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Commit 09e7fa2e authored by Caroline DE POURTALES's avatar Caroline DE POURTALES
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adding callbacks chnaged with buttons

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app.py
+ 2
− 35
View file @ 09e7fa2e
# Run this app with `python app.py` and
# visit http://127.0.0.1:8050/ in your web browser.
import json
import dash
import dash_bootstrap_components as dbc
from dash import dcc, html
from callbacks import register_callbacks
from pages.application.application import Application, Model, View
from utils import extract_data
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.LUX], suppress_callback_exceptions=True,
meta_tags=[{'name': 'viewport', 'content': 'width=device-width, initial-scale=1'}])
app.config.suppress_callback_exceptions = True
#################################################################################
############################# Layouts ###########################################
#################################################################################
models_data = open('data_retriever.json')
data = json.load(models_data)["data"]
# For home directory
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%"},
)
main_course = dcc.Tabs(children=[
dcc.Tab(label='Data format', children=[course_data_format]),
dcc.Tab(label='Course Decision Tree', children=[course_decision_tree])])
page_course = dbc.Row([main_course])
# For the application
names_models, dict_components, dic_solvers, dic_xtypes = extract_data(data)
model_application = Model(names_models, dict_components, dic_solvers, dic_xtypes)
view_application = View(model_application)
page_application = Application(view_application)
server = app.server
......@@ -67,7 +34,7 @@ app.layout = html.Div([
#################################################################################
################################# Callback for the app ##########################
#################################################################################
register_callbacks(page_home, page_course, page_application, app)
register_callbacks(app)
#################################################################################
################################# Launching app #################################
......
assets/header.css
+ 15
− 0
View file @ 09e7fa2e
......@@ -80,6 +80,21 @@ div.sidebar.col-3 {
background-color: rgb(26,26,26);
}
.sidebar .button{
display: block;
margin-left: auto;
margin-right: auto;
width: 50%;
height: 30px;
border-width: 1px;
border-radius: 5px;
text-align: center;
align: center;
color:rgb(255, 255, 255);
font-weight: 400;
background-color: rgb(26,26,26);
}
.sidebar .has-value.Select--single > .Select-control .Select-value .Select-value-label, .has-value.is-pseudo-focused.Select--single > .sidebar .Select-control .Select-value .Select-value-label {
color:rgb(255, 255, 255);
}
......
callbacks.py
+ 182
− 153
View file @ 09e7fa2e
import ast
import json
import dash
from dash.dependencies import Input, Output, State
import dash_bootstrap_components as dbc
from dash import Input, Output, State, html
from dash import dcc
from utils import parse_contents_graph, parse_contents_instance, parse_contents_data
from pages.application.application import Model, View
from utils import extract_data
from pages.application.RandomForest.utils import xrf
from pages.application.RandomForest.utils.xrf import *
sys.modules['xrf'] = xrf
def register_callbacks(page_home, page_course, page_application, app):
def register_callbacks(app):
page_list = ['home', 'course', 'application']
####### Creates alerts when callback fails #########
# For home directory
page_home = dbc.Row([])
# For course directory
main_course = dcc.Tabs(children=[
dcc.Tab(label='Data format', children=[]),
dcc.Tab(label='Course Decision Tree', children=[])])
page_course = dbc.Row([main_course])
models_data = open('data_retriever.json')
data = json.load(models_data)["data"]
# For the application
names_models, dict_components, dic_solvers, dic_xtypes = extract_data(data)
model_application = Model(names_models, dict_components, dic_solvers, dic_xtypes)
view_application = View(model_application)
####### Creates alerts when callback fails #########
