core: extracted static folder from app base into configs

.gitignore

@@ -20,6 +20,7 @@ virtual_env
 .vscode/
 .idea
 .git
+instance
 
 # sphinx docs 
 _build

Dashboard/Dash_fun.py

@@ -58,4 +58,3 @@ def serve_layout():
     app.config.suppress_callback_exceptions = True
     app.layout = serve_layout
 
-    print('App Layout: ', app.layout)

Dashboard/dataset/datasetLayout.py

@@ -1,3 +1,4 @@
+import plotly
 from dash import dcc, html, dash_table
 from dash.dependencies import Input, Output, State
 from dash.exceptions import PreventUpdate
@@ -33,8 +34,11 @@ def generateDataSetDistributions(df: pd.DataFrame, feature, legendTranslations):
             line=dict(color="royalblue", width=1, dash="dot"),
         ))
         graph['graph_data'].append(
-            go.Bar(name=setText(legendTranslations, 'bars', 'dashboard.data.common.distributions.legend'), x=x, y=y,
-                   width=0.5))
+            go.Bar(
+                name=setText(legendTranslations, 'bars', 'dashboard.data.common.distributions.legend'),
+                x=x, y=y, width=0.5,
+                marker=dict(color=['#095e49' for _ in range(len(x))])
+            ))
     return graph
 
 
@@ -209,7 +213,7 @@ def datasetLayout(dataTranslations):
                         ),
                     ], className="container")
                 ],
-            )
+            ),
         ],
         className="section-content",
         style={"margin": "auto"},
@@ -463,6 +467,7 @@ def replace_empty_values(x):
                                 y=corr_matrix.columns,
                                 text=round(corr_matrix, 2),
                                 texttemplate="%{text}",
+                                colorscale=['#c6efce', '#8bc34a', '#2f4f4f']
                             ),
                         )
                     )

Dashboard/importances/importancesLayout.py

@@ -100,7 +100,7 @@ def importancesLayout(importanceTranslations):
                                                 'dashboard.importance.classifier'),
                         )
                     ], id='selector-container', hidden=True)),
-                ], className="container")
+                ], className="container"),
             ],
         ),
         style={"padding-left": "30px", "padding-right": "30px", "margin": "auto"},
@@ -154,6 +154,7 @@ def graph_explainers(cl, positive_class):
                 x=setText(translationsGini, 'label-x', 'dashboard.importance.common.gini'),
                 y=setText(translationsGini, 'label-y', 'dashboard.importance.common.gini'),
                 title=setText(translationsGini, 'title', 'dashboard.importance.common.gini'),
+                color_discrete_sequence=['#0a5b35']
             )
 
             if not isRegressor:
@@ -209,6 +210,7 @@ def graph_explainers(cl, positive_class):
                     "importances_mean":
                         setText(translationsPermutation, 'label-x', 'dashboard.importance.common.permutation')
                 },
+                color_discrete_sequence=['#0b787d']
             )
             classifier_hint = html.Strong(setText(translationsClassifier, 'hint', 'dashboard.importance.classifier'))
             return (

Dashboard/metrics/metricsLayout.py

@@ -139,7 +139,8 @@ def generateDependencePlots(X: pd.DataFrame, qualitative_dict,
         graph['graph_data'].append(
             go.Bar(
                 name=setText(legendTranslations, 'bars', 'dashboard.metrics.regressor.partial-dependence.legend'),
-                x=x, y=y, width=0.5
+                x=x, y=y, width=0.5,
+                marker=dict(color=['#095e49' for _ in range(len(x))])
             )
         )
 
@@ -487,7 +488,7 @@ def __create_matrix(cm, class_names, matrixTranslations):
         x=class_names,
         y=class_names,
         text_auto=True,
-        color_continuous_scale="Blues",
+        color_continuous_scale=["white", "aqua", "#095e49"],
     )
     return fig
 
@@ -1019,15 +1020,19 @@ def graph_pred_vs_real(cl):
                 )
                 y = regressor_dataset[model_x.getElement('target_row')]
                 y_pred = regressor_model.predict(regressor_dataset.drop(columns=model_x.getElement('target_row')))
-                fig = px.scatter(x=y, y=y_pred, title=setText(regressorPredRealTranslations, 'title',
-                                                              'dashboard.metrics.regressor.prediction-real'),
-                                 marginal_y='histogram',
-                                 labels={
-                                     'x': setText(regressorPredRealLabelsTranslations, 'x',
-                                                  'dashboard.metrics.regressor.prediction-real.labels'),
-                                     'y': setText(regressorPredRealLabelsTranslations, 'y',
-                                                  'dashboard.metrics.regressor.prediction-real.labels')
-                                 })
+                fig = px.scatter(
+                    x=y, y=y_pred,
+                    title=setText(regressorPredRealTranslations, 'title',
+                                  'dashboard.metrics.regressor.prediction-real'),
+                    marginal_y='histogram',
+                    labels={
+                        'x': setText(regressorPredRealLabelsTranslations, 'x',
+                                     'dashboard.metrics.regressor.prediction-real.labels'),
+                        'y': setText(regressorPredRealLabelsTranslations, 'y',
+                                     'dashboard.metrics.regressor.prediction-real.labels')
+                    },
+                    color_discrete_sequence=['#0a5b35']
+                )
                 fig.add_shape(
                     type="line", line=dict(dash='dash'),
                     x0=y.min(), y0=y.min(),
@@ -1036,15 +1041,19 @@ def graph_pred_vs_real(cl):
 
                 fig_1 = dcc.Graph(figure=fig)
 
