diff --git a/app/index.py b/app/index.py
index a69816e..ec49ba1 100644
--- a/app/index.py
+++ b/app/index.py
@@ -339,10 +339,10 @@ def update_production_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:
     | UID | SupplyAmount      | Branch        |
     |-----|-------------------|---------------|
     | 0   | 1                 | NaN           |
-    | 1   | 0.25              | [0,1]         |
+    | 1   | 0.25              | [0,1]         | NOTA BENE!
     | 2   | 0.2 * (0.25/0.5)  | [0,1,2]       |
     | 3   | 0.1               | [0,3]         |
-    | 4   | 0.18              | [0,1,2,4]     |
+    | 4   | 0.18              | [0,1,2,4]     | NOTA BENE!
     | 5   | 0.05 * (0.1/0.18) | [0,1,2,4,5]   |
     | 6   | 0.01 * (0.1/0.18) | [0,1,2,4,5,6] |
 
@@ -391,10 +391,20 @@ def update_production_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:
     def multiplier(row):
         if not isinstance(row['Branch'], list):
             return row['SupplyAmount']
-        for branch_UID in reversed(row['Branch']):
-            if branch_UID in dict_user_input:
-                return row['SupplyAmount'] * dict_user_input[branch_UID]
-        return row['SupplyAmount']
+        elif (
+            row['UID'] == row['Branch'][-1] and
+            np.isnan(row['SupplyAmount_USER'])
+        ):
+            return row['SupplyAmount']
+        elif (
+            row['UID'] == row['Branch'][-1] and not
+            np.isnan(row['SupplyAmount_USER'])
+        ):
+            return row['SupplyAmount_USER']
+        else:
+            for branch_UID in reversed(row['Branch']):
+                if branch_UID in dict_user_input:
+                    return row['SupplyAmount'] * dict_user_input[branch_UID]
 
     df['SupplyAmount_EDITED'] = df.apply(multiplier, axis=1)
 
diff --git a/dev/test_edit_graph.py b/dev/test_edit_graph.py
index e4d2c42..d9f056f 100644
--- a/dev/test_edit_graph.py
+++ b/dev/test_edit_graph.py
@@ -81,10 +81,7 @@ def create_user_input_column(
     return df_merged
 
 
-def update_production_based_on_user_data(
-        df: pd.DataFrame,
-        column_name: str
-    ) -> pd.DataFrame:
+def update_production_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:
     """
     Updates the production amount of all nodes which are upstream
     of a node with user-supplied production amount.
@@ -143,8 +140,8 @@ def update_production_based_on_user_data(
         Output DataFrame.
     """
 
-    df_filtered = df[~df[f'{column_name}_USER'].isna()]
-    dict_user_input = df_filtered.set_index('UID').to_dict()[f'{column_name}_USER']
+    df_filtered = df[~df['SupplyAmount_USER'].isna()]
+    dict_user_input = df_filtered.set_index('UID').to_dict()['SupplyAmount_USER']
     
     """
     For the example DataFrame from the docstrings above,
@@ -159,20 +156,34 @@ def update_production_based_on_user_data(
     df = df.copy(deep=True)
     def multiplier(row):
         if not isinstance(row['Branch'], list):
-            return row[column_name]
-        for branch_UID in reversed(row['Branch']):
-            if branch_UID in dict_user_input:
-                return row[column_name] * dict_user_input[branch_UID]
-        return row[column_name]
-
-    df[column_name] = df.apply(multiplier, axis=1)
-    df.drop(columns=[f'{column_name}_USER'], inplace=True)
-
-    return df, dict_user_input
-
+            return row['SupplyAmount']
+        elif (
+            row['UID'] == row['Branch'][-1] and
+            np.isnan(row['SupplyAmount_USER'])
+        ):
+            return row['SupplyAmount']
+        elif (
+            row['UID'] == row['Branch'][-1] and not
+            np.isnan(row['SupplyAmount_USER'])
+        ):
+            return row['SupplyAmount_USER']
+        else:
+            for branch_UID in reversed(row['Branch']):
+                if branch_UID in dict_user_input:
+                    return row['SupplyAmount'] * dict_user_input[branch_UID]
+
+    df['SupplyAmount_EDITED'] = df.apply(multiplier, axis=1)
+
+    df.drop(columns=['SupplyAmount_USER'], inplace=True)
+    df['SupplyAmount'] = df['SupplyAmount_EDITED']
+    df.drop(columns=['SupplyAmount_EDITED'], inplace=True)
+
+    return df
 
 df_user_col = create_user_input_column(
     df_original=df_original,
     df_user_input=df_user_input,
     column_name='SupplyAmount'
 )
+
+df_updated = update_production_based_on_user_data(df_user_col)
\ No newline at end of file
diff --git a/pyodide/index.html b/pyodide/index.html
index 8c5d0d3..74495d4 100644
--- a/pyodide/index.html
+++ b/pyodide/index.html
@@ -107,14 +107,14 @@
           <span class="mdc-top-app-bar__title app-header">
 <a href="/"><img src="https://raw.githubusercontent.com/brightway-lca/brightway-webapp/main/app/_media/logo_brightway_white.svg" class="app-logo"></a><a class="title" href="" >&nbsp;Brightway WebApp (Carbon Accounting)</a>          </span>
           <div id="header-items">
-            <div id="be0f16f7-aa6e-419c-8e47-6fc907f528c9" data-root-id="p1026" style="display: contents;"></div>
-            <div id="aa76ea6b-99dc-42a9-a4b7-b1b2cb786566" data-root-id="p1027" style="display: contents;"></div>
-            <div id="b99d3e10-9c7e-47f5-98d2-d654dece7fe1" data-root-id="p1028" style="display: contents;"></div>
+            <div id="d44f9f37-7296-4416-b748-a17fc0b8448d" data-root-id="p1026" style="display: contents;"></div>
+            <div id="d383a476-cdd2-42a7-ae16-30a7b1e22ff6" data-root-id="p1027" style="display: contents;"></div>
+            <div id="ef72f8f3-0be7-4ffc-81e1-6d0571f45f8e" data-root-id="p1028" style="display: contents;"></div>
           </div>
         </div>
       </section>
       <div class="pn-busy-container">
-	<div id="ddc0d8f8-c392-4e3e-b207-7c7a96130865" data-root-id="p1024" style="display: contents;"></div>
+	<div id="b0c25278-e81f-4d6f-9c0d-5f21bde77a8d" data-root-id="p1024" style="display: contents;"></div>
       </div>
     </div>
   </header>
@@ -122,7 +122,7 @@
 
   <div class="mdc-drawer-app-content mdc-top-app-bar--fixed-adjust">
     <main class="main-content" id="main">
-      <div id="a5cbf1bb-a5aa-4d4d-8042-facc48f884c9" data-root-id="p1029" style="display: contents;"></div>
+      <div id="e4cc0bd0-4385-4d60-afec-76a685225138" data-root-id="p1029" style="display: contents;"></div>
     </main>
 
     <div id="pn-Modal" class="mdc-dialog">
@@ -147,11 +147,11 @@
     }
   </script>
 
-  <div id="d823506e-fc73-411e-9208-dbef8d723481" data-root-id="p1010" style="display: contents;"></div>
-<div id="d15327d7-be13-4731-a37c-6ff4ca8909cc" data-root-id="p1013" style="display: contents;"></div>
