Update gui.py

gui.py

@@ -1,386 +1,388 @@
-import panel as pn
-import pandas as pd
-import param
-from bokeh.models.formatters import PrintfTickFormatter
-
-from calculations import CannabinoidCalculations
-from config import slider_design, slider_style, slider_stylesheet, get_formatter
-
-
-class CannabinoidEstimatorGUI(CannabinoidCalculations):
-    # DataFrame params for tables
-    money_data_unit_df = param.DataFrame(
-        pd.DataFrame(),
-        precedence=-1,  # precedence to hide from param pane if shown
-    )
-    money_data_time_df = param.DataFrame(pd.DataFrame(), precedence=-1)
-    profit_data_df = param.DataFrame(pd.DataFrame(), precedence=-1)
-    processing_data_df = param.DataFrame(pd.DataFrame(), precedence=-1)
-
-    def __init__(self, **params):
-        super().__init__(**params)
-        self._create_sliders()
-        self._create_tables_and_indicators()
-        self._update_calculations()  # Initial calculation and table update
-
-    def _create_sliders(self):
-        self.batch_frequency_radio = pn.widgets.RadioButtonGroup.from_param(
-            self.param.batch_frequency,
-            name=self.param.batch_frequency.label,
-            options=["Shift", "Day", "Week"],
-            button_type="primary",
-        )
-
-    def _create_tables_and_indicators(self):
-        # Table for $/kg Biomass and $/kg Output
-        self.money_unit_table = pn.widgets.Tabulator(
-            self.money_data_unit_df,  # Initial empty or pre-filled df
-            formatters={
-                "$/kg Biomass": get_formatter("$%.02f"),
-                "$/kg Output": get_formatter("$%.02f"),
-            },
-            disabled=True,
-            layout="fit_data",
-            sizing_mode="fixed",
-            align="center",
-            show_index=False,
-            text_align={
-                " ": "right",
-                "$/kg Biomass": "center",
-                "$/kg Output": "center",
-            },
-        )
-        # Table for Per Shift, Per Day, Per Week
-        self.money_time_table = pn.widgets.Tabulator(
-            self.money_data_time_df,  # Initial empty or pre-filled df
-            formatters={
-                "Per Shift": get_formatter("$%.02f"),
-                "Per Day": get_formatter("$%.02f"),
-                "Per Week": get_formatter("$%.02f"),
-            },
-            disabled=True,
-            layout="fit_data",
-            sizing_mode="fixed",
-            align="center",
-            show_index=False,
-            text_align={
-                " ": "right",
-                "Per Shift": "center",
-                "Per Day": "center",
-                "Per Week": "center",
-            },
-        )
-        self.profit_table = pn.widgets.Tabulator(
-            self.profit_data_df,  # Initial empty or pre-filled df
-            disabled=True,
-            layout="fit_data_table",
-            sizing_mode="fixed",
-            align="center",
-            show_index=False,
-            text_align={"Metric": "right", "Value": "center"},
-        )
-        self.processing_table = pn.widgets.Tabulator(
-            self.processing_data_df,  # Initial empty or pre-filled df
-            formatters={},
-            disabled=True,
-            layout="fit_data_table",
-            sizing_mode="fixed",
-            align="center",
-            show_index=False,
-            text_align={"Metric (Per Shift)": "right", "Value": "center"},
-        )
-        self.profit_weekly = pn.indicators.Number(
-            name="Weekly Profit",
-            value=0,
-            format="$0 k",
-            default_color="green",
-            align="center",
-        )
-        self.profit_pct = pn.indicators.Number(
-            name="Operating Profit",
-            value=0,
-            format="0.00%",
-            default_color="green",
-            align="center",
-        )
-
-    @param.depends("labour_hours_per_shift", watch=True)
-    def _update_processing_hours_slider_constraints(self):
-        new_max_processing_hours = self.labour_hours_per_shift
-        # Ensure min bound is not greater than new max bound
-        current_min_processing_hours = min(
-            self.param.processing_hours_per_shift.bounds[0], new_max_processing_hours
-        )
-
-        self.param.processing_hours_per_shift.bounds = (
-            current_min_processing_hours,
-            new_max_processing_hours,
-        )
-        # Check if processing_hours_per_shift_slider exists before trying to update it
-        if hasattr(self, "processing_hours_per_shift_slider"):
-            self.processing_hours_per_shift_slider.end = new_max_processing_hours
-            if self.processing_hours_per_shift > new_max_processing_hours:
-                self.processing_hours_per_shift = new_max_processing_hours
-            # Also update start if it's now greater than end
-            if self.processing_hours_per_shift_slider.start > new_max_processing_hours:
-                self.processing_hours_per_shift_slider.start = (
-                    current_min_processing_hours  # or new_max_processing_hours
-                )
-
-    def _post_calculation_update(self):
-        """Overrides the base class method to update GUI elements."""
