diff --git a/assets/course_data_format.html b/assets/course_data_format.html
index 59a308599b718ef461a3a08c1d8b296d39541ac0..a717f145339a997b199c68a9ed1e3d0737562799 100644
--- a/assets/course_data_format.html
+++ b/assets/course_data_format.html
@@ -11,7 +11,7 @@
<h2> An option to add information on model </h2>
<h3> Why ? </h3>
-<p> There is a switch button, you can use it when you want to attach the csv you trained your model on or a feature mapping. This is useful when the values are categorical.
+<p> There is a switch button, you can use it when you want to attach the data (csv) you trained your model on or a feature mapping (txt). This is useful when the values are categorical.
</p>
<h3> How ? </h3>
@@ -22,11 +22,11 @@ If you decide to upload the feature mapping, it is a .txt file.
<h2> What kind of model can I upload ? </h2>
-<p> You can only import .pkl models.</p>
+<p> You can only import .pkl models. But it works with joblib and pickle libraries. </p>
<h2> What should the format of the instance be ? </h2>
-<p> You can either upload a .txt file containing hte instance with the format : feature1=value1,feature2=value2,... where feature1, feature2 are the names of the columns.
+<p> You can either upload a .txt file containing the instance with the format : feature1=value1,feature2=value2,... where feature1, feature2 are the names of the columns.
But you can also upload a json of your instance.</p>
diff --git a/assets/course_decision_tree.html b/assets/course_decision_tree.html
index b07a373ce003b2127983fa81d3dfb93c5e81771c..f20dff9cd5091e34137f4a3bfdf46cd72f7b65a4 100644
--- a/assets/course_decision_tree.html
+++ b/assets/course_decision_tree.html
@@ -2,13 +2,15 @@
<html lang="en">
<head>
<meta charset="UTF-8">
- <title>What kinf of model, data, or instance can I upload ?</title>
+ <title>Decision Tree</title>
</head>
<body>
-<h1> What library am I able to use on the platform ?</h1>
-
+<h2> What library am I able to use on the platform ?</h2>
<p> Only models from scikit-learn are allowed.</p>
+<h2> Do I have to upload data ?</h2>
+You have to upload data or feature mapping when you have categorical data.
+
</body>
</html>
\ No newline at end of file
diff --git a/assets/course_random_forest.html b/assets/course_random_forest.html
new file mode 100644
index 0000000000000000000000000000000000000000..d71522ea842aca3d4e61f8cab2efe997bad23d0b
--- /dev/null
+++ b/assets/course_random_forest.html
@@ -0,0 +1,16 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+ <meta charset="UTF-8">
+ <title>Random Forest</title>
+</head>
+<body>
+
+<h2> What library am I able to use on the platform ?</h2>
+<p> You can upload RandomForestClassifier from sklearn library or RF2001 models (from Aniti's team).</p>
+
+<h2> Do I have to upload data ?</h2>
+You need to upload the dataset your machine learning model trained on.
+
+</body>
+</html>
\ No newline at end of file
diff --git a/assets/typography.css b/assets/typography.css
index d7e515476d401f1752d55a05d301b1a5c850571f..6fdf27a664f50443c9eb597370399b09eb6ca62a 100644
--- a/assets/typography.css
+++ b/assets/typography.css
@@ -2,6 +2,10 @@ body {
font-family: sans-serif;
}
+H1 {
+ font-family:Georgia, 'Times New Roman', Times, serif;
+}
+
H4 {
font-size: 20px;
text-decoration-line:underline;
diff --git a/callbacks.py b/callbacks.py
index 906b09c41a107b7943cb9842a8a4a293fd5a649a..8213b6102a7d722b53a0de519799f34c502c1418 100644
--- a/callbacks.py
+++ b/callbacks.py
@@ -29,12 +29,28 @@ def register_callbacks(app):
page_list = ['home', 'course', 'application']
# For home directory
- page_home = dbc.Row([])
+ welcome_message = html.Div(html.Iframe(
+ src=app.get_asset_url("welcome.html"),
+ style={"height": "1067px", "width": "100%"},
+ ))
+ page_home = dbc.Row([welcome_message])
# For course directory
+ course_data_format = html.Div(html.Iframe(
+ src=app.get_asset_url("course_data_format.html"),
+ style={"height": "1067px", "width": "100%"},
+ ))
+ course_decision_tree = html.Iframe(
+ src="assets/course_decision_tree.html",
+ style={"height": "1067px", "width": "100%"},
+ )
+ course_random_forest = html.Iframe(
+ src="assets/course_random_forest.html",
+ style={"height": "1067px", "width": "100%"},
+ )
main_course = dcc.Tabs(children=[
- dcc.Tab(label='Data format', children=[]),
- dcc.Tab(label='Course Decision Tree', children=[]),
- dcc.Tab(label='Course Random Forest', children=[])])
+ dcc.Tab(label='Data format', children=[course_data_format]),
+ dcc.Tab(label='Course Decision Tree', children=[course_decision_tree]),
+ dcc.Tab(label='Course Random Forest', children=[course_random_forest])])
page_course = dbc.Row([main_course])
# For the application