|
||||
|
||||
urlpatterns = [
|
||||
path('', views.home, name="home"),
|
||||
path('preprocess', views.preprocess, name="preprocess"),
|
||||
path('stats', views.stats, name="stats"),
|
||||
path('train',views.training, name = 'training'),
|
||||
path('stats', views.stats, name="stats"),
|
||||
]
|
||||
532
base/views.py
532
base/views.py
@ -2,168 +2,434 @@ from django.shortcuts import render, redirect
|
||||
import pandas as pd
|
||||
from django.core.files.storage import FileSystemStorage
|
||||
import pickle, os
|
||||
from sklearn.preprocessing import OneHotEncoder
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
import numpy as np
|
||||
from pandas.api.types import is_string_dtype
|
||||
from pandas.api.types import is_numeric_dtype
|
||||
from . import utils
|
||||
from sklearn.metrics import accuracy_score, classification_report
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import joblib
|
||||
from sklearn.decomposition import PCA
|
||||
from sklearn.manifold import TSNE
|
||||
|
||||
|
||||
fig = None
|
||||
excel_file_name_preprocessed = "dataset_preprocessed.csv"
|
||||
excel_file_name = "dataset.csv"
|
||||
clas_report = None
|
||||
FILE_NAME = "dataset.csv"
|
||||
PROCESS_FILE_NAME = "dataset_preprocessed.csv"
|
||||
|
||||
|
||||
def home(request):
|
||||
global fig
|
||||
global excel_file_name
|
||||
global excel_file_name_preprocessed
|
||||
|
||||
if request.method == 'POST':
|
||||
if 'csv' in request.POST:
|
||||
global clas_report
|
||||
# request.session.flush()
|
||||
if "fig" in request.session:
|
||||
fig = request.session.get("fig")
|
||||
else:
|
||||
fig = None
|
||||
|
||||
if "fig2" in request.session:
|
||||
fig2 = request.session.get("fig2")
|
||||
else:
|
||||
fig2 = None
|
||||
|
||||
if "pca" in request.session:
|
||||
pca = request.session.get("pca")
|
||||
else:
|
||||
pca = None
|
||||
|
||||
if "tsne" in request.session:
|
||||
tsne = request.session.get("tsne")
|
||||
else:
|
||||
tsne = None
|
||||
|
||||
if "excel_file_name" in request.session:
|
||||
excel_file_name = request.session.get("excel_file_name")
|
||||
else:
|
||||
# name for default dataset. If session variable
|
||||
# is not set that means there was never a csv
|
||||
# UPLOAD and thus the default dataset should used
|
||||
excel_file_name = FILE_NAME
|
||||
request.session["excel_file_name"] = excel_file_name
|
||||
|
||||
if "excel_file_name_preprocessed" in request.session:
|
||||
excel_file_name_preprocessed = request.session.get(
|
||||
"excel_file_name_preprocessed"
|
||||
)
|
||||
else:
|
||||
excel_file_name_preprocessed = PROCESS_FILE_NAME
|
||||
request.session["excel_file_name_preprocessed"] = excel_file_name_preprocessed
|
||||
|
||||
df = pd.DataFrame()
|
||||
|
||||
if request.method == "POST":
|
||||
if "csv" in request.POST:
|
||||
excel_file = request.FILES["excel_file"]
|
||||
excel_file_name = request.FILES["excel_file"].name
|
||||
|
||||
# here we dont use the name of the file since the
|
||||
|
||||
fig = None
|
||||
fig2 = None
|
||||
|
||||
# here we dont use the name of the file since the
|
||||
# uploaded file is not yet saved
|
||||
# In every other case we just need the name
|
||||
df = pd.read_csv(excel_file)
|
||||
|
||||
fs = FileSystemStorage() #defaults to MEDIA_ROOT
|
||||
request.session['excel_file_name'] = excel_file_name
|
||||
fs.save(excel_file_name, excel_file)
|
||||
|
||||
idx = excel_file_name.index('.')