-<div id="ce748d6e-7137-4c03-8494-43a014e98d04" data-root-id="p1002" style="display: contents;"></div>
-<div id="b06d0085-baaf-4ac4-a973-0fa1d6b18607" data-root-id="p1021" style="display: contents;"></div>
-<div id="ff14e71a-5e69-4372-9b78-7f840acdd888" data-root-id="p1022" style="display: contents;"></div>
+  <div id="f8cccc78-3423-4a72-b322-ca2545d19f74" data-root-id="p1010" style="display: contents;"></div>
+<div id="ae7f8521-cb29-447b-805a-e6fbf58fff11" data-root-id="p1013" style="display: contents;"></div>
+<div id="f93e21bc-936b-4d1f-ab90-9b2654ccb24d" data-root-id="p1002" style="display: contents;"></div>
+<div id="bd063395-68e5-436d-921c-863984799331" data-root-id="p1021" style="display: contents;"></div>
+<div id="a62eba75-dfec-4099-8cf1-870a368aba72" data-root-id="p1022" style="display: contents;"></div>
 
 </div>
   
@@ -255,7 +255,7 @@
       };
     </script>
     <script type="application/json" id="p1082">
-      {"a8a2d2db-d36f-4abd-a1ad-cbbfe169f641":{"version":"3.6.0","title":"Brightway WebApp (Carbon Accounting)","roots":[{"type":"object","name":"panel.models.location.Location","id":"p1002","attributes":{"name":"location","reload":false}},{"type":"object","name":"panel.models.markup.HTML","id":"p1010","attributes":{"name":"js_area","stylesheets":["\n:host(.pn-loading):before, .pn-loading:before {\n  background-color: #c3c3c3;\n  mask-size: auto calc(min(50%, 400px));\n  -webkit-mask-size: auto calc(min(50%, 400px));\n}",{"type":"object","name":"ImportedStyleSheet","id":"p1008","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/css/loading.css"}},{"type":"object","name":"ImportedStyleSheet","id":"p1004","attributes":{"url":"https://cdn.jsdelivr.net/npm/material-components-web@7.0.0/dist/material-components-web.min.css"}},{"type":"object","name":"ImportedStyleSheet","id":"p1005","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/theme/default.css"}},{"type":"object","name":"ImportedStyleSheet","id":"p1006","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/theme/material_variables.css"}},{"type":"object","name":"ImportedStyleSheet","id":"p1007","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/theme/material.css"}}],"width":0,"height":0,"margin":0,"sizing_mode":"fixed","align":"start","disable_math":true}},{"type":"object","name":"panel.models.reactive_html.ReactiveHTML","id":"p1013","attributes":{"name":"actions","subscribed_events":{"type":"set","entries":["dom_event"]},"stylesheets":["\n:host(.pn-loading):before, .pn-loading:before {\n  background-color: #c3c3c3;\n  mask-size: auto calc(min(50%, 400px));\n  -webkit-mask-size: auto calc(min(50%, 400px));\n}",{"id":"p1008"},{"id":"p1007"}],"margin":0,"align":"start","data":{"type":"object","name":"MaterialTemplateActions1","id":"p1012","attributes":{"name":"MaterialTemplateActions00168"}},"scripts":{"type":"map","entries":[["open_modal",["modal.open();\nsetTimeout(function() {{\n  window.dispatchEvent(new Event(&amp;#x27;resize&amp;#x27;));\n}}, 200);"]],["close_modal",["modal.close()"]]]}}},{"type":"object","name":"panel.models.reactive_html.ReactiveHTML","id":"p1021","attributes":{"name":"notifications","subscribed_events":{"type":"set","entries":["dom_event"]},"stylesheets":[{"type":"object","name":"ImportedStyleSheet","id":"p1018","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/notificationarea/notyf@3/notyf.min.css"}},{"type":"object","name":"ImportedStyleSheet","id":"p1019","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/font-awesome/css/all.min.css"}},{"type":"object","name":"ImportedStyleSheet","id":"p1020","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/css/notifications.css"}},{"id":"p1018"},{"id":"p1019"},{"id":"p1020"},"\n:host(.pn-loading):before, .pn-loading:before {\n  background-color: #c3c3c3;\n  mask-size: auto calc(min(50%, 400px));\n  -webkit-mask-size: auto calc(min(50%, 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notification._destroyed = true;\n  }\n}\ntoast.on(&amp;#x27;dismiss&amp;#x27;, destroy)\nif (notification.duration) {\n  setTimeout(destroy, notification.duration)\n}\nif (notification.properties === undefined)\n  return\nview.connect(notification.properties._destroyed.change, function () {\n  state.toaster.dismiss(toast)\n})"]],["_clear",["state.toaster.dismissAll()"]],["position",["script(&amp;#x27;_clear&amp;#x27;);\nscript(&amp;#x27;render&amp;#x27;);\nfor (notification of data.notifications) {\n  state.current = notification;\n  script(&amp;#x27;notifications&amp;#x27;);\n}\nstate.current = undefined"]]]}}},{"type":"object","name":"panel.models.browser.BrowserInfo","id":"p1022","attributes":{"name":"browser_info"}},{"type":"object","name":"panel.models.markup.HTML","id":"p1024","attributes":{"name":"busy_indicator","css_classes":["loader","light"],"stylesheets":[":host { --loading-spinner-size: 20px; }","\n:host(.pn-loading):before, .pn-loading:before {\n  background-color: 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}"}}]]]},"subscribed_events":{"type":"set","entries":["button_click"]},"css_classes":["solid"],"stylesheets":["\n:host(.pn-loading):before, .pn-loading:before {\n  background-color: #c3c3c3;\n  mask-size: auto calc(min(50%, 400px));\n  -webkit-mask-size: auto calc(min(50%, 400px));\n}",{"id":"p1008"},{"type":"object","name":"ImportedStyleSheet","id":"p1025","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/css/button.css"}},{"id":"p1004"},{"id":"p1005"},{"id":"p1006"},{"id":"p1007"}],"margin":[5,10],"align":"start","label":"Learn more about this prototype..."}},{"type":"object","name":"Spacer","id":"p1027","attributes":{"name":"header-13064916384","tags":["header"],"stylesheets":["\n:host(.pn-loading):before, .pn-loading:before {\n  background-color: #c3c3c3;\n  mask-size: auto calc(min(50%, 400px));\n  -webkit-mask-size: auto calc(min(50%, 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href=&amp;quot;https://doi.org/10.1038/s41597-022-01293-7&amp;quot;&amp;gt;USEEIO release article&amp;lt;/a&amp;gt;.&amp;lt;/p&amp;gt;\n"}},{"type":"object","name":"panel.models.widgets.CustomSelect","id":"p1038","attributes":{"stylesheets":["\n:host(.pn-loading):before, .pn-loading:before {\n  background-color: #c3c3c3;\n  mask-size: auto calc(min(50%, 400px));\n  -webkit-mask-size: auto calc(min(50%, 400px));\n}",{"id":"p1008"},{"type":"object","name":"ImportedStyleSheet","id":"p1037","attributes":{"url":"https://cdn.holoviz.org/panel/1.5.2/dist/css/select.css"}},{"id":"p1004"},{"id":"p1005"},{"id":"p1006"},{"id":"p1007"}],"width":300,"margin":[5,10],"sizing_mode":"stretch_width","align":"start","title":"Impact Assessment Method"}},{"type":"object","name":"Spinner","id":"p1039","attributes":{"stylesheets":["\n:host(.pn-loading):before, .pn-loading:before {\n  background-color: #c3c3c3;\n  mask-size: auto calc(min(50%, 400px));\n  -webkit-mask-size: auto calc(min(50%, 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@@ -264,7 +264,7 @@