-        super()._post_calculation_update()  # Call base class method if it has any logic
-        self._update_tables_data()
-
-    def _update_tables_data(self):
-        metric_names = [
-            "Biomass cost",
-            "Processing cost",
-            "Gross Revenue",
-            "Net Revenue",
-        ]
-        money_data_unit_dict = {
-            " ": metric_names,
-            "$/kg Biomass": [
-                self.bio_cost,
-                self.internal_cogs_per_kg_bio,
-                self.gross_rev_per_kg_bio,
-                self.net_rev_per_kg_bio,
-            ],
-            "$/kg Output": [
-                self.biomass_cost_per_kg_output,
-                self.internal_cogs_per_kg_output,
-                self.wholesale_cbx_price,
-                self.net_rev_per_kg_output,
-            ],
-        }
-        self.money_data_unit_df = pd.DataFrame(money_data_unit_dict)
-        if hasattr(self, "money_unit_table"):
-            self.money_unit_table.value = self.money_data_unit_df
-
-        money_data_time_dict = {
-            " ": metric_names,
-            "Per Shift": [
-                self.biomass_cost_per_shift,
-                self.internal_cogs_per_shift,
-                self.gross_rev_per_shift,
-                self.net_rev_per_shift,
-            ],
-            "Per Day": [
-                self.biomass_cost_per_day,
-                self.internal_cogs_per_day,
-                self.gross_rev_per_day,
-                self.net_rev_per_day,
-            ],
-            "Per Week": [
-                self.biomass_cost_per_week,
-                self.internal_cogs_per_week,
-                self.gross_rev_per_week,
-                self.net_rev_per_week,
-            ],
-        }
-        self.money_data_time_df = pd.DataFrame(money_data_time_dict)
-        if hasattr(self, "money_time_table"):
-            self.money_time_table.value = self.money_data_time_df
-
-        profit_data_dict = {
-            "Metric": ["Operating Profit", "Resin Spread"],
-            "Value": [
-                f"{self.operating_profit_pct * 100.0:.2f}%",
-                f"{self.resin_spread_pct * 100.0:.2f}%",
-            ],
-        }
-        self.profit_data_df = pd.DataFrame(profit_data_dict)
-        if hasattr(self, "profit_table"):
-            self.profit_table.value = self.profit_data_df
-
-        processing_values_formatted_shift = [
-            f"{self.kg_processed_per_shift:,.0f}",
-            f"{self.saleable_kg_per_shift:,.0f}",
-            f"${self.labour_cost_per_shift:,.2f}",
-            f"${self.variable_cost_per_shift:,.2f}",
-            f"${self.overhead_cost_per_shift:,.2f}",
-        ]
-        processing_values_formatted_day = [
-            f"{self.kg_processed_per_shift * self.shifts_per_day:,.0f}",
-            f"{self.saleable_kg_per_day:,.0f}",
-            f"${self.labour_cost_per_shift * self.shifts_per_day:,.2f}",
-            f"${self.variable_cost_per_shift * self.shifts_per_day:,.2f}",
-            f"${self.overhead_cost_per_shift * self.shifts_per_day:,.2f}",
-        ]
-        processing_values_formatted_week = [
-            f"{self.kg_processed_per_shift * self.shifts_per_week:,.0f}",
-            f"{self.saleable_kg_per_week:,.0f}",
-            f"${self.labour_cost_per_shift * self.shifts_per_week:,.2f}",
-            f"${self.variable_cost_per_shift * self.shifts_per_week:,.2f}",
-            f"${self.overhead_cost_per_shift * self.shifts_per_week:,.2f}",
-        ]
-        processing_data_dict = {
-            "Metric Per": [
-                "Kilograms Extracted",
-                "Kg CBx Produced",
-                "Labour Cost",
-                "Variable Cost",