|
||||
excel_file_name_preprocessed = excel_file_name[:idx] + "_preprocessed" + excel_file_name[idx:]
|
||||
fs = FileSystemStorage() #defaults to MEDIA_ROOT
|
||||
request.session['excel_file_name_preprocess'] = excel_file_name_preprocessed
|
||||
fs.save(excel_file_name_preprocessed, excel_file)
|
||||
# if file for preprocessing does not exist create it
|
||||
if os.path.exists(excel_file_name_preprocessed) == False:
|
||||
df.to_csv(excel_file_name_preprocessed)
|
||||
|
||||
|
||||
# fs = FileSystemStorage() # defaults to MEDIA_ROOT
|
||||
request.session["excel_file_name"] = excel_file_name
|
||||
# fs.save(excel_file_name, excel_file)
|
||||
|
||||
df = pd.read_csv(excel_file)
|
||||
df.drop(["id"], axis=1, inplace=True)
|
||||
df.to_csv(excel_file_name, index=False)
|
||||
|
||||
feature1 = df.columns[0]
|
||||
feature2 = df.columns[1]
|
||||
request.session['feature1'] = feature1
|
||||
request.session['feature2'] = feature2
|
||||
|
||||
elif 'plot' in request.POST:
|
||||
df = pd.read_csv(excel_file_name)
|
||||
feature1 = request.POST.get('feature1')
|
||||
feature2 = request.POST.get('feature2')
|
||||
print(feature1)
|
||||
request.session['feature1'] = feature1
|
||||
request.session['feature2'] = feature2
|
||||
else:
|
||||
df = pd.read_csv(excel_file_name)
|
||||
|
||||
if 'std' in request.POST:
|
||||
preprocess(excel_file_name_preprocessed, 'std')
|
||||
if 'onehot' in request.POST:
|
||||
preprocess(excel_file_name_preprocessed, 'onehot')
|
||||
if 'imp' in request.POST:
|
||||
preprocess(excel_file_name_preprocessed, 'imp')
|
||||
|
||||
if 'logit' in request.POST:
|
||||
training(excel_file_name_preprocessed, 'logit')
|
||||
if 'xgb' in request.POST:
|
||||
training(excel_file_name_preprocessed, 'xgb')
|
||||
else:
|
||||
df = pd.read_csv(excel_file_name)
|
||||
request.session["feature1"] = feature1
|
||||
request.session["feature2"] = feature2
|
||||
|
||||
fig = stats(
|
||||
excel_file_name,
|
||||
request.session["feature1"],
|
||||
request.session["feature2"],
|
||||
)
|
||||
|
||||
elif "plot" in request.POST:
|
||||
feature1 = request.POST.get("feature1")
|
||||
feature2 = request.POST.get("feature2")
|
||||
request.session["feature1"] = feature1
|
||||
request.session["feature2"] = feature2
|
||||
fig = stats(
|
||||
excel_file_name,
|
||||
request.session["feature1"],
|
||||
request.session["feature2"],
|
||||
)
|
||||
|
||||
elif "traintest" in request.POST:
|
||||
mode = request.POST.get("colorRadio")
|
||||
model = request.POST.get("model")
|
||||
test_size = float(request.POST.get("split_input"))
|
||||
print(test_size, mode, model)
|
||||
if mode == "train":
|
||||
if model == "logit":
|
||||
con = training(excel_file_name_preprocessed, "logit", test_size)
|
||||
|
||||
elif model == "xgb":
|
||||
con = training(excel_file_name_preprocessed, "xgb", test_size)
|
||||
|
||||
elif model == "dt":
|
||||
con = training(excel_file_name_preprocessed, "dt", test_size)
|
||||
|
||||
elif model == "svm":
|
||||
con = training(excel_file_name_preprocessed, "svm", test_size)
|
||||
|
||||
fig2 = con["fig2"]
|
||||
clas_report = con["clas_report"].to_html()
|
||||
elif mode == "test":
|
||||
if model == "logit":
|
||||
con = testing(excel_file_name_preprocessed, "logit")
|
||||
|
||||
elif model == "xgb":
|
||||
con = testing(excel_file_name_preprocessed, "xgb")
|
||||
|
||||
elif model == "dt":
|
||||
con = testing(excel_file_name_preprocessed, "dt")
|
||||
|
||||
elif model == "svm":
|
||||
con = testing(excel_file_name_preprocessed, "svm")
|
||||
|
||||
fig2 = con["fig2"]
|
||||
clas_report = con["clas_report"].to_html()
|
||||
|
||||
elif "preprocess" in request.POST:
|
||||
value_list = request.POST.getlist("boxes")
|
||||
|
||||
# if file for preprocessing does not exist create it
|
||||
if os.path.exists(excel_file_name_preprocessed) == False:
|
||||
df.to_csv(excel_file_name_preprocessed)
|
||||
|
||||
# also apply basic preprocessing
|
||||
if os.path.exists(excel_file_name_preprocessed) == False:
|
||||
|
||||
# generate filename
|
||||
idx = excel_file_name.index(".")
