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diff --git a/resources/tools/dash/app/pal/trending/trending.py b/resources/tools/dash/app/pal/trending/trending.py
new file mode 100644
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+++ b/resources/tools/dash/app/pal/trending/trending.py
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+# Copyright (c) 2022 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Instantiate the Trending Dash applocation.
+"""
+import dash
+from dash import dcc
+from dash import html
+from dash import dash_table
+import numpy as np
+import pandas as pd
+
+from .data import create_dataframe
+from .layout import Layout
+
+
+def init_trending(server):
+    """Create a Plotly Dash dashboard.
+
+    :param server: Flask server.
+    :type server: Flask
+    :returns: Dash app server.
+    :rtype: Dash
+    """
+
+    dash_app = dash.Dash(
+        server=server,
+        routes_pathname_prefix=u"/trending/",
+        external_stylesheets=[
+            u"/static/dist/css/styles.css",
+            u"https://fonts.googleapis.com/css?family=Lato",
+        ],
+    )
+
+    # Load DataFrame
+    df = create_dataframe()
+
+    # Custom HTML layout
+    layout = Layout(
+        app=dash_app,
+        html_layout_file="pal/trending/html_layout.txt",
+        spec_file="pal/trending/spec_test_selection.yaml"
+    )
+    dash_app.index_string = layout.html_layout
+    dash_app.layout = layout.add_content()
+
+    return dash_app.server
+
+
+def create_data_table(df):
+    """Create Dash datatable from Pandas DataFrame.
+
+    DEMO
+    """
+
+    table = dash_table.DataTable(
+        id=u"database-table",
+        columns=[{u"name": i, u"id": i} for i in df.columns],
+        data=df.to_dict(u"records"),
+        sort_action=u"native",
+        sort_mode=u"native",
+        page_size=10,
+    )
+    return table