-                "Overhead",
-            ],
-            "Shift": processing_values_formatted_shift,
-            "Day": processing_values_formatted_day,
-            "Week": processing_values_formatted_week,
-        }
-        self.processing_data_df = pd.DataFrame(processing_data_dict)
-        if hasattr(self, "processing_table"):
-            self.processing_table.value = self.processing_data_df
-
-        if hasattr(self, "profit_weekly"):
-            self.profit_weekly.value = self.net_rev_per_week
-            # Ensure format updates if value changes significantly (e.g. from 0 to large number)
-            self.profit_weekly.format = (
-                f"${self.net_rev_per_week / 1000:.0f} k"
-                if self.net_rev_per_week != 0
-                else "$0 k"
-            )
-
-        if hasattr(self, "profit_pct"):
-            self.profit_pct.value = self.operating_profit_pct
-            self.profit_pct.format = f"{self.operating_profit_pct * 100.0:.2f}%"
-
-    def view(self):
-        input_col_max_width = 400
-        extractionCol = pn.Column(
-            "### Extraction",
-            self.param.kg_processed_per_hour,
-            self.param.finished_product_yield_pct,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        biomassCol = pn.Column(
-            pn.pane.Markdown("### Biomass parameters", margin=0),
-            self.param.bio_cbx_pct,
-            self.param.bio_cost,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        consumableCol = pn.Column(
-            pn.pane.Markdown("### Consumable rates", margin=0),
-            self.param.kwh_rate,
-            self.param.water_cost_per_1000l,
-            self.param.consumables_per_kg_bio_rate,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        wholesaleCol = pn.Column(
-            pn.pane.Markdown("### Wholesale details", margin=0),
-            self.param.wholesale_cbx_price,
-            self.param.wholesale_cbx_pct,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        variableCol = pn.Column(
-            pn.pane.Markdown("### Variable processing costs", margin=0),
-            self.param.kwh_per_kg_bio,
-            self.param.water_liters_consumed_per_kg_bio,
-            self.param.consumables_per_kg_output,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        complianceBatchCol = pn.Column(
-            pn.pane.Markdown("### Compliance", margin=0),
-            self.param.batch_test_cost,
-            pn.pane.Markdown("New Batch Every:", margin=0),
-            self.batch_frequency_radio,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        leechCol = pn.Column(
-            pn.pane.Markdown("### Weekly Rent & Fixed Overheads", margin=0),
-            self.param.weekly_rent,
-            self.param.non_production_electricity_cost_weekly,
-            self.param.property_insurance_weekly,
-            self.param.general_liability_insurance_weekly,
-            self.param.product_recall_insurance_weekly,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        workerCol = pn.Column(
-            pn.pane.Markdown("### Worker Details", margin=0),
-            self.param.workers_per_shift,
-            self.param.worker_base_pay_rate,
-            self.param.managers_per_shift,
-            self.param.manager_base_pay_rate,
-            self.param.direct_cost_pct,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        shiftCol = pn.Column(
-            pn.pane.Markdown("### Shift details", margin=0),
-            self.param.labour_hours_per_shift,
-            self.param.processing_hours_per_shift,
-            self.param.shifts_per_day,
-            self.param.shifts_per_week,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-