|
||||
excel_file_name_preprocessed = (
|
||||
excel_file_name[:idx] + "_preprocessed" + excel_file_name[idx:]
|
||||
)
|
||||
|
||||
# save file for preprocessing
|
||||
preprocess_df = pd.read_csv(excel_file_name)
|
||||
fs = FileSystemStorage() # defaults to MEDIA_ROOT
|
||||
request.session["excel_file_name_preprocessed"] = (
|
||||
excel_file_name_preprocessed
|
||||
)
|
||||
preprocess_df.to_csv(excel_file_name_preprocessed, index=False)
|
||||
|
||||
preprocess_df.drop(
|
||||
["perimeter_mean", "area_mean"], axis=1, inplace=True
|
||||
)
|
||||
preprocess_df.drop(
|
||||
["perimeter_worst", "area_worst"], axis=1, inplace=True
|
||||
)
|
||||
preprocess_df.drop(["perimeter_se", "area_se"], axis=1, inplace=True)
|
||||
preprocess_df.drop(
|
||||
[
|
||||
"radius_worst",
|
||||
"concave_points_mean",
|
||||
"texture_worst",
|
||||
"symmetry_worst",
|
||||
"smoothness_worst",
|
||||
],
|
||||
axis=1,
|
||||
inplace=True,
|
||||
)
|
||||
|
||||
# preprocess_df.drop(["id"], axis=1, inplace=True)
|
||||
le = LabelEncoder()
|
||||
preprocess_df["diagnosis"] = le.fit_transform(
|
||||
preprocess_df["diagnosis"]
|
||||
)
|
||||
else:
|
||||
preprocess_df = pd.read_csv(excel_file_name_preprocessed)
|
||||
|
||||
preprocess(preprocess_df, value_list, excel_file_name_preprocessed)
|
||||
|
||||
pca = PCA()
|
||||
pca.fit(preprocess_df)
|
||||
exp_var_cumul = np.cumsum(pca.explained_variance_ratio_)
|
||||
pca = px.area(
|
||||
x=range(1, exp_var_cumul.shape[0] + 1),
|
||||
y=exp_var_cumul,
|
||||
labels={"x": "# Components", "y": "Explained Variance"},
|
||||
).to_html()
|
||||
|
||||
features = preprocess_df.loc[:, :"compactness_se"]
|
||||
|
||||
tsne = TSNE(n_components=2, random_state=0)
|
||||
projections = tsne.fit_transform(features)
|
||||
|
||||
tsne = px.scatter(
|
||||
projections,
|
||||
x=0,
|
||||
y=1,
|
||||
color=preprocess_df.diagnosis,
|
||||
labels={"color": "diagnosis"},
|
||||
).to_html()
|
||||
|
||||
else:
|
||||
|
||||
if os.path.exists(excel_file_name) == False:
|
||||
excel_file_name = "dataset.csv"
|
||||
request.session["excel_file_name"] = excel_file_name
|
||||
|
||||
df = pd.read_csv(excel_file_name)
|
||||
fig2 = None
|
||||
|
||||
# just random columns to plot
|
||||
feature1 = df.columns[0]
|
||||
feature2 = df.columns[1]
|
||||
request.session['feature1'] = feature1
|
||||
request.session['feature2'] = feature2
|
||||
|
||||
fig = stats(excel_file_name, request.session['feature1'], request.session['feature2'])
|
||||
|
||||
data_to_display = df[:5].to_html()
|
||||
request.session["feature1"] = feature1
|
||||
request.session["feature2"] = feature2
|
||||
fig = stats(
|
||||
excel_file_name, request.session["feature1"], request.session["feature2"]
|
||||
)
|
||||
|
||||
request.session['data_to_display'] = data_to_display
|
||||
request.session['excel_file_name'] = excel_file_name
|
||||
request.session['excel_file_name_preprocessed'] = excel_file_name_preprocessed
|
||||
if df.empty:
|
||||
df = pd.read_csv(excel_file_name)
|
||||
|
||||
request.session["fig"] = fig
|
||||
request.session["fig2"] = fig2
|
||||
request.session["pca"] = pca
|
||||
request.session["tsne"] = tsne
|
||||
|
||||
data_to_display = df[:5].to_html()
|
||||
request.session["data_to_display"] = data_to_display
|
||||
labels = df.columns
|
||||
|
||||
context = {'data_to_display': data_to_display, 'excel_file': excel_file_name, 'labels': labels, 'fig': fig, 'feature1': request.session['feature1'], 'feature2': request.session['feature2']}
|
||||
return render(request,'base/home.html', context)
|
||||
context = {
|
||||