warning_selection_model = html.Div([dbc.Alert("You didn't choose a king of Machine Learning model first.",
is_open=True,
color='warning',
duration=10000, ), ])
warning_selection_pretrained_model = html.Div([dbc.Alert("You uploaded the data, now upload the pretrained model.",
is_open=True,
color='warning',
duration=10000, ), ])
warning_selection_data = html.Div([dbc.Alert("You uploaded the model, now upload the data.",
is_open=True,
color='warning',
duration=10000, ), ])
alert_network = html.Div([dbc.Alert("There was a problem while computing the graph, read the documentation. \
You might have forgotten to upload the data for Random Forest or you tried to upload an unknown format.",
is_open=True,
......@@ -44,7 +57,17 @@ def register_callbacks(page_home, page_course, page_application, app):
color='danger',
duration=10000, ), ])
alert_version_model = []
reinit = html.Div([dbc.Alert(
"Reinitialization caused by changing model type or pkl or data or instance.",
is_open=True,
color='info',
duration=5000, ), ])
init_network = html.Div([dbc.Alert(
"Initialization.",
is_open=True,
color='info',
duration=5000, ), ])
######################################################
......@@ -55,7 +78,7 @@ def register_callbacks(page_home, page_course, page_application, app):
if pathname == '/':
return page_home
if pathname == '/application':
return page_application.view.layout
return view_application.layout
if pathname == '/course':
return page_course
......@@ -67,6 +90,7 @@ def register_callbacks(page_home, page_course, page_application, app):
active_link = ([pathname == f'/{i}' for i in page_list])
return active_link[0], active_link[1], active_link[2]
# region mltype
@app.callback(Output('solver_sat', 'options'),
Output('solver_sat', 'value'),
Output('explanation_type', 'options'),
......@@ -74,211 +98,217 @@ def register_callbacks(page_home, page_course, page_application, app):
Input('ml_model_choice', 'value'),
prevent_initial_call=True
)
def update_ml_type_options(value_ml_model):
model_application = page_application.model
model_application.update_ml_model(value_ml_model)
def update_ml_type_options(ml_type):
model_application.update_ml_model(ml_type)
return model_application.solvers, model_application.solvers[0], model_application.xtypes, [
list(model_application.xtypes.keys())[0]]
@app.callback(Output('pretrained_model_filename', 'children'),
Input('ml_model_choice', 'value'),
Input('ml_pretrained_model_choice', 'filename'),
prevent_initial_call=True
)
def update_model_pretrained_name(value_ml_model, pretrained_model_filename):
# endregion
# region pretrained model
@app.callback(
Output('pretrained_model_filename', 'children'),
Input('ml_model_choice', 'value'),
Input('ml_pretrained_model_choice', 'contents'),
State('ml_pretrained_model_choice', 'filename'),
prevent_initial_call=True)
def select_model(ml_type, model, filename):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered[0]['prop_id'].split('.')[0]
ihm_id = ctx.triggered_id
if ihm_id == 'ml_model_choice':
return None
else:
return pretrained_model_filename
graph = parse_contents_graph(model, filename)
model_application.update_pretrained_model(graph)
return filename
# endregion
# region data
@app.callback(
Output('add_info_model_choice', 'on'),
Input('ml_model_choice', 'value'),
prevent_initial_call=True
)
def delete_info(ml_type):
model_application.update_info_needed(False)
return False
@app.callback(
Output('choice_info_div', 'hidden'),
Input('add_info_model_choice', 'on'),
prevent_initial_call=True
)
def add_model_info(add_info_model_choice):
model_application.update_info_needed(add_info_model_choice)
if add_info_model_choice:
return False
else:
return True
@app.callback(Output('info_filename', 'children'),
Output('intermediate-value-data', 'data'),
Input('ml_model_choice', 'value'),
Input('model_info_choice', 'filename'),
Input('model_info_choice', 'contents'),
State('model_info_choice', 'filename'),
prevent_initial_call=True
)
def update_model_info_filename(value_ml_model, model_info_filename):
def select_data(ml_type, data, filename):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered[0]['prop_id'].split('.')[0]
ihm_id = ctx.triggered_id
if ihm_id == 'ml_model_choice':
return None
return None, None
else:
return model_info_filename
model_info = parse_contents_data(data, filename)