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               function embed_document(root) {
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diff --git a/pyodide/index.js b/pyodide/index.js
index bbb1cf7..854cba8 100644
--- a/pyodide/index.js
+++ b/pyodide/index.js
@@ -40,7 +40,7 @@ async function startApplication() {
   console.log("Packages loaded!");
   self.postMessage({type: 'status', msg: 'Executing code'})
   const code = `
-  \nimport asyncio\n\nfrom panel.io.pyodide import init_doc, write_doc\n\ninit_doc()\n\nimport panel as pn\npn.extension(notifications=True)\npn.extension(design='material')\npn.extension('plotly')\npn.extension('tabulator')\n\n# plotting\nimport plotly\n\n# data science\nimport pandas as pd\nimport numpy as np\n\n# system\nimport os\n\n# brightway\nimport bw_graph_tools as bgt\nimport bw2io as bi\nimport bw2data as bd\nimport bw2calc as bc\n\n# type hints\nfrom bw2data.backends.proxies import Activity\nfrom bw_graph_tools.graph_traversal import Node\nfrom bw_graph_tools.graph_traversal import Edge\n\n\ndef brightway_wasm_database_storage_workaround() -> None:\n    """\n    Sets the Brightway project directory to \`/tmp/.\n    \n    The JupyterLite file system currently does not support storage of SQL database files\n    in directories other than \`/tmp/\`. This function sets the Brightway environment variable\n    \`BRIGHTWAY_DIR\` to \`/tmp/\` to work around this limitation.\n    \n    Notes\n    -----\n    - https://docs.brightway.dev/en/latest/content/faq/data_management.html#how-do-i-change-my-data-directory\n    - https://github.com/brightway-lca/brightway-live/issues/10\n    """\n    os.environ["BRIGHTWAY_DIR"] = "/tmp/"\n\n\ndef check_for_useeio_brightway_project(event):\n    """\n    Checks if the USEEIO-1.1 Brightway project is installed.\n    If not, installs it. Shows Panel notifications for the user.\n\n    Returns\n    -------\n    SQLiteBackend\n        bw2data.backends.base.SQLiteBackend of the USEEIO-1.1 database\n    """\n    if 'USEEIO-1.1' not in bd.projects:\n        notification_load = pn.state.notifications.info('Loading USEEIO database...')\n        bi.install_project(project_key="USEEIO-1.1", overwrite_existing=True)\n        notification_load.destroy()\n        pn.state.notifications.success('USEEIO database loaded!', duration=7000)\n    else:\n        pn.state.notifications.success('USEEIO database already loaded!', duration=7000)\n        pass\n    bd.projects.set_current(name='USEEIO-1.1')\n\n\ndef nodes_dict_to_dataframe(\n        nodes: dict,\n        uid_electricity: int = 53 # hardcoded for USEEIO\n    ) -> pd.DataFrame:\n    """\n    Returns a dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal.\n\n    Parameters\n    ----------\n    nodes : dict\n        A dictionary of nodes in the graph traversal.\n        Can be created by selecting the 'nodes' key from the dictionary\n        returned by the function \`bw_graph_tools.NewNodeEachVisitGraphTraversal.calculate()\`.\n\n    Returns\n    -------\n    pd.DataFrame\n        A dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal.\n    """\n    list_of_row_dicts = []\n    for current_node in nodes.values():\n\n        scope: int = 3\n        if current_node.unique_id == -1:\n            continue\n        elif current_node.unique_id == 0:\n            scope = 1\n        elif current_node.activity_datapackage_id == uid_electricity:\n            scope = 2\n        else:\n            pass\n        list_of_row_dicts.append(\n            {\n                'UID': current_node.unique_id,\n                'Scope': scope,\n                'Name': bd.get_node(id=current_node.activity_datapackage_id)['name'],\n                'SupplyAmount': current_node.supply_amount,\n                'BurdenIntensity': current_node.direct_emissions_score/current_node.supply_amount,\n                # 'Burden(Cumulative)': current_node.cumulative_score,\n                'Burden(Direct)': current_node.direct_emissions_score + current_node.direct_emissions_score_outside_specific_flows,\n                'Depth': current_node.depth,\n                'activity_datapackage_id': current_node.activity_datapackage_id,\n            }\n        )\n    return pd.DataFrame(list_of_row_dicts)\n\n\ndef edges_dict_to_dataframe(edges: list) -> pd.DataFrame:\n    """\n    To be added...\n    """\n    if len(edges) < 2:\n        return pd.DataFrame()\n    else:\n        list_of_row_dicts = []\n        for current_edge in edges:\n            list_of_row_dicts.append(\n                {\n                    'consumer_unique_id': current_edge.consumer_unique_id,\n                    'producer_unique_id': current_edge.producer_unique_id\n                }\n            )\n        return pd.DataFrame(list_of_row_dicts).drop(0)\n\n\ndef trace_branch(df: pd.DataFrame, start_node: int) -> list:\n    """\n    Given a dataframe of graph edges and a "starting node" (producer_unique_id), returns the branch of nodes that lead to the starting node.\n\n    For example:\n\n    | consumer_unique_id | producer_unique_id |\n    |--------------------|--------------------|\n    | 0                  | 1                  | # 1 is terminal producer node\n    | 0                  | 2                  |\n    | 0                  | 3                  |\n    | 2                  | 4                  | # 4 is terminal producer node\n    | 3                  | 5                  |\n    | 5                  | 6                  | # 6 is terminal producer node\n\n    For start_node = 6, the function returns [0, 3, 5, 6]\n\n    Parameters\n    ----------\n    df : pd.DataFrame\n        Dataframe of graph edges. Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.\n    start_node : int\n        The integer indicating the producer_unique_id starting node to trace back from.\n\n    Returns\n    -------\n    list\n        A list of integers indicating the branch of nodes that lead to the starting node.\n    """\n\n    branch: list = [start_node]\n\n    while True:\n        previous_node: int = df[df['producer_unique_id'] == start_node]['consumer_unique_id']\n        if previous_node.empty:\n            break\n        start_node: int = previous_node.values[0]\n        branch.insert(0, start_node)\n\n    return branch\n\n\ndef add_branch_information_to_edges_dataframe(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Adds 'branch' information to terminal nodes in a dataframe of graph edges.