"data_to_display": data_to_display,
|
||||
"excel_file": excel_file_name,
|
||||
"labels": labels,
|
||||
"fig": fig,
|
||||
"fig2": fig2,
|
||||
"feature1": request.session["feature1"],
|
||||
"feature2": request.session["feature2"],
|
||||
"clas_report": clas_report,
|
||||
"pca": pca,
|
||||
"tsne": tsne,
|
||||
}
|
||||
|
||||
return render(request, "base/home.html", context)
|
||||
|
||||
|
||||
def stats(name, feature1, feature2):
|
||||
global fig
|
||||
df = pd.read_csv(name)
|
||||
import plotly.express as px
|
||||
if is_numeric_dtype(df[feature1]) and is_numeric_dtype(df[feature2]):
|
||||
print("if")
|
||||
fig = px.scatter(df, feature1, feature2, color='DEATH_EVENT')
|
||||
elif is_string_dtype(df[feature1]) and is_string_dtype(df[feature2]):
|
||||
print("elseif")
|
||||
fig = px.histogram(df, feature1)
|
||||
binary1 = df[feature1].isin([0, 1]).all()
|
||||
binary2 = df[feature2].isin([0, 1]).all()
|
||||
if binary1 == True or binary2 == True:
|
||||
fig = px.histogram(df, x=feature1, color=feature2)
|
||||
elif is_numeric_dtype(df[feature1]) or is_numeric_dtype(df[feature2]):
|
||||
if not is_numeric_dtype(df[feature1]):
|
||||
# feature1 is not numeric but feature2 should be
|
||||
fig = px.histogram(df, x=feature2, color=feature1)
|
||||
elif not is_numeric_dtype(df[feature2]):
|
||||
# feature2 is not numeric but feature1 should be
|
||||
fig = px.histogram(df, x=feature1, color=feature2)
|
||||
else:
|
||||
# they both are numeric so do scatter
|
||||
fig = px.scatter(
|
||||
df, x=feature1, y=feature2, color=df["diagnosis"].astype(str)
|
||||
)
|
||||
else:
|
||||
print("else")
|
||||
fig = px.strip(df, feature1, feature2)
|
||||
|
||||
# they both are categorical so do scatter
|
||||
fig = px.histogram(df, x=feature1, color=feature2)
|
||||
fig = fig.to_html(full_html=False)
|
||||
return fig
|
||||
|
||||
def preprocess(name, type):
|
||||
|
||||
def preprocess(data, value_list, name):
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
data = pd.read_csv(name)
|
||||
|
||||
if set(['No','customerID','Churn']).issubset(data.columns):
|
||||
data.drop(['No','customerID','Churn'],axis=1,inplace=True)
|
||||
|
||||
if type == 'std':
|
||||
# define standard scaler
|
||||
scaler = StandardScaler()
|
||||
# transform data
|
||||
print("HEY")
|
||||
num_d = data.select_dtypes(exclude=['object'])
|
||||
data[num_d.columns] = scaler.fit_transform(num_d)
|
||||
|
||||
if type=='onehot':
|
||||
data = pd.get_dummies(data,columns=['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',
|
||||
'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
|
||||
'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',
|
||||
'PaperlessBilling', 'PaymentMethod'],drop_first = True)
|
||||
|
||||
if type == 'imp':
|
||||
data_numeric = data.select_dtypes(exclude=['object'])
|
||||
data_categorical = data.select_dtypes(exclude=['number'])
|
||||
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
|
||||
data_numeric = pd.DataFrame(imp.fit_transform(data_numeric), columns = data_numeric.columns, index=data_numeric.index) #only apply imputer to numeric columns
|
||||
data = pd.concat([data_numeric, data_categorical], axis = 1)
|
||||
|
||||
os.remove(excel_file_name_preprocessed)
|
||||
data.to_csv(excel_file_name_preprocessed)
|
||||
return
|
||||
|
||||
def training(name, type):
|
||||
global fig
|
||||
for type in value_list:
|
||||
if type == "std":
|
||||
# define standard scaler
|
||||
scaler = StandardScaler()
|
||||
y = data["diagnosis"]
|
||||
|
||||
if is_numeric_dtype(data["diagnosis"]):
|
||||