-        input_grid = pn.FlexBox(
-            extractionCol,
-            biomassCol,
-            consumableCol,
-            wholesaleCol,
-            variableCol,
-            complianceBatchCol,
-            workerCol,
-            shiftCol,
-            
-            leechCol,
-            align_content="flex-start",
-            align_items="flex-start",
-            # valid options include: '[stretch, flex-start, flex-end, center, baseline, first baseline, last baseline, start, end, self-start, self-end]'
-            flex_wrap="wrap",
-        )  # Added flex_wrap
-
-        money_unit_table_display = pn.Column(
-            pn.pane.Markdown(
-                "### Financial Summary (Per Unit)", styles={"text-align": "center"}
-            ),
-            self.money_unit_table,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width + 50,
-        )
-        money_time_table_display = pn.Column(
-            pn.pane.Markdown(
-                "### Financial Summary (Aggregated)", styles={"text-align": "center"}
-            ),
-            self.money_time_table,
-            sizing_mode="stretch_width",
-            max_width=500,
-        )
-        profit_table_display = pn.Column(
-            pn.pane.Markdown("### Profitability", styles={"text-align": "center"}),
-            self.profit_table,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-        processing_table_display = pn.Column(
-            pn.pane.Markdown("### Processing Summary", styles={"text-align": "center"}),
-            self.processing_table,
-            sizing_mode="stretch_width",
-            max_width=input_col_max_width,
-        )
-
-        table_grid = pn.FlexBox(
-            self.profit_weekly,
-            self.profit_pct,
-            processing_table_display,
-            profit_table_display,
-            money_unit_table_display,
-            money_time_table_display,
-            align_content="normal",
-            flex_wrap="wrap",
-        )
-        main_layout = pn.Column(
-            pn.Accordion(("Knobs & Dials",input_grid)),
-            pn.layout.Divider(margin=(10, 0)),
-            table_grid,
-            styles={"margin": "0px 10px"},
-        )
+import panel as pn
+import pandas as pd
+import param
+from bokeh.models.formatters import PrintfTickFormatter
+
+from calculations import CannabinoidCalculations
+from config import slider_design, slider_style, slider_stylesheet, get_formatter
+
+
+class CannabinoidEstimatorGUI(CannabinoidCalculations):
+    # DataFrame params for tables
+    money_data_unit_df = param.DataFrame(
+        pd.DataFrame(),
+        precedence=-1,  # precedence to hide from param pane if shown
+    )
+    money_data_time_df = param.DataFrame(pd.DataFrame(), precedence=-1)
+    profit_data_df = param.DataFrame(pd.DataFrame(), precedence=-1)
+    processing_data_df = param.DataFrame(pd.DataFrame(), precedence=-1)
+
+    def __init__(self, **params):
+        super().__init__(**params)
+        self._create_sliders()
+        self._create_tables_and_indicators()
+        self._update_calculations()  # Initial calculation and table update
+
+    def _create_sliders(self):
+        self.batch_frequency_radio = pn.widgets.RadioButtonGroup.from_param(
+            self.param.batch_frequency,
+            name=self.param.batch_frequency.label,
+            options=["Shift", "Day", "Week"],
+            button_type="primary",
+        )
+
+    def _create_tables_and_indicators(self):
+        # Table for $/kg Biomass and $/kg Output
+        self.money_unit_table = pn.widgets.Tabulator(
+            self.money_data_unit_df,  # Initial empty or pre-filled df
+            formatters={