model_application.update_info(model_info)
return filename, model_info
# endregion
# region instance
@app.callback(Output('instance_filename', 'children'),
Input('ml_model_choice', 'value'),
Input('ml_pretrained_model_choice', 'contents'),
Input('ml_instance_choice', 'filename'),
Input('ml_instance_choice', 'contents'),
State('ml_instance_choice', 'filename'),
Input('number_explanations', 'value'),
Input('explanation_type', 'value'),
Input('solver_sat', 'value'),
prevent_initial_call=True
)
def update_instance_filename(value_ml_model, model_info_filename, instance_filename):
def select_instance(ml_type, model, instance, filename, enum, xtype, solver):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered[0]['prop_id'].split('.')[0]
if ihm_id == 'ml_model_choice':
return None
elif ihm_id == 'ml_pretrained_model_choice':
ihm_id = ctx.triggered_id
if ihm_id == 'ml_model_choice' or ihm_id == 'ml_pretrained_model_choice':
return None
else:
return instance_filename
# Choice of number of expls
if ihm_id == 'number_explanations':
model_application.update_enum(enum)
# Choice of AxP or CxP
elif ihm_id == 'explanation_type':
model_application.update_xtype(xtype)
# Choice of solver
elif ihm_id == 'solver_sat':
model_application.update_solver(solver)
else:
instance = parse_contents_instance(instance, filename)
model_application.update_instance(instance)
return filename
@app.callback(
Output('choice_info_div', 'hidden'),
Input('add_info_model_choice', 'on'),
prevent_initial_call=True
)
def add_model_info(add_info_model_choice):
model_application = page_application.model
model_application.update_info_needed(add_info_model_choice)
if add_info_model_choice:
return False
else:
return True
# endregion
# region draw
@app.callback(
Output('graph', 'children'),
Output('explanation', 'children'),
Input('ml_model_choice', 'value'),
Input('ml_pretrained_model_choice', 'contents'),
State('ml_pretrained_model_choice', 'filename'),
Input('model_info_choice', 'contents'),
Input('submit-model', 'n_clicks'),
State('model_info_choice', 'filename'),
Input('ml_instance_choice', 'contents'),
State('ml_instance_choice', 'filename'),
Input('number_explanations', 'value'),
Input('explanation_type', 'value'),
Input('solver_sat', 'value'),
Input('expl_choice', 'value'),
Input('cont_expl_choice', 'value'),
Input('choice_tree', 'value'),
prevent_initial_call=True
)
def update_ml_type(value_ml_model, pretrained_model_contents, pretrained_model_filename, model_info,
model_info_filename, instance_contents, instance_filename, enum, xtype, solver, expl_choice,
cont_expl_choice, id_tree):
prevent_initial_call=True)
def draw_model(ml_type, model, click, model_info_filename, expl_choice, cont_expl_choice, id_tree):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered[0]['prop_id'].split('.')[0]
model_application = page_application.model
# Choice of model
if ihm_id == 'ml_model_choice':
model_application.update_ml_model(value_ml_model)
return None, None
# Choice of information for the model - data, feature mapping ...
elif ihm_id == 'model_info_choice':
try:
model_info = parse_contents_data(model_info, model_info_filename)
model_application.update_info(model_info)
try:
if ctx.triggered:
ihm_id = ctx.triggered_id
if ihm_id == "ml_model_choice" :
return reinit
elif ihm_id == "ml_pretrained_model_choice":
return init_network
elif ihm_id == "submit-model":
if model_application.ml_model is None:
return warning_selection_model, None
elif model_application.pretrained_model is not None:
model_application.update_pretrained_model_layout_with_info(model_info_filename)
return model_application.component.network, None
else:
return warning_selection_model, None
except:
return warning_selection_pretrained_model, None
# Choice of pkl pretrained model
elif ihm_id == 'ml_pretrained_model_choice':
try:
graph = parse_contents_graph(pretrained_model_contents, pretrained_model_filename)
model_application.update_pretrained_model(graph)
if not model_application.add_info:
return warning_selection_model
elif not model_application.add_info:
model_application.update_pretrained_model_layout()
return model_application.component.network, None
return model_application.component.network
elif model_application.model_info is not None:
model_application.update_pretrained_model_layout_with_info(model_info_filename)