# if class column is numeric do not
|
||||
# apply preprocessing
|
||||
data = data.drop(["diagnosis"], axis=1)
|
||||
|
||||
# transform data
|
||||
cols = data.select_dtypes(np.number).columns
|
||||
data[cols] = scaler.fit_transform(data[cols])
|
||||
y = y.to_frame()
|
||||
data = data.join(y)
|
||||
|
||||
if type == "onehot":
|
||||
data = pd.get_dummies(data)
|
||||
|
||||
if type == "imp":
|
||||
data_numeric = data.select_dtypes(exclude=["object"])
|
||||
data_categorical = data.select_dtypes(exclude=["number"])
|
||||
imp = SimpleImputer(missing_values=np.nan, strategy="most_frequent")
|
||||
data_numeric = pd.DataFrame(
|
||||
imp.fit_transform(data_numeric),
|
||||
columns=data_numeric.columns,
|
||||
index=data_numeric.index,
|
||||
) # only apply imputer to numeric columns
|
||||
data = pd.concat(
|
||||
[data_numeric, data_categorical], axis=1, ignore_index=False
|
||||
)
|
||||
|
||||
os.remove(name)
|
||||
data.to_csv(name, index=False)
|
||||
return
|
||||
|
||||
|
||||
def training(name, type, test_size=0.7):
|
||||
data = pd.read_csv(name)
|
||||
|
||||
y=data['Churn']
|
||||
y=y.replace({"Yes":1,"No":0})
|
||||
from sklearn.model_selection import train_test_split
|
||||
X_train, X_test, y_train, y_test = train_test_split(data, y, test_size=0.2, random_state=0)
|
||||
X_train.to_csv("X_train.csv")
|
||||
X_test.to_csv("X_test.csv")
|
||||
y_train.to_csv("y_train.csv")
|
||||
y_test.to_csv("y_test.csv")
|
||||
|
||||
if 'logit' == type:
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
|
||||
filename = 'lg.sav'
|
||||
pickle.dump(clf, open(filename, 'wb'))
|
||||
|
||||
if 'xgb' == type:
|
||||
y = data["diagnosis"]
|
||||
X = data.drop("diagnosis", axis=1)
|
||||
X_train, X_test, y_train, y_test = train_test_split(
|
||||
X, y, shuffle=True, test_size=test_size, stratify=y, random_state=42
|
||||
)
|
||||
|
||||
test = X_test.join(y_test.to_frame())
|
||||
test.to_csv("test.csv", index=False)
|
||||
train = X_train.join(y_train.to_frame())
|
||||
train.to_csv("train.csv", index=False)
|
||||
|
||||
if "logit" == type:
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
|
||||
y_pred = clf.predict(X_test)
|
||||
filename = "lg.sav"
|
||||
pickle.dump(clf, open(filename, "wb"))
|
||||
importance = clf.coef_[0]
|
||||
model = clf
|
||||
|
||||
if "xgb" == type:
|
||||
from xgboost import XGBClassifier
|
||||
xgb = XGBClassifier(learning_rate = 0.01,n_estimators=1000).fit(X_train, y_train)
|
||||
file_name = 'xgb.sav'
|
||||
pickle.dump(xgb,open(file_name,'wb'))
|
||||
|
||||
return
|
||||
|
||||
xgb = XGBClassifier(learning_rate=0.01, n_estimators=1000).fit(X_train, y_train)
|
||||
y_pred = xgb.predict(X_test)
|
||||
filename = "xgb.sav"
|
||||
importance = xgb.feature_importances_
|
||||
model = xgb
|
||||
|
||||
if "dt" == type:
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
|
||||
dt = DecisionTreeClassifier(max_depth=4, random_state=1)
|
||||
dt.fit(X_train, y_train)
|
||||
y_pred = dt.predict(X_test)
|
||||
filename = "dt.sav"
|
||||
importance = dt.feature_importances_
|
||||
model = dt
|
||||
|
||||
if "svm" == type:
|
||||
from sklearn import svm
|
||||
|
||||
svc = svm.LinearSVC()
|
||||
svc.fit(X_train, y_train)
|
||||
y_pred = svc.predict(X_test)
|
||||
filename = "svc.sav"
|
||||
importance = svc.coef_[0]
|
||||
model = svc
|
||||
|
||||
clas_report = classification_report(y_test, y_pred, output_dict=True)
|
||||
clas_report = pd.DataFrame(clas_report).transpose()
|
||||
clas_report = clas_report.sort_values(by=["f1-score"], ascending=False)
|
||||
fig2 = px.bar(x=importance, y=X_train.columns)