\n\n    For example:\n\n    | consumer_unique_id | producer_unique_id |\n    |--------------------|--------------------|\n    | 0                  | 1                  | # 1 is terminal producer node\n    | 0                  | 2                  |\n    | 0                  | 3                  |\n    | 2                  | 4                  | # 4 is terminal producer node\n    | 3                  | 5                  |\n    | 5                  | 6                  | # 6 is terminal producer node\n\n    | consumer_unique_id | producer_unique_id | branch       |\n    |--------------------|--------------------|--------------|\n    | 0                  | 1                  | [0, 1]       |\n    | 0                  | 2                  | [0, 2]       |\n    | 0                  | 3                  | [0, 3]       |\n    | 2                  | 4                  | [0, 2, 4]    |\n    | 3                  | 5                  | [0, 3, 5]    |\n    | 5                  | 6                  | [0, 3, 5, 6] |\n\n    Parameters\n    ----------\n    df_edges : pd.DataFrame\n        A dataframe of graph edges.\n        Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.\n\n    Returns\n    -------\n    pd.DataFrame\n        A dataframe of graph nodes with a column 'branch' that contains the branch of nodes that lead to the terminal producer node.\n    """\n    # initialize empty list to store branches\n    branches: list = []\n\n    for _, row in df.iterrows():\n        branch: list = trace_branch(df, int(row['producer_unique_id']))\n        branches.append({\n            'producer_unique_id': int(row['producer_unique_id']),\n            'Branch': branch\n        })\n\n    return pd.DataFrame(branches)\n\n\ndef create_user_input_columns(\n        df_original: pd.DataFrame,\n        df_user_input: pd.DataFrame,\n    ) -> pd.DataFrame:\n    """\n    Creates a new column in the 'original' DataFrame where only the\n    user-supplied values are kept. The other values are replaced by NaN.\n\n    For instance, given an "original" DataFrame of the kind:\n\n    | UID | SupplyAmount | BurdenIntensity |\n    |-----|--------------|-----------------|\n    | 0   | 1            | 0.1             |\n    | 1   | 0.5          | 0.5             |\n    | 2   | 0.2          | 0.3             |\n\n    and a "user input" DataFrame of the kind:\n\n    | UID | SupplyAmount | BurdenIntensity |\n    |-----|--------------|-----------------|\n    | 0   | 1            | 0.1             |\n    | 1   | 0            | 0.5             |\n    | 2   | 0.2          | 2.1             |\n\n    the function returns a DataFrame of the kind:\n\n    | UID | SupplyAmount | SupplyAmount_USER | BurdenIntensity | BurdenIntensity_USER |\n    |-----|--------------|-------------------|-----------------|----------------------|\n    | 0   | 1            | NaN               | 0.1             | NaN                  |\n    | 1   | 0.5          | 0                 | 0.5             | NaN                  |\n    | 2   | 0.2          | NaN               | 0.3             | 2.1                  |\n\n    Parameters\n    ----------\n    df_original : pd.DataFrame\n        Original DataFrame.\n\n    df_user_input : pd.DataFrame\n        User input DataFrame.\n    """\n    \n    df_merged = pd.merge(\n        df_original,\n        df_user_input[['UID', 'SupplyAmount', 'BurdenIntensity']],\n        on='UID',\n        how='left',\n        suffixes=('', '_USER')\n    )\n\n    for column_name in ['SupplyAmount', 'BurdenIntensity']:\n        df_merged[f'{column_name}_USER'] = np.where(\n            df_merged[f'{column_name}_USER'] != df_merged[f'{column_name}'],\n            df_merged[f'{column_name}_USER'],\n            np.nan\n        )\n\n    return df_merged\n\n\ndef update_burden_intensity_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Updates the burden intensity when user data is provided.\n\n    For instance, given a DataFrame of the kind:\n\n    | UID | BurdenIntensity | BurdenIntensity_USER |\n    |-----|-----------------|----------------------|\n    | 0   | 0.1             | NaN                  |\n    | 1   | 0.5             | 0.25                 |\n    | 2   | 0.3             | NaN                  |\n\n    the function returns a DataFrame of the kind:\n\n    | UID | BurdenIntensity |\n    |-----|-----------------|\n    | 0   | 0.1             |\n    | 1   | 0.25            |\n    | 2   | 0.3             |\n\n    Parameters\n    ----------\n    df : pd.DataFrame\n        Input dataframe.\n\n    Returns\n    -------\n    pd.DataFrame\n        Output dataframe.\n    """\n\n\n    df['BurdenIntensity'] = df['BurdenIntensity_USER'].combine_first(df['BurdenIntensity'])\n    df = df.drop(columns=['BurdenIntensity_USER'])\n\n    return df\n\n\ndef update_production_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Updates the production amount of all nodes which are upstream\n    of a node with user-supplied production amount.\n    If an upstream node has half the use-supplied production amount,\n    then the production amount of all downstream node is also halved.\n\n    For instance, given a DataFrame of the kind:\n\n    | UID | SupplyAmount | SupplyAmount_USER | Branch        |\n    |-----|--------------|-------------------|---------------|\n    | 0   | 1            | NaN               | NaN           |\n    | 1   | 0.5          | 0.25              | [0,1]         |\n    | 2   | 0.2          | NaN               | [0,1,2]       |\n    | 3   | 0.1          | NaN               | [0,3]         |\n    | 4   | 0.1          | 0.18              | [0,1,2,4]     |\n    | 5   | 0.05         | NaN               | [0,1,2,4,5]   |\n    | 6   | 0.01         | NaN               | [0,1,2,4,5,6] |\n\n    the function returns a DataFrame of the kind:\n\n    | UID | SupplyAmount      | Branch        |\n    |-----|-------------------|---------------|\n    | 0   | 1                 | NaN           |\n    | 1   | 0.25              | [0,1]         |\n    | 2   | 0.2 * (0.25/0.5)  | [0,1,2]       |\n    | 3   | 0.1               | [0,3]         |\n    | 4   | 0.18              | [0,1,2,4]     |\n    | 5   | 0.05 * (0.1/0.18) | [0,1,2,4,5]   |\n    | 6   | 0.01 * (0.1/0.18) | [0,1,2,4,5,6] |\n\n    Notes\n    -----\n\n    As we can see, the function updates production only\n    for those nodes upstream of a node with 'production_user':\n\n    - Node 2 is upstream of node 1, which has a 'production_user' value.