--- a
+++ b/openomics_web/app.py
@@ -0,0 +1,209 @@
+import dash
+import dash_html_components as html
+from dash.dependencies import Input, Output, State
+
+from openomics import MultiOmics
+from openomics_web.layouts import app_layout
+from openomics_web.layouts.clinical_view import ClinicalDataColumnSelect, ClinicalDataTable
+from openomics_web.layouts.datatable_view import ExpressionDataTable, DataTableColumnSelect, split_filter_part
+from openomics_web.server import server
+from openomics_web.utils.io import get_table_columns, get_expression_data, get_clinical_data
+
+external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
+
+# running directly with Python
+app = dash.Dash(__name__,
+                server=server,
+                external_stylesheets=external_stylesheets)
+
+app.layout = app_layout.app_main()
+
+user_multiomics = MultiOmics(cohort_name="TEST", )
+
+
+@app.callback([
+    Output('data-table-column-select', 'children'),
+    Output('upload-data-table', 'children')
+], [
+    Input('upload-data-table', 'contents'),
+    Input('upload-data-table', 'filename')
+], [
+    State('data-table-type', 'value'),
+])
+def update_datatable_metadata(
+    list_of_contents,
+    list_of_names,
+    data_type,
+):
+    """
+    Args:
+        list_of_contents:
+        list_of_names:
+        data_type:
+    """
+    if list_of_contents is None:
+        return None, ['Drag and Drop or ', html.A('Select Files')]
+
+    try:
+        columns = get_table_columns(list_of_contents, list_of_names)
+
+    except Exception as e:
+        print(e)
+        return None, 'There was an error processing this file.'
+
+    return DataTableColumnSelect(columns), "Uploaded {}".format(list_of_names)
+
+
+@app.callback(Output('output-data-upload', 'children'),
+              [Input('upload-data-table-submit', 'n_clicks')], [
+                  State('data-table-cohort', 'value'),
+                  State('data-table-type', 'value'),
+                  State('upload-data-table', 'contents'),
+                  State('upload-data-table', 'filename'),
+                  State('data-table-genes-col-name', 'value'),
+                  State('data-table-columns-select', 'value'),
+                  State('data-table-transpose', 'value')
+])
+def import_datatable_upload(n_clicks, cohort_name, data_type, list_of_contents,
+                            list_of_names, genes_col_name, columns_select,
+                            transposed):
+    """
+    Args:
+        n_clicks:
+        cohort_name:
+        data_type:
+        list_of_contents:
+        list_of_names:
+        genes_col_name:
+        columns_select:
+        transposed:
+    """
+    if list_of_contents is None:
+        return []
+    try:
+
+        omics_data = get_expression_data(list_of_contents, list_of_names,
+                                         data_type, cohort_name,
+                                         genes_col_name, columns_select,
+                                         transposed)
+        user_multiomics.add_omic(omics_data)
+    except Exception as e:
+        print(e)
+        return html.Div(['There was an error processing this file.'])
+
+    return ExpressionDataTable(omics_data.expressions.head(20))
+
+
+@app.callback(Output('expression-datatable', "data"), [
+    Input('expression-datatable', "page_current"),
+    Input('expression-datatable', "page_size"),
+    Input('expression-datatable', "sort_by"),
+    Input('expression-datatable', "filter_query")
+])
+def update_table(page_current, page_size, sort_by, filter):
+    """
+    Args:
+        page_current:
+        page_size:
+        sort_by:
+        filter:
+    """
+    filtering_expressions = filter.split(' && ')
+    print(user_multiomics.get_omics_list())
+    dff = user_multiomics[user_multiomics.get_omics_list()[0]]
+    for filter_part in filtering_expressions:
+        col_name, operator, filter_value = split_filter_part(filter_part)
+
+        if operator in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'):
+            # these operators match pandas series operator method names
+            dff = dff.loc[getattr(dff[col_name], operator)(filter_value)]
+        elif operator == 'contains':
+            dff = dff.loc[dff[col_name].str.contains(filter_value)]
+        elif operator == 'datestartswith':
+            # this is a simplification of the front-end filtering logic,
+            # only works with complete fields in standard format
+            dff = dff.loc[dff[col_name].str.startswith(filter_value)]
+
+    if sort_by:
+        dff = dff.sort_values(
+            [col['column_id'] for col in sort_by],
+            ascending=[col['direction'] == 'asc' for col in sort_by],
+            inplace=False)
+
+    return dff.iloc[page_current * page_size:(page_current + 1) *
+                    page_size].to_dict('records')
+
+
+@app.callback(
+    [
+        Output('clinical-column-select', 'children'),
+        Output('upload-clinical', 'children')
+    ],
+    [
+        Input('upload-clinical', 'contents'),
+        Input('upload-clinical', 'filename')
+    ],
+)
+def update_clinical_upload_metadata(
+    file_content,
+    file_name,
+):
+    """
+    Args:
+        file_content:
+        file_name:
+    """
+    if file_content is None:
+        return None, ['Drag and Drop or ', html.A('Select Files')]
+
+    try:
+        columns = get_table_columns([
+            file_content,
+        ], [
+            file_name,
+        ])
+
+    except Exception as e:
+        print(e)
+        return None, 'There was an error processing this file.'
+
+    return ClinicalDataColumnSelect(columns), "Uploaded {}".format(file_name)
+
+
+@app.callback(Output('output-clinical-upload', 'children'),
+              [Input('clinical-submit-button', 'n_clicks')], [
+                  State('clinical-cohort', 'value'),
+                  State('clinical-data-type', 'value'),
+                  State('upload-clinical', 'contents'),
+                  State('upload-clinical', 'filename'),
+                  State('clinical-patient-col-name', 'value'),
+                  State('clinical-data-columns-select', 'value'),
+])
+def import_datatable_upload(n_clicks, cohort_name, data_type, list_of_contents,
+                            list_of_names, patient_id_col, columns_select):
+    """
+    Args:
+        n_clicks:
+        cohort_name:
+        data_type:
+        list_of_contents:
+        list_of_names:
+        patient_id_col:
+        columns_select:
+    """
+    if list_of_contents is None:
+        return []
+    try:
+        clinical_data = get_clinical_data(list_of_contents, list_of_names,
+                                          data_type, cohort_name,
+                                          patient_id_col, columns_select)
+        user_multiomics.add_clinical_data(clinical_data)
+    except Exception as e:
+        print(e)
+        return html.Div(['There was an error processing this file.'])
+
+    return ClinicalDataTable(clinical_data.patient.head(20))
+
+
+if __name__ == '__main__':
+    app.run_server(debug=False, port=8050)