|
||||
pickle.dump(model, open(filename, "wb"))
|
||||
con = {
|
||||
"fig2": fig2.to_html(),
|
||||
"clas_report": clas_report,
|
||||
}
|
||||
return con
|
||||
|
||||
|
||||
def testing(name, type):
|
||||
data = pd.read_csv(name)
|
||||
y_test = data["diagnosis"]
|
||||
X_test = data.drop("diagnosis", axis=1)
|
||||
|
||||
if "logit" == type:
|
||||
filename = "lg.sav"
|
||||
clf = joblib.load(filename)
|
||||
y_pred = clf.predict(X_test)
|
||||
importance = clf.coef_[0]
|
||||
model = clf
|
||||
|
||||
if "xgb" == type:
|
||||
filename = "xgb.sav"
|
||||
xgb = joblib.load(filename)
|
||||
y_pred = xgb.predict(X_test)
|
||||
filename = "xgb.sav"
|
||||
importance = xgb.feature_importances_
|
||||
model = xgb
|
||||
|
||||
if "dt" == type:
|
||||
filename = "dt.sav"
|
||||
dt = joblib.load(filename)
|
||||
y_pred = dt.predict(X_test)
|
||||
importance = dt.feature_importances_
|
||||
model = dt
|
||||
|
||||
if "svm" == type:
|
||||
filename = "svc.sav"
|
||||
svc = joblib.load(filename)
|
||||
y_pred = svc.predict(X_test)
|
||||
importance = svc.coef_[0]
|
||||
model = svc
|
||||
|
||||
clas_report = classification_report(y_test, y_pred, output_dict=True)
|
||||
clas_report = pd.DataFrame(clas_report).transpose()
|
||||
clas_report = clas_report.sort_values(by=["f1-score"], ascending=False)
|
||||
fig2 = px.bar(x=importance, y=X_test.columns)
|
||||
pickle.dump(model, open(filename, "wb"))
|
||||
con = {
|
||||
"fig2": fig2.to_html(),
|
||||
"clas_report": clas_report,
|
||||
}
|
||||
return con
|
||||
|
||||
11098
dataset.csv
11098
dataset.csv
File diff suppressed because it is too large
Load Diff
BIN
db.sqlite3
BIN
db.sqlite3
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -21,7 +21,7 @@ BASE_DIR = Path(__file__).resolve().parent.parent
|
||||
# See https://docs.djangoproject.com/en/5.0/howto/deployment/checklist/
|
||||
|
||||
# SECURITY WARNING: keep the secret key used in production secret!
|
||||
SECRET_KEY = 'django-insecure-0w^ybt_7vclag#rrutc_eo)m+l^@ml)t%jsg6n06siu)xyls+-'
|
||||
SECRET_KEY = "django-insecure-0w^ybt_7vclag#rrutc_eo)m+l^@ml)t%jsg6n06siu)xyls+-"
|
||||
|
||||
# SECURITY WARNING: don't run with debug turned on in production!
|
||||
DEBUG = True
|
||||
@ -32,56 +32,54 @@ ALLOWED_HOSTS = []
|
||||
# Application definition
|
||||
|
||||
INSTALLED_APPS = [
|
||||
'django.contrib.admin',
|
||||
'django.contrib.auth',
|
||||
'django.contrib.contenttypes',
|
||||
'django.contrib.sessions',
|
||||
'django.contrib.messages',
|
||||
'django.contrib.staticfiles',
|
||||
'base.apps.BaseConfig',
|
||||
'bootstrap5',
|
||||
"django.contrib.admin",
|
||||
"django.contrib.auth",
|
||||
"django.contrib.contenttypes",
|
||||
"django.contrib.sessions",
|
||||
"django.contrib.messages",
|
||||
"django.contrib.staticfiles",
|
||||
"base.apps.BaseConfig",
|
||||
"bootstrap5",
|
||||
]
|
||||
|
||||
MIDDLEWARE = [
|
||||
'django.middleware.security.SecurityMiddleware',
|
||||
'django.contrib.sessions.middleware.SessionMiddleware',
|
||||
'django.middleware.common.CommonMiddleware',
|
||||
'django.middleware.csrf.CsrfViewMiddleware',
|
||||
'django.contrib.auth.middleware.AuthenticationMiddleware',
|
||||
'django.contrib.messages.middleware.MessageMiddleware',
|
||||
'django.middleware.clickjacking.XFrameOptionsMiddleware',
|
||||
"django.middleware.security.SecurityMiddleware",
|
||||
"django.contrib.sessions.middleware.SessionMiddleware",
|
||||
"django.middleware.common.CommonMiddleware",
|
||||
"django.middleware.csrf.CsrfViewMiddleware",
|
||||
"django.contrib.auth.middleware.AuthenticationMiddleware",