\n    - Node 3 is NOT upstream of node 1. It is upstream of node 0, but node 0 does not have a 'production_user' value.\n\n    As we can see, the function always takes the "most recent"\n    'production_user' value upstream of a node:\n\n    - Node 5 is upstream of node 4, which has a 'production_user' value.\n    - Node 4 is upstream of node 1, which also has a 'production_user' value.\n\n    In this case, the function takes the 'production_user' value of node 4, not of node 1.\n\n    Parameters\n    ----------\n    df : pd.DataFrame\n        Input DataFrame. Must have the columns 'production', 'production_user' and 'branch'.\n\n    Returns\n    -------\n    pd.DataFrame\n        Output DataFrame.\n    """\n\n    df_filtered = df[~df['SupplyAmount_USER'].isna()]\n    dict_user_input = df_filtered.set_index('UID').to_dict()['SupplyAmount_USER']\n    \n    """\n    For the example DataFrame from the docstrings above,\n    the dict_user_input would be:\n\n    dict_user_input = {\n        1: 0.25,\n        4: 0.18\n    }\n    """\n\n    df = df.copy(deep=True)\n    def multiplier(row):\n        if not isinstance(row['Branch'], list):\n            return row['SupplyAmount']\n        for branch_UID in reversed(row['Branch']):\n            if branch_UID in dict_user_input:\n                return row['SupplyAmount'] * dict_user_input[branch_UID]\n        return row['SupplyAmount']\n\n    df['SupplyAmount_EDITED'] = df.apply(multiplier, axis=1)\n\n    df.drop(columns=['SupplyAmount_USER'], inplace=True)\n    df['SupplyAmount'] = df['SupplyAmount_EDITED']\n    df.drop(columns=['SupplyAmount_EDITED'], inplace=True)\n\n    return df\n\n\ndef update_burden_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Updates the environmental burden of nodes\n    by multiplying the burden intensity and the supply amount.\n\n    Parameters\n    ----------\n    df : pd.DataFrame\n        Input dataframe.\n\n    Returns\n    -------\n    pd.DataFrame\n        Output dataframe.\n    """\n\n    df['Burden(Direct)'] = df['SupplyAmount'] * df['BurdenIntensity']\n    return df\n\n\ndef determine_edited_rows(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Determines which rows have been edited by the user.\n\n    For instance, given a DataFrame of the kind:\n\n    | UID | SupplyAmount_USER | BurdenIntensity_USER |\n    |-----|-------------------|----------------------|\n    | 0   | NaN               | NaN                  |\n    | 1   | 0.25              | NaN                  |\n    | 2   | NaN               | 2.1                  |\n    | 3   | NaN               | NaN                  |\n\n    the function returns a DataFrame of the kind:\n\n    | UID | SupplyAmount_USER | BurdenIntensity_USER | Edited? |\n    |-----|-------------------|----------------------|---------|\n    | 0   | NaN               | NaN                  | False   |\n    | 1   | 0.25              | NaN                  | True    |\n    | 2   | NaN               | 2.1                  | True    |\n    | 3   | NaN               | NaN                  | False   |\n    """\n    df['Edited?'] = df[['SupplyAmount_USER', 'BurdenIntensity_USER']].notnull().any(axis=1)\n    return df\n\n\ndef create_plotly_figure_piechart(data_dict: dict) -> plotly.graph_objects.Figure:\n    marker_colors = []\n    for label in data_dict.keys():\n        if label == 'Scope 1':\n            marker_colors.append('#33cc33')  # Color for Scope 1\n        elif label == 'Scope 2':\n            marker_colors.append('#ffcc00')  # Color for Scope 2\n        elif label == 'Scope 3':\n            marker_colors.append('#3366ff')  # Color for Scope 3\n        else:\n            marker_colors.append('#000000')  # Default color for other labels\n\n    plotly_figure = plotly.graph_objects.Figure(\n        data=[\n            plotly.graph_objects.Pie(\n                labels=list(data_dict.keys()),\n                values=list(data_dict.values()),\n                marker=dict(colors=marker_colors)  # Set the colors for the pie chart\n            )\n        ]\n    )\n    plotly_figure.update_traces(\n        marker=dict(\n            line=dict(color='#000000', width=2)\n        )\n    )\n    plotly_figure.update_layout(\n        autosize=True,\n        height=300,\n        legend=dict(\n            orientation="v",\n            yanchor="auto",\n            y=1,\n            xanchor="right",\n            x=-0.3\n        ),\n        margin=dict(\n            l=50,\n            r=50,\n            b=0,\n            t=0,\n            pad=0\n        ),\n    )\n    return plotly_figure\n\n\ndef generate_table_filename() -> str:\n    """\n    Generates a string to be used a filename for downloading the tabulator.value DataFrame.\n\n    Returns\n    -------\n    str\n        Filename string.\n    """\n    str_filename: str = (\n        "activity='"\n        + panel_lca_class_instance.chosen_activity['name'].replace(' ', '_').replace(';', '') .replace(',', '')\n        + "'_method='"\n        + '-'.join(panel_lca_class_instance.chosen_method.name).replace(' ', '-')\n        + "'_cutoff=" \n        + str(panel_lca_class_instance.graph_traversal_cutoff).replace('.', ',') \n        + ".csv"\n    )\n    return str_filename\n\n\ndef determine_scope_emissions(df: pd.DataFrame):\n        """\n        Determines the scope 1/2/3 emissions from the graph traversal nodes dataframe.\n        """\n        dict_scope = {\n            'Scope 1': 0,\n            'Scope 2': 0,\n            'Scope 3': 0\n        }\n        \n        dict_scope['Scope 1'] = df.loc[(df['Scope'] == 1)]['Burden(Direct)'].values.sum()\n        dict_scope['Scope 2'] = df.loc[(df['Scope'] == 2)]['Burden(Direct)'].values.sum()\n        dict_scope['Scope 3'] = df['Burden(Direct)'].sum() - dict_scope['Scope 1'] - dict_scope['Scope 2']\n\n        return dict_scope\n\nclass panel_lca_class:\n    """\n    This class is used to store all the necessary information for the LCA calculation.\n    It provides methods to populate the database and perform Brightway LCA calculations.\n    All methods can be bound to a button click event.