-                fig = px.scatter(x=y_pred, y=y, title=setText(regressorRealPredTranslations, 'title',
-                                                              'dashboard.metrics.regressor.real-prediction'),
-                                 marginal_y='histogram',
-                                 labels={
-                                     'x': setText(regressorRealPredLabelsTranslations, 'x',
-                                                  'dashboard.metrics.regressor.real-prediction.labels'),
-                                     'y': setText(regressorRealPredLabelsTranslations, 'y',
-                                                  'dashboard.metrics.regressor.real-prediction.labels')
-                                 })
+                fig = px.scatter(
+                    x=y_pred, y=y,
+                    title=setText(regressorRealPredTranslations, 'title',
+                                  'dashboard.metrics.regressor.real-prediction'),
+                    marginal_y='histogram',
+                    labels={
+                        'x': setText(regressorRealPredLabelsTranslations, 'x',
+                                     'dashboard.metrics.regressor.real-prediction.labels'),
+                        'y': setText(regressorRealPredLabelsTranslations, 'y',
+                                     'dashboard.metrics.regressor.real-prediction.labels')
+                    },
+                    color_discrete_sequence=['#0b787d']
+                )
                 fig.add_shape(
                     type="line", line=dict(dash='dash'),
                     x0=y_pred.min(), y0=y_pred.min(),

Dashboard/predictions/predictionsLayout.py

@@ -126,7 +126,7 @@ def getTreeInterpreterParamethersRegressor(
                  f" ({round(point, 3)}) "
                  f"{round((point / (prediction[0][0] if prediction[0][0] > 0 else 1) * 100), 2)} %",
             line=dict(dash="dash"),
-            marker_color=["blue", "blue"],
+            marker_color=["#095e49", "#095e49"],
         )
     )
 
@@ -240,7 +240,7 @@ def getTreeInterpreterParamethersClassifier(
                     mode="lines",
                     name=f"{actual} ({round(point, 3)}) {round((point / (prediction[0][index] if prediction[0][index] > 0 else 1) * 100), 2)} %",
                     line=dict(dash="dash"),
-                    marker_color=["blue", "blue"],
+                    marker_color=["#095e49", "#095e49"],
                 )
             )
 
@@ -262,6 +262,7 @@ def getIndividualPredictionsRegressor(model: RandomForestRegressor, instance, tr
             name=setText(treesTranslations, 'bar-title', 'dashboard.predictions.regressor.trees'),
             y=np.round(sorted(individual_predictions), 2),
             x=x[1:],
+            marker=dict(color=['#095e49' for _ in range(len(x[1:]))])
         ),
         go.Scatter(
             name=setText(treesTranslations, 'line-title', 'dashboard.predictions.regressor.trees'),
@@ -295,19 +296,19 @@ def getIndividualPredictionsClassifier(model, class_names, instance, cut_point,
     markers = []
     for val in np.array(individual_predictions)[:, index]:
         if val * 100 >= cut_point:
-            markers.append("blue")
+            markers.append("#095e49")
         else:
-            markers.append("red")
+            markers.append("#ba3333")
 
     sorted_predictions = sorted(
         zip(np.array(individual_predictions)[:, index], markers),
-        key=lambda x: (-x[0] if markers.count("blue") > markers.count("red") else x[0]),
+        key=lambda x: (-x[0] if markers.count("#095e49") > markers.count("#ba3333") else x[0]),
     )
     predictions_for_actual_clase = np.array(sorted_predictions)[:, 0]
     prev_x = list(range(len(predictions_for_actual_clase) + 1))
     x = (
         prev_x[1:]
-        if markers.count("blue") > markers.count("red")
+        if markers.count("#095e49") > markers.count("#ba3333")
         else prev_x[::-1][:-1]
     )
     y = np.round(predictions_for_actual_clase.astype(np.float64) * 100, 2)
@@ -320,7 +321,7 @@ def getIndividualPredictionsClassifier(model, class_names, instance, cut_point,
             marker_color=np.array(sorted_predictions)[:, 1],
         ), go.Scatter(
             name=setText(treesTranslations, 'line-title', 'dashboard.predictions.classifier.trees'),
-            x=[1, x[0]] if markers.count("blue") < markers.count("red") else [x[-1], 1],
+            x=[1, x[0]] if markers.count("#095e49") < markers.count("#ba3333") else [x[-1], 1],
             y=[cut_point, cut_point],
             mode="lines",
             line=dict(dash="dash"),
@@ -335,7 +336,7 @@ def getIndividualPredictionsClassifier(model, class_names, instance, cut_point,
         bargap=0.1,
         xaxis=dict(
             autorange=(
-                "reversed" if markers.count("blue") > markers.count("red") else True
+                "reversed" if markers.count("#095e49    ") > markers.count("#ba3333") else True
             )
         ),
     )

LICENSE

@@ -1,21 +1,4 @@
 MIT License
 
-Copyright (c) 2017 Antoine Fourmy
+Copyright (c) 2024 Manuel Alfonso Rodríguez CUJAE
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -10,10 +10,6 @@ This project integrates Gentelella with Flask using:
 - [flask_migrate](https://flask-migrate.readthedocs.io/en/latest/).
 - [dash](https://dash.plot.ly/).
 
-Here is an example of a real project implemented using Flask-Gentelella:
-- [Online demo](http://afourmy.pythonanywhere.com/)
-- [Source code](https://github.com/afourmy/eNMS)
-
 ##  Install requirements 
     pipenv sync
     or