|
||||
"django.contrib.messages.middleware.MessageMiddleware",
|
||||
"django.middleware.clickjacking.XFrameOptionsMiddleware",
|
||||
]
|
||||
|
||||
ROOT_URLCONF = 'extremum.urls'
|
||||
ROOT_URLCONF = "extremum.urls"
|
||||
|
||||
TEMPLATES = [
|
||||
{
|
||||
'BACKEND': 'django.template.backends.django.DjangoTemplates',
|
||||
'DIRS': [
|
||||
|
||||
BASE_DIR / 'templates'],
|
||||
'APP_DIRS': True,
|
||||
'OPTIONS': {
|
||||
'context_processors': [
|
||||
'django.template.context_processors.debug',
|
||||
'django.template.context_processors.request',
|
||||
'django.contrib.auth.context_processors.auth',
|
||||
'django.contrib.messages.context_processors.messages',
|
||||
"BACKEND": "django.template.backends.django.DjangoTemplates",
|
||||
"DIRS": [BASE_DIR / "templates"],
|
||||
"APP_DIRS": True,
|
||||
"OPTIONS": {
|
||||
"context_processors": [
|
||||
"django.template.context_processors.debug",
|
||||
"django.template.context_processors.request",
|
||||
"django.contrib.auth.context_processors.auth",
|
||||
"django.contrib.messages.context_processors.messages",
|
||||
],
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
WSGI_APPLICATION = 'extremum.wsgi.application'
|
||||
WSGI_APPLICATION = "extremum.wsgi.application"
|
||||
|
||||
|
||||
# Database
|
||||
# https://docs.djangoproject.com/en/5.0/ref/settings/#databases
|
||||
|
||||
DATABASES = {
|
||||
'default': {
|
||||
'ENGINE': 'django.db.backends.sqlite3',
|
||||
'NAME': BASE_DIR / 'db.sqlite3',
|
||||
"default": {
|
||||
"ENGINE": "django.db.backends.sqlite3",
|
||||
"NAME": BASE_DIR / "db.sqlite3",
|
||||
}
|
||||
}
|
||||
|
||||
@ -91,16 +89,16 @@ DATABASES = {
|
||||
|
||||
AUTH_PASSWORD_VALIDATORS = [
|
||||
{
|
||||
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
|
||||
"NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator",
|
||||
},
|
||||
{
|
||||
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
|
||||
"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",
|
||||
},
|
||||
{
|
||||
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
|
||||
"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",
|
||||
},
|
||||
{
|
||||
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
|
||||
"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",
|
||||
},
|
||||
]
|
||||
|
||||
@ -108,9 +106,9 @@ AUTH_PASSWORD_VALIDATORS = [
|
||||
# Internationalization
|
||||
# https://docs.djangoproject.com/en/5.0/topics/i18n/
|
||||
|
||||
LANGUAGE_CODE = 'en-us'
|
||||
LANGUAGE_CODE = "en-us"
|
||||
|
||||
TIME_ZONE = 'UTC'
|
||||
TIME_ZONE = "UTC"
|
||||
|
||||
USE_I18N = True
|
||||
|
||||
@ -120,11 +118,10 @@ USE_TZ = True
|
||||
# Static files (CSS, JavaScript, Images)
|
||||
# https://docs.djangoproject.com/en/5.0/howto/static-files/
|
||||
|
||||
STATIC_URL = '/static/'
|
||||
MEDIA_URL = '/images/'
|
||||
STATICFILES_DIRS = (os.path.join(BASE_DIR,'base/static'),)
|
||||
STATIC_URL = "base/static/"
|
||||
STATICFILES_DIRS = [BASE_DIR / "base/static"]
|
||||
|
||||
# Default primary key field type
|
||||
# https://docs.djangoproject.com/en/5.0/ref/settings/#default-auto-field
|
||||
|
||||
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
|
||||
DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField"
|
||||
|
||||
@ -1,111 +1,40 @@
|
||||
{% load static %}
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
{% load bootstrap5 %}
|
||||
{% bootstrap_css %}
|
||||
{% bootstrap_javascript %}
|
||||
<meta charset="utf-8" />
|
||||
<meta http-equiv="X-UA-Compatible" content="'IE=edge" />
|
||||
<title>EXTREMUM</title>
|
||||