+                "$/kg Biomass": get_formatter("$%.02f"),
+                "$/kg Output": get_formatter("$%.02f"),
+            },
+            disabled=True,
+            layout="fit_data",
+            sizing_mode="fixed",
+            align="center",
+            show_index=False,
+            text_align={
+                " ": "right",
+                "$/kg Biomass": "center",
+                "$/kg Output": "center",
+            },
+        )
+        # Table for Per Shift, Per Day, Per Week
+        self.money_time_table = pn.widgets.Tabulator(
+            self.money_data_time_df,  # Initial empty or pre-filled df
+            formatters={
+                "Per Shift": get_formatter("$%.02f"),
+                "Per Day": get_formatter("$%.02f"),
+                "Per Week": get_formatter("$%.02f"),
+            },
+            disabled=True,
+            layout="fit_data",
+            sizing_mode="fixed",
+            align="center",
+            show_index=False,
+            text_align={
+                " ": "right",
+                "Per Shift": "center",
+                "Per Day": "center",
+                "Per Week": "center",
+            },
+        )
+        self.profit_table = pn.widgets.Tabulator(
+            self.profit_data_df,  # Initial empty or pre-filled df
+            disabled=True,
+            layout="fit_data_table",
+            sizing_mode="fixed",
+            align="center",
+            show_index=False,
+            text_align={"Metric": "right", "Value": "center"},
+        )
+        self.processing_table = pn.widgets.Tabulator(
+            self.processing_data_df,  # Initial empty or pre-filled df
+            formatters={},
+            disabled=True,
+            layout="fit_data_table",
+            sizing_mode="fixed",
+            align="center",
+            show_index=False,
+            text_align={"Metric (Per Shift)": "right", "Value": "center"},
+        )
+        self.profit_weekly = pn.indicators.Number(
+            name="Weekly Profit",
+            value=0,
+            format="$0 k",
+            default_color="green",
+            align="center",
+        )
+        self.profit_pct = pn.indicators.Number(
+            name="Operating Profit",
+            value=0,
+            format="0.00%",
+            default_color="green",
+            align="center",
+        )
+
+    @param.depends("labour_hours_per_shift", watch=True)
+    def _update_processing_hours_slider_constraints(self):
+        new_max_processing_hours = self.labour_hours_per_shift
+        # Ensure min bound is not greater than new max bound
+        current_min_processing_hours = min(
+            self.param.processing_hours_per_shift.bounds[0], new_max_processing_hours
+        )
+
+        self.param.processing_hours_per_shift.bounds = (
+            current_min_processing_hours,
+            new_max_processing_hours,
+        )
+        # Check if processing_hours_per_shift_slider exists before trying to update it
+        if hasattr(self, "processing_hours_per_shift_slider"):
+            self.processing_hours_per_shift_slider.end = new_max_processing_hours
+            if self.processing_hours_per_shift > new_max_processing_hours:
+                self.processing_hours_per_shift = new_max_processing_hours
+            # Also update start if it's now greater than end
+            if self.processing_hours_per_shift_slider.start > new_max_processing_hours:
+                self.processing_hours_per_shift_slider.start = (
+                    current_min_processing_hours  # or new_max_processing_hours
+                )
+
+    def _post_calculation_update(self):
+        """Overrides the base class method to update GUI elements."""