\n\n    Notes\n    -----\n    https://discourse.holoviz.org/t/update-global-variable-through-function-bound-with-on-click/\n    """\n    brightway_wasm_database_storage_workaround()\n    def __init__(self):\n        self.db_name = 'USEEIO-1.1'\n        self.db = None\n        self.list_db_products = []\n        self.dict_db_methods = {}\n        self.list_db_methods = []\n        self.chosen_activity = ''\n        self.chosen_method = ''\n        self.chosen_method_unit = ''\n        self.chosen_amount = 0\n        self.lca = None\n        self.scope_dict = {'Scope 1':0, 'Scope 2':0, 'Scope 3':0}\n        self.graph_traversal_cutoff = 1\n        self.graph_traversal = {}\n        self.df_graph_traversal_nodes = None\n        self.df_graph_traversal_edges = None\n        self.df_tabulator_from_traversal = None\n        self.df_tabulator_from_user = None\n        self.df_tabulator = None # nota bene: gets updated automatically when cells in the tabulator are edited # https://panel.holoviz.org/reference/widgets/Tabulator.html#editors-editing\n        self.bool_user_provided_data = False\n\n\n    def set_db(self, event):\n        """\n        Checks if the USEEIO-1.1 Brightway project is installed.\n        If not, installs it and sets is as current project.\n        Else just sets the current project to USEEIO-1.1.\n        """\n        check_for_useeio_brightway_project(event)\n        self.db = bd.Database(self.db_name)\n\n\n    def set_list_db_products(self, event):\n        """\n        Sets \`list_db_products\` to a list of product names from the database for use in the autocomplete widget.\n        """\n        self.list_db_products = [node['name'] for node in self.db if 'product' in node['type']]\n    \n\n    def set_methods_objects(self, event):\n        """\n        dict_methods = {\n            'HRSP': ('Impact Potential', 'HRSP'),\n            'OZON': ('Impact Potential', 'OZON'),\n            ...\n        }\n        """\n        dict_methods = {i[-1]:[i] for i in bd.methods}\n\n        """\n        dict_method_names = {\n            'HRSP': 'Human Health: Respiratory effects',\n            'OZON': 'Ozone Depletion',\n            ...\n        }\n        """\n        # hardcoded for better Pyodide performance\n        dict_methods_names = {\n            "HRSP": "Human Health - Respiratory Effects",\n            "OZON": "Ozone Depletion",\n            "HNC": "Human Health Noncancer",\n            "WATR": "Water",\n            "METL": "Metals",\n            "EUTR": "Eutrophication",\n            "HTOX": "Human Health Cancer and Noncancer",\n            "LAND": "Land",\n            "NREN": "Nonrenewable Energy",\n            "ETOX": "Freshwater Aquatic Ecotoxicity",\n            "PEST": "Pesticides",\n            "REN": "Renewable Energy",\n            "MINE": "Minerals and Metals",\n            "GCC": "Global Climate Change",\n            "ACID": "Acid Rain",\n            "HAPS": "Hazardous Air Pollutants",\n            "HC": "Human Health Cancer",\n            "SMOG": "Smog Formation",\n            "ENRG": "Energy"\n        }\n        # path_impact_categories_names: str = '../app/_data/USEEIO_impact_categories_names.csv'\n        # dict_methods_names = {}\n        # with open(path_impact_categories_names, mode='r', newline='', encoding='utf-8-sig') as file:\n        #    reader = csv.reader(file)\n        #    dict_methods_names = {rows[0]: rows[1] for rows in reader}\n\n        """\n        dict_methods_units = {\n            'HRSP': '[kg PM2.5 eq]',\n            'OZON': '[kg O3 eq]',\n            ...\n        }\n        """\n        # hardcoded for better Pyodide performance\n        dict_methods_units = {\n            "HRSP": "[kg PM2.5 eq]",\n            "OZON": "[kg O3 eq]",\n            "HNC": "[CTUh]",\n            "WATR": "[m3]",\n            "METL": "[kg]",\n            "EUTR": "[kg N eq]",\n            "HTOX": "[CTUh]",\n            "LAND": "[m2*yr]",\n            "NREN": "[MJ]",\n            "ETOX": "[CTUe]",\n            "PEST": "[kg]",\n            "REN": "[MJ]",\n            "MINE": "[kg]",\n            "GCC": "[kg CO2 eq]",\n            "ACID": "[kg SO2 eq]",\n            "HAPS": "[kg]",\n            "HC": "[CTUh]",\n            "SMOG": "[kg O3 eq]",\n            "ENRG": "[MJ]"\n        }\n        # path_impact_categories_units: str = '../app/_data/USEEIO_impact_categories_units.csv'\n        # dict_methods_units = {}\n        # with open(path_impact_categories_units, mode='r', newline='', encoding='utf-8-sig') as file:\n        #    reader = csv.reader(file)\n        #    dict_methods_units = {rows[0]: str('[')+rows[1]+str(']') for rows in reader}\n\n        """\n        dict_methods_enriched = {\n            'HRSP': [('Impact Potential', 'HRSP'), 'Human Health - Respiratory effects', '[kg PM2.5 eq]'],\n            'OZON': [('Impact Potential', 'OZON'), 'Ozone Depletion', '[kg O3 eq]'],\n            ...\n        }\n        """\n        dict_methods_enriched = {\n            key: [dict_methods[key][0], dict_methods_names[key], dict_methods_units[key]]\n            for key in dict_methods\n        }\n\n        """\n        list_methods_for_autocomplete = [\n            ('HRSP', 'Human Health: Respiratory effects', '[kg PM2.5 eq]'),\n            ('OZON', 'Ozone Depletion', '[kg O3 eq]'),\n            ...\n        ]\n        """\n        list_methods_for_autocomplete = [(key, value[1], value[2]) for key, value in dict_methods_enriched.items()]\n\n        self.dict_db_methods = dict_methods_enriched\n        self.list_db_methods = list_methods_for_autocomplete\n\n\n    def set_chosen_activity(self, event):\n        """\n        Sets \`chosen_activity\` to the \`bw2data.backends.proxies.Activity\` object of the chosen product from the autocomplete widget.\n        """\n        self.chosen_activity: Activity = bd.utils.get_node(\n            database = self.db_name,\n            name = widget_autocomplete_product.value,\n            type = 'product',\n            location = 'United States'\n        )\n\n\n    def set_chosen_method_and_unit(self, event):\n        """\n        Sets \`chosen_method\` to the (tuple) corresponding to the chosen method string from the select widget.\n\n        Example:\n        --------\n        widget_select_method.value = ('HRSP', 'Human Health: Respiratory effects', '[kg PM2.5 eq]')\n        widget_select_method.value[0] = 'HRSP'\n        dict_db_methods = {'HRSP': [('Impact Potential', 'HRSP'), 'Human Health - Respiratory effects', '[kg PM2.5 eq]']}\n        dict_db_methods['HRSP'][0] = ('Impact Potential', 'HRSP') # which is the tuple that bd.Method needs\n        """\n        self.chosen_method = bd.Method(self.dict_db_methods[widget_select_method.value[0]][0])\n        self.chosen_method_unit = widget_select_method.value[2]\n\n\n    def set_chosen_amount(self, event):\n        """\n        Sets \`chosen_amount\` to the float value from the float input widget.