<meta name="viewport" content="'width=device-width, initial-scale=1" />
|
||||
{% load static %}
|
||||
{% load bootstrap5 %}
|
||||
{% bootstrap_css %}
|
||||
{% bootstrap_javascript %}
|
||||
<link rel="stylesheet" href="{% static 'css/style.css' %}">
|
||||
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.6.0/jquery.min.js"></script>
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.2/css/all.min.css">
|
||||
</head>
|
||||
|
||||
<style>
|
||||
|
||||
.scrollit {
|
||||
overflow: auto;
|
||||
position: fixed;
|
||||
float: left;
|
||||
height: auto;
|
||||
width: 40%;
|
||||
max-width: fit-content;
|
||||
margin: 0px auto;
|
||||
}
|
||||
<nav>
|
||||
<div class="wrapper">
|
||||
<div class="logo"><a href="#">Extremum</a></div>
|
||||
<input type="radio" name="slider" id="menu-btn">
|
||||
<input type="radio" name="slider" id="close-btn">
|
||||
<label for="menu-btn" class="btn menu-btn"><i class="fas fa-bars"></i></label>
|
||||
</div>
|
||||
</nav>
|
||||
<br>
|
||||
<br>
|
||||
<br>
|
||||
<br>
|
||||
<br>
|
||||
<body onscroll="setScroll()" onload="setScreen()">
|
||||
|
||||
.dataframe {
|
||||
font-size: 11pt;
|
||||
font-family: Arial;
|
||||
border-collapse: collapse;
|
||||
margin: 25px 0;
|
||||
font-size: 0.9em;
|
||||
}
|
||||
|
||||
.dataframe thead tr {
|
||||
text-align: left;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.dataframe th,
|
||||
.dataframe td {
|
||||
padding: 12px 15px;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.dataframe tbody tr {
|
||||
border-bottom: 1px solid #dddddd;
|
||||
}
|
||||
|
||||
.dataframe tbody tr:nth-of-type(even) {
|
||||
background-color: #f3f3f3;
|
||||
}
|
||||
|
||||
.dataframe tbody tr:last-of-type {
|
||||
border-bottom: 2px solid #009879;
|
||||
}
|
||||
|
||||
/* CSS */
|
||||
.button-6 {
|
||||
align-items: center;
|
||||
background-color: #FFFFFF;
|
||||
border: 1px solid rgba(0, 0, 0, 0.1);
|
||||
border-radius: .25rem;
|
||||
box-shadow: rgba(0, 0, 0, 0.02) 0 1px 3px 0;
|
||||
box-sizing: border-box;
|
||||
color: rgba(0, 0, 0, 0.85);
|
||||
cursor: pointer;
|
||||
display: inline-flex;
|
||||
font-family: system-ui,-apple-system,system-ui,"Helvetica Neue",Helvetica,Arial,sans-serif;
|
||||
font-size: 16px;
|
||||
font-weight: 600;
|
||||
justify-content: center;
|
||||
line-height: 1.25;
|
||||
margin: 0;
|
||||
min-height: 3rem;
|
||||
padding: calc(.875rem - 1px) calc(1.5rem - 1px);
|
||||
position: relative;
|
||||
text-decoration: none;
|
||||
transition: all 250ms;
|
||||
user-select: none;
|
||||
-webkit-user-select: none;
|
||||
touch-action: manipulation;
|
||||
vertical-align: baseline;
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.button-6:hover,
|
||||
.button-6:focus {
|
||||
border-color: rgba(0, 0, 0, 0.15);
|
||||
box-shadow: rgba(0, 0, 0, 0.1) 0 4px 12px;
|
||||
color: rgba(0, 0, 0, 0.65);
|
||||
}
|
||||
|
||||
.button-6:hover {
|
||||
transform: translateY(-1px);
|
||||
}
|
||||
|
||||
.button-6:active {
|
||||
background-color: #F0F0F1;
|
||||
border-color: rgba(0, 0, 0, 0.15);
|
||||
box-shadow: rgba(0, 0, 0, 0.06) 0 2px 4px;
|
||||
color: rgba(0, 0, 0, 0.65);
|
||||
transform: translateY(0);
|
||||
}
|
||||
|
||||
</style>
|
||||
|
||||
<body>
|
||||
{% block content %} {% endblock content %}
|
||||
{% block content %}
|
||||
{% endblock content %}
|
||||
|
||||
<script src="{% static 'js/hide_seek.js' %}"></script>
|
||||
<script src="{% static 'js/slider.js' %}"></script>
|
||||
<script src="{% static 'js/keep_scroll_on_load.js' %}"></script>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user