+        super()._post_calculation_update()  # Call base class method if it has any logic
+        self._update_tables_data()
+
+    def _update_tables_data(self):
+        metric_names = [
+            "Biomass cost",
+            "Processing cost",
+            "Gross Revenue",
+            "Net Revenue",
+        ]
+        money_data_unit_dict = {
+            " ": metric_names,
+            "$/kg Biomass": [
+                self.bio_cost,
+                self.internal_cogs_per_kg_bio,
+                self.gross_rev_per_kg_bio,
+                self.net_rev_per_kg_bio,
+            ],
+            "$/kg Output": [
+                self.biomass_cost_per_kg_output,
+                self.internal_cogs_per_kg_output,
+                self.wholesale_cbx_price,
+                self.net_rev_per_kg_output,
+            ],
+        }
+        self.money_data_unit_df = pd.DataFrame(money_data_unit_dict)
+        if hasattr(self, "money_unit_table"):
+            self.money_unit_table.value = self.money_data_unit_df
+
+        money_data_time_dict = {
+            " ": metric_names,
+            "Per Shift": [
+                self.biomass_cost_per_shift,
+                self.internal_cogs_per_shift,
+                self.gross_rev_per_shift,
+                self.net_rev_per_shift,
+            ],
+            "Per Day": [
+                self.biomass_cost_per_day,
+                self.internal_cogs_per_day,
+                self.gross_rev_per_day,
+                self.net_rev_per_day,
+            ],
+            "Per Week": [
+                self.biomass_cost_per_week,
+                self.internal_cogs_per_week,
+                self.gross_rev_per_week,
+                self.net_rev_per_week,
+            ],
+        }
+        self.money_data_time_df = pd.DataFrame(money_data_time_dict)
+        if hasattr(self, "money_time_table"):
+            self.money_time_table.value = self.money_data_time_df
+
+        profit_data_dict = {
+            "Metric": ["Operating Profit", "Resin Spread"],
+            "Value": [
+                f"{self.operating_profit_pct * 100.0:.2f}%",
+                f"{self.resin_spread_pct * 100.0:.2f}%",
+            ],
+        }
+        self.profit_data_df = pd.DataFrame(profit_data_dict)
+        if hasattr(self, "profit_table"):
+            self.profit_table.value = self.profit_data_df
+
+        processing_values_formatted_shift = [
+            f"{self.kg_processed_per_shift:,.0f}",
+            f"{self.saleable_kg_per_shift:,.0f}",
+            f"${self.labour_cost_per_shift:,.2f}",
+            f"${self.variable_cost_per_shift:,.2f}",
+            f"${self.overhead_cost_per_shift:,.2f}",
+        ]
+        processing_values_formatted_day = [
+            f"{self.kg_processed_per_shift * self.shifts_per_day:,.0f}",
+            f"{self.saleable_kg_per_day:,.0f}",
+            f"${self.labour_cost_per_shift * self.shifts_per_day:,.2f}",
+            f"${self.variable_cost_per_shift * self.shifts_per_day:,.2f}",
+            f"${self.overhead_cost_per_shift * self.shifts_per_day:,.2f}",
+        ]
+        processing_values_formatted_week = [
+            f"{self.kg_processed_per_shift * self.shifts_per_week:,.0f}",
+            f"{self.saleable_kg_per_week:,.0f}",
+            f"${self.labour_cost_per_shift * self.shifts_per_week:,.2f}",
+            f"${self.variable_cost_per_shift * self.shifts_per_week:,.2f}",
+            f"${self.overhead_cost_per_shift * self.shifts_per_week:,.2f}",
+        ]
+        processing_data_dict = {
+            "Metric Per": [
+                "Kilograms Extracted",
+                "Kg CBx Produced",
+                "Labour Cost",
+                "Variable Cost",
+                "Overhead",
+            ],
+            "Shift": processing_values_formatted_shift,
+            "Day": processing_values_formatted_day,
+            "Week": processing_values_formatted_week,
+        }
+        self.processing_data_df = pd.DataFrame(processing_data_dict)
+        if hasattr(self, "processing_table"):
+            self.processing_table.value = self.processing_data_df
+
+        if hasattr(self, "profit_weekly"):
+            self.profit_weekly.value = self.net_rev_per_week
+            # Ensure format updates if value changes significantly (e.g. from 0 to large number)
+            self.profit_weekly.format = (
+                f"${self.net_rev_per_week / 1000:.0f} k"
+                if self.net_rev_per_week != 0
+                else "$0 k"
+            )
+
+        if hasattr(self, "profit_pct"):
+            self.profit_pct.value = self.operating_profit_pct
+            self.profit_pct.format = f"{self.operating_profit_pct * 100.0:.2f}%"
+
+    def view(self):
+        input_col_max_width = 400
+        extractionCol = pn.Column(
+            "### Extraction",
+            self.param.kg_processed_per_hour,