return model_application.component.network, None
return model_application.component.network
else:
return warning_selection_data, None
except:
return alert_network, None
# Choice of instance to explain
elif ihm_id == 'ml_instance_choice':
try:
instance = parse_contents_instance(instance_contents, instance_filename)
model_application.update_instance(instance)
return model_application.component.network, model_application.component.explanation
except:
return model_application.component.network, alert_explanation
# Choice of number of expls
elif ihm_id == 'number_explanations':
try:
model_application.update_enum(enum)
return model_application.component.network, model_application.component.explanation
except:
return model_application.component.network, alert_explanation
# Choice of AxP or CxP
elif ihm_id == 'explanation_type':
try:
model_application.update_xtype(xtype)
return model_application.component.network, model_application.component.explanation
except:
return model_application.component.network, alert_explanation
# Choice of solver
elif ihm_id == 'solver_sat':
try:
model_application.update_solver(solver)
return model_application.component.network, model_application.component.explanation
except:
return model_application.component.network, alert_explanation
return warning_selection_data
# Choice of AxP to draw
elif ihm_id == 'expl_choice':
try:
elif ihm_id == 'expl_choice':
model_application.update_expl(expl_choice)
return model_application.component.network, model_application.component.explanation
except:
return alert_network, model_application.component.explanation
return model_application.component.network
# Choice of CxP to draw
elif ihm_id == 'cont_expl_choice':
try:
# Choice of CxP to draw
elif ihm_id == 'cont_expl_choice':
model_application.update_cont_expl(cont_expl_choice)
return model_application.component.network, model_application.component.explanation
except:
return alert_network, model_application.component.explanation
return model_application.component.network
# In the case of RandomForest, id of tree to choose to draw tree
elif ihm_id == 'choice_tree':
try:
# In the case of RandomForest, id of tree to choose to draw tree
elif ihm_id == 'choice_tree':
model_application.update_tree_to_plot(id_tree)
return model_application.component.network, model_application.component.explanation
except:
return alert_network, model_application.component.explanation
return model_application.component.network
except:
return alert_network
# endregion
# region explanation
@app.callback(
Output('explanation', 'hidden'),
Input('explanation', 'children'),
Input('explanation_type', 'value'),
Input('submit-instance', 'n_clicks'),
Input('ml_model_choice', 'value'),
Input('ml_pretrained_model_choice', 'contents'),
Input('ml_instance_choice', 'contents'),
prevent_initial_call=True
)
def show_explanation_window(explanation, explanation_type):
if explanation is None or len(explanation_type) == 0:
return True
else:
return False
def show_explanation_window(click, ml_type, model, instance):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered_id
if ihm_id == "ml_model_choice" or ihm_id == "ml_pretrained_model_choice" or ihm_id == "ml_instance_choice":
return True
elif ihm_id == "submit-instance":
return False
else :
return True
@app.callback(
Output('explanation', 'children'),
Input('ml_model_choice', 'value'),
Input('ml_pretrained_model_choice', 'contents'),
Input('ml_instance_choice', 'contents'),
Input('submit-instance', 'n_clicks'),
prevent_initial_call=True)
def run_explanation(ml_type, model, instance, click):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered_id
if ihm_id == "ml_model_choice" or ihm_id == "ml_pretrained_model_choice" or ihm_id == "ml_instance_choice":
return reinit
elif ihm_id == "submit-instance":
try:
if model_application.ml_model is None:
return warning_selection_model
elif model_application.pretrained_model is None:
return warning_selection_pretrained_model
elif model_application.instance is not None:
return model_application.component.explanation
else:
return warning_selection_data
except:
return alert_explanation
# endregion
########### RandomForest ###########
@app.callback(
Output('choosing_tree', 'hidden'),
Input('ml_model_choice', 'value'),
Input('graph', 'children'),
prevent_initial_call=True