+            self.param.finished_product_yield_pct,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        biomassCol = pn.Column(
+            pn.pane.Markdown("### Biomass parameters", margin=0),
+            self.param.bio_cbx_pct,
+            self.param.bio_cost,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        consumableCol = pn.Column(
+            pn.pane.Markdown("### Consumable rates", margin=0),
+            self.param.kwh_rate,
+            self.param.water_cost_per_1000l,
+            self.param.consumables_per_kg_bio_rate,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        wholesaleCol = pn.Column(
+            pn.pane.Markdown("### Wholesale details", margin=0),
+            self.param.wholesale_cbx_price,
+            self.param.wholesale_cbx_pct,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        variableCol = pn.Column(
+            pn.pane.Markdown("### Variable processing costs", margin=0),
+            self.param.kwh_per_kg_bio,
+            self.param.water_liters_consumed_per_kg_bio,
+            self.param.consumables_per_kg_output,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        complianceBatchCol = pn.Column(
+            pn.pane.Markdown("### Compliance", margin=0),
+            self.param.batch_test_cost,
+            pn.pane.Markdown("New Batch Every:", margin=0),
+            self.batch_frequency_radio,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        leechCol = pn.Column(
+            pn.pane.Markdown("### Weekly Rent & Fixed Overheads", margin=0),
+            self.param.weekly_rent,
+            self.param.non_production_electricity_cost_weekly,
+            self.param.property_insurance_weekly,
+            self.param.general_liability_insurance_weekly,
+            self.param.product_recall_insurance_weekly,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        workerCol = pn.Column(
+            pn.pane.Markdown("### Worker Details", margin=0),
+            self.param.workers_per_shift,
+            self.param.worker_base_pay_rate,
+            self.param.managers_per_shift,
+            self.param.manager_base_pay_rate,
+            self.param.direct_cost_pct,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        shiftCol = pn.Column(
+            pn.pane.Markdown("### Shift details", margin=0),
+            self.param.labour_hours_per_shift,
+            self.param.processing_hours_per_shift,
+            self.param.shifts_per_day,
+            self.param.shifts_per_week,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+
+        input_grid = pn.FlexBox(
+            extractionCol,
+            biomassCol,
+            consumableCol,
+            wholesaleCol,
+            variableCol,
+            complianceBatchCol,
+            workerCol,
+            shiftCol,
+            
+            leechCol,
+            align_content="flex-start",
+            align_items="flex-start",
+            # valid options include: '[stretch, flex-start, flex-end, center, baseline, first baseline, last baseline, start, end, self-start, self-end]'
+            flex_wrap="wrap",
+        )  # Added flex_wrap
+
+        money_unit_table_display = pn.Column(
+            pn.pane.Markdown(
+                "### Financial Summary (Per Unit)", styles={"text-align": "center"}
+            ),
+            self.money_unit_table,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width + 50,
+        )
+        money_time_table_display = pn.Column(
+            pn.pane.Markdown(
+                "### Financial Summary (Aggregated)", styles={"text-align": "center"}
+            ),
+            self.money_time_table,
+            sizing_mode="stretch_width",
+            max_width=500,
+        )
+        profit_table_display = pn.Column(
+            pn.pane.Markdown("### Profitability", styles={"text-align": "center"}),
+            self.profit_table,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+        processing_table_display = pn.Column(
+            pn.pane.Markdown("### Processing Summary", styles={"text-align": "center"}),
+            self.processing_table,
+            sizing_mode="stretch_width",
+            max_width=input_col_max_width,
+        )
+
+        table_grid = pn.FlexBox(
+            self.profit_weekly,
+            self.profit_pct,
+            processing_table_display,
+            profit_table_display,
+            money_unit_table_display,
+            money_time_table_display,
+            align_content="normal",
+            flex_wrap="wrap",
+        )
+        knobs = pn.Accordion(("Knobs & Dials",input_grid))
+        knobs.active = [0]
+        main_layout = pn.Column(
+            knobs,
+            pn.layout.Divider(margin=(10, 0)),
+            table_grid,
+            styles={"margin": "0px 10px"},
+        )
         return main_layout