)
def choose_tree_in_forest(ml_type, graph):
if ml_type == "RandomForest" and graph is not None:
def choose_tree_in_forest(ml_type):
if ml_type == "RandomForest":
return False
else:
return True
......@@ -318,5 +348,4 @@ def register_callbacks(page_home, page_course, page_application, app):
if not bool_draw:
return True, {}, {}
else:
model_application = page_application.model
return False, model_application.options_expls, model_application.options_cont_expls
pages/application/application.py
+ 15
− 18
View file @ 09e7fa2e
......@@ -5,14 +5,6 @@ import dash_daq as daq
from pages.application.DecisionTree.DecisionTreeComponent import DecisionTreeComponent
from pages.application.RandomForest.RandomForestComponent import RandomForestComponent
class Application:
def __init__(self, view):
self.view = view
self.model = view.model
class Model:
def __init__(self, names_models, dict_components, dic_solvers, dic_xtypes):
......@@ -55,7 +47,7 @@ class Model:
self.xtypes = self.dic_xtypes[self.ml_model]
self.xtype = [list(self.xtypes.keys())[0]]
#init all params
# init all params
self.component = None
self.pretrained_model = None
self.model_info = None
......@@ -77,6 +69,8 @@ class Model:
self.cont_expl = None
def update_info_needed(self, add_info):
if not add_info:
self.model_info = None
self.add_info = add_info
def update_info(self, model_info):
......@@ -95,7 +89,7 @@ class Model:
def update_instance(self, instance):
self.instance = instance
list_expls, list_cont_expls = self.component.update_with_explicability(self.instance, self.enum,
self.xtype, self.solver)
self.xtype, self.solver)
self.options_expls = {}
self.options_cont_expls = {}
for i in range(len(list_expls)):
......@@ -106,7 +100,7 @@ class Model:
def update_enum(self, enum):
self.enum = enum
list_expls, list_cont_expls = self.component.update_with_explicability(self.instance, self.enum,
self.xtype, self.solver)
self.xtype, self.solver)
self.options_expls = {}
self.options_cont_expls = {}
for i in range(len(list_expls)):
......@@ -114,11 +108,10 @@ class Model:
for i in range(len(list_cont_expls)):
self.options_cont_expls[str(list_cont_expls[i])] = list_cont_expls[i]
def update_xtype(self, xtype):
self.xtype = xtype
list_expls, list_cont_expls = self.component.update_with_explicability(self.instance, self.enum,
self.xtype, self.solver)
self.xtype, self.solver)
self.options_expls = {}
self.options_cont_expls = {}
for i in range(len(list_expls)):
......@@ -129,7 +122,7 @@ class Model:
def update_solver(self, solver):
self.solver = solver
list_expls, list_cont_expls = self.component.update_with_explicability(self.instance, self.enum,
self.xtype, self.solver)
self.xtype, self.solver)
self.options_expls = {}
self.options_cont_expls = {}
for i in range(len(list_expls)):
......@@ -241,8 +234,12 @@ class View:
self.ml_menu_models,
self.add_model_info_choice,
self.model_info,
dcc.Store(id='intermediate-value-data'),
self.pretrained_model_upload,
self.instance_upload], className="sidebar"),
html.Button('Submit model', id='submit-model', n_clicks=0, className="button"),
html.Hr(),
self.instance_upload,
html.Button('Submit instance', id='submit-instance', n_clicks=0, className="button"),], className="sidebar"),
dcc.Tab(label='Advanced Parameters', children=[
html.Br(),
self.num_explanation,
......@@ -251,9 +248,9 @@ class View:
], className="sidebar")])
self.switch = html.Div(id="div_switcher_draw_expl", hidden=True,
children=[html.Label("Draw explanations ?"),
html.Br(),
daq.BooleanSwitch(id='drawing_expl', on=False, color="#FFFFFF", )])
children=[html.Label("Draw explanations ?"),
html.Br(),
daq.BooleanSwitch(id='drawing_expl', on=False, color="#FFFFFF", )])
self.expl_choice = html.Div(id="interaction_graph", hidden=True,
children=[html.H5("Navigate through the explanations and plot them on the tree : "),
......
requirements.txt
+ 3
− 2
View file @ 09e7fa2e
......@@ -7,7 +7,7 @@ shap==0.40.0
anchor-exp==0.0.2.0
pysmt==0.9.0
anytree==2.8.0
dash==2.1.0
dash==2.4.0
dash_bootstrap_components==1.0.3
dash_daq==0.5.0
dash_interactive_graphviz==0.3.0
......@@ -21,4 +21,5 @@ scikit_learn==1.0.2
six==1.16.0
gunicorn==20.1.0
Werkzeug==2.0.1
pydot==1.4.2
\ No newline at end of file
pydot==1.4.2
Flask-Caching==1.8
\ No newline at end of file
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