\n        """\n        self.chosen_amount = widget_float_input_amount.value\n\n\n    def perform_lca(self, event):\n        """\n        Performs the LCA calculation using the chosen product, method, and amount.\n        Sets the \`lca\` attribute to an instance of the \`bw2calc.LCA\` object.\n        """\n        self.lca = bc.LCA( \n            demand={self.chosen_activity: self.chosen_amount}, \n            method = self.chosen_method.name\n        )\n        self.lca.lci()\n        self.lca.lcia()\n\n\n    def set_graph_traversal_cutoff(self, event):\n        """\n        Sets the \`graph_traversal_cutoff\` attribute to the float value from the float slider widget.\n        Note that the value is divided by 100 to convert from percentage to decimal.\n        """\n        self.graph_traversal_cutoff = widget_float_slider_cutoff.value / 100\n\n\n    def perform_graph_traversal(self, event):\n        widget_cutoff_indicator_statictext.value = self.graph_traversal_cutoff * 100\n        self.graph_traversal: dict = bgt.NewNodeEachVisitGraphTraversal.calculate(self.lca, cutoff=self.graph_traversal_cutoff)\n        self.df_graph_traversal_nodes: pd.DataFrame = nodes_dict_to_dataframe(self.graph_traversal['nodes'])\n        self.df_graph_traversal_edges: pd.DataFrame = edges_dict_to_dataframe(self.graph_traversal['edges'])\n        if self.df_graph_traversal_edges.empty:\n            return\n        else:\n            self.df_graph_traversal_edges = add_branch_information_to_edges_dataframe(self.df_graph_traversal_edges)\n            self.df_tabulator_from_traversal = pd.merge(\n                self.df_graph_traversal_nodes,\n                self.df_graph_traversal_edges,\n                left_on='UID',\n                right_on='producer_unique_id',\n                how='left')\n\n\nbrightway_wasm_database_storage_workaround()\npanel_lca_class_instance = panel_lca_class()\n\n# COLUMN 1 ####################################################################\n\ndef button_action_load_database(event):\n    panel_lca_class_instance.set_db(event)\n    panel_lca_class_instance.set_list_db_products(event)\n    panel_lca_class_instance.set_methods_objects(event)\n    widget_autocomplete_product.options = panel_lca_class_instance.list_db_products\n    widget_select_method.options = panel_lca_class_instance.list_db_methods\n    widget_select_method.value = [item for item in panel_lca_class_instance.list_db_methods if 'GCC' in item[0]][0] # global warming as default value\n\n\ndef button_action_perform_lca(event):\n    if widget_autocomplete_product.value == '':\n        pn.state.notifications.error('Please select a reference product first!', duration=5000)\n        return\n    else:\n        panel_lca_class_instance.df_graph_traversal_nodes = pd.DataFrame()\n        widget_plotly_figure_piechart.object = create_plotly_figure_piechart({'null':0})\n        pn.state.notifications.info('Calculating LCA score...', duration=5000)\n        pass\n    panel_lca_class_instance.set_chosen_activity(event)\n    panel_lca_class_instance.set_chosen_method_and_unit(event)\n    panel_lca_class_instance.set_chosen_amount(event)\n    panel_lca_class_instance.perform_lca(event)\n    pn.state.notifications.success('Completed LCA score calculation!', duration=5000)\n    widget_number_lca_score.format = f'{{value:,.3f}} {panel_lca_class_instance.chosen_method_unit}'\n    perform_graph_traversal(event)\n    perform_scope_analysis(event)\n\n\ndef perform_graph_traversal(event):\n    pn.state.notifications.info('Performing Graph Traversal...', duration=5000)\n    panel_lca_class_instance.bool_user_provided_data = False\n    panel_lca_class_instance.set_graph_traversal_cutoff(event)\n    panel_lca_class_instance.perform_graph_traversal(event)\n    panel_lca_class_instance.df_tabulator = panel_lca_class_instance.df_tabulator_from_traversal.copy()\n    widget_tabulator.value = panel_lca_class_instance.df_tabulator\n    column_editors = {\n        colname: None\n        for colname in panel_lca_class_instance.df_tabulator.columns\n        if colname not in ['Scope', 'SupplyAmount', 'BurdenIntensity']\n    }\n    column_editors['Scope'] = {'type': 'list', 'values': [1, 2, 3]}\n    widget_tabulator.editors = column_editors\n    pn.state.notifications.success('Graph Traversal Complete!', duration=5000)\n\n\n\ndef perform_scope_analysis(event):\n    pn.state.notifications.info('Performing Scope Analysis...', duration=5000)\n    panel_lca_class_instance.scope_dict = determine_scope_emissions(df=widget_tabulator.value)\n    widget_plotly_figure_piechart.object = create_plotly_figure_piechart(panel_lca_class_instance.scope_dict)\n    filename_download.value = generate_table_filename()\n    widget_number_lca_score.value = panel_lca_class_instance.df_tabulator['Burden(Direct)'].sum()\n    pn.state.notifications.success('Scope Analysis Complete!', duration=5000)\n\n\ndef update_data_based_on_user_input(event):\n    pn.state.notifications.info('Updating Supply Chain based on User Input...', duration=5000)\n    panel_lca_class_instance.df_tabulator_from_user = create_user_input_columns(\n        df_original=panel_lca_class_instance.df_tabulator_from_traversal,\n        df_user_input=panel_lca_class_instance.df_tabulator_from_user,\n    )\n    panel_lca_class_instance.df_tabulator_from_user = determine_edited_rows(df=panel_lca_class_instance.df_tabulator_from_user)\n    panel_lca_class_instance.df_tabulator_from_user = update_production_based_on_user_data(df=panel_lca_class_instance.df_tabulator_from_user)\n    panel_lca_class_instance.df_tabulator_from_user = update_burden_intensity_based_on_user_data(df=panel_lca_class_instance.df_tabulator_from_user)\n    panel_lca_class_instance.df_tabulator_from_user = update_burden_based_on_user_data(panel_lca_class_instance.df_tabulator_from_user)\n    panel_lca_class_instance.df_tabulator = panel_lca_class_instance.df_tabulator_from_user.copy()\n    widget_tabulator.value = panel_lca_class_instance.df_tabulator\n    pn.state.notifications.success('Completed Updating Supply Chain based on User Input!', duration=5000)\n\ndef button_action_scope_analysis(event):\n    if panel_lca_class_instance.lca is None:\n        pn.state.notifications.error('Please perform an LCA Calculation first!', duration=5000)\n        return\n    else:\n        # if the user has not yet performed graph traversal, or changed the cutoff value,\n        # then perform graph traversal and scope analysis\n        if (\n            panel_lca_class_instance.df_graph_traversal_nodes.empty or\n            widget_float_slider_cutoff.value / 100 != panel_lca_class_instance.graph_traversal_cutoff\n        ):\n            perform_graph_traversal(event)\n            perform_scope_analysis(event)\n        # if the user has already provided input data, we reset the dataframe\n        elif panel_lca_class_instance.bool_user_provided_data == True:\n            pn.state.notifications.error('You are not allowed to edit a table which has already been re-computed on your input! Resetting the table...', duration=5000)\n            perform_graph_traversal(event)\n            perform_scope_analysis(event)\n        # if the user has overriden either supply or burden intensity values in the table,\n        # then update upstream values based on user input\n        elif (\n            (panel_lca_class_instance.df_tabulator['SupplyAmount'] != panel_lca_class_instance.df_tabulator_from_traversal['SupplyAmount']).any() or \n            (panel_lca_class_instance.df_tabulator['BurdenIntensity'] != panel_lca_class_instance.df_tabulator_from_traversal['BurdenIntensity']).any()\n        ):\n            panel_lca_class_instance.bool_user_provided_data = True\n            panel_lca_class_instance.df_tabulator_from_user = panel_lca_class_instance.df_tabulator.copy()\n            update_data_based_on_user_input(event)\n            perform_scope_analysis(event)\n        elif (\n            (panel_lca_class_instance.df_tabulator['Scope'] != panel_lca_class_instance.df_tabulator_from_traversal['Scope']).any()\n        ):\n            perform_scope_analysis(event)\n\n\nwidget_button_load_db = pn.widgets.Button( \n    name='Load USEEIO Database',\n    icon='database-plus',\n    button_type='primary',\n    sizing_mode='stretch_width'\n)\nwidget_button_load_db.on_click(button_action_load_database)\n\nwidget_autocomplete_product = pn.widgets.AutocompleteInput( \n    name='Reference Product/Product/Service',\n    options=[],\n    case_sensitive=False,\n    search_strategy='includes',\n    placeholder='Start typing your product name here...',\n    sizing_mode='stretch_width'\n)\n\nmarkdown_method_documentation = pn.pane.Markdown("""\nThe impact assessment methods are documented [in Table 3](https://www.nature.com/articles/s41597-022-01293-7/tables/4) of the [USEEIO release article](https://doi.org/10.1038/s41597-022-01293-7).\n""")\n\nwidget_select_method = pn.widgets.Select( \n    name='Impact Assessment Method',\n    options=[],\n    sizing_mode='stretch_width',\n\n)\n\nwidget_float_input_amount = pn.widgets.FloatInput( \n    name='(Monetary) Amount of Reference Product [USD]',\n    value=100,\n    step=1,\n    start=0,\n    sizing_mode='stretch_width'\n)\n\nwidget_button_lca = pn.widgets.Button( \n    name='Compute LCA Score',\n    icon='calculator',\n    button_type='primary',\n    sizing_mode='stretch_width'\n)\nwidget_button_lca.on_click(button_action_perform_lca)\n\nwidget_float_slider_cutoff = pn.widgets.EditableFloatSlider(\n    name='Graph Traversal Cut-Off [%]',\n    start=1,\n    end=50,\n    step=1,\n    value=10,\n    sizing_mode='stretch_width'\n)\n\nmarkdown_cutoff_documentation = pn.pane.Markdown("""\n[A cut-off of 10%](https://docs.brightway.dev/projects/graphtools/en/latest/content/api/bw_graph_tools/graph_traversal/new_node_each_visit/index.html) means that only those processes responsible or 90% of impact will be computed.\n""")\n        \nwidget_button_graph = pn.widgets.Button(\n    name='Update Data based on User Input',\n    icon='chart-donut-3',\n    button_type='primary',\n    sizing_mode='stretch_width'\n)\nwidget_button_graph.on_click(button_action_scope_analysis)\n\nwidget_number_lca_score = pn.indicators.Number(\n    name='LCA Impact Score',\n    font_size='30pt',\n    title_size='20pt',\n    value=0,\n    format='{value:,.3f}',\n    margin=0\n)\n\nwidget_plotly_figure_piechart = pn.pane.Plotly(\n    create_plotly_figure_piechart(\n        {'Scope 1': 0}\n    )\n)\n\ncol1 = pn.Column(\n    '# LCA Settings',\n    widget_button_load_db,\n    widget_autocomplete_product,\n    markdown_method_documentation,\n    widget_select_method,\n    widget_float_input_amount,\n    markdown_cutoff_documentation,\n    widget_float_slider_cutoff,\n    widget_button_lca,\n    widget_button_graph,\n    pn.Spacer(height=10),\n    widget_number_lca_score,\n    widget_plotly_figure_piechart,\n)\n\n# COLUMN 2 ####################################################################\n\n\ndef highlight_tabulator_cells(tabulator_row):\n    """\n    Applies a background color to all rows where the 'Edited?' column is True.\n\n    See Also\n    --------\n    - https://stackoverflow.com/a/48306463\n    - https://discourse.holoviz.org/t/dynamic-update-of-tabulator-style\n    """\n    if tabulator_row['Edited?'] == True:\n        return ['background-color: orange'] * len(tabulator_row)\n    else:\n        return [''] * len(tabulator_row)\n    \n\nwidget_tabulator = pn.widgets.Tabulator(\n    pd.DataFrame([['']], columns=['Data will appear here after calculations...']),\n    theme='site',\n    show_index=False,\n    hidden_columns=['activity_datapackage_id', 'producer_unique_id'],\n    layout='fit_data_stretch',\n    sizing_mode='stretch_width'\n)\nwidget_tabulator.style.apply(highlight_tabulator_cells, axis=1)\n\nfilename_download, button_download = widget_tabulator.download_menu(\n    text_kwargs={'name': 'Filename', 'value': 'filename.csv'},\n    button_kwargs={'name': 'Download Table'}\n)\nfilename_download.sizing_mode = 'stretch_width'\nbutton_download.align = 'center'\nbutton_download.icon = 'download'\n\nwidget_cutoff_indicator_statictext = pn.widgets.StaticText(\n    name='Includes processes responsible for amount of emissions [%]',\n    value=None\n)\n\ncol2 = pn.Column(\n    pn.Row('# Table of Upstream Processes', filename_download, button_download),\n    widget_tabulator\n)\n\n# SITE ######################################################################\n\ncode_open_window = """\nwindow.open("https://github.com/brightway-lca/brightway-webapp/blob/main/README.md")\n"""\nbutton_about = pn.widgets.Button(name="Learn more about this prototype...", button_type="success")\nbutton_about.js_on_click(code=code_open_window)\n\nheader = pn.Row(\n    button_about,\n    pn.HSpacer(),\n    pn.pane.SVG(\n        'https://raw.githubusercontent.com/brightway-lca/brightway-webapp/main/app/_media/logo_PSI-ETHZ-WISER_white.svg',\n        #height=50,\n        margin=0,\n        align="center"\n    ),\n    sizing_mode="stretch_width",\n)\n\ntemplate = pn.template.MaterialTemplate(\n    header=header,\n    title='Brightway WebApp (Carbon Accounting)',\n    header_background='#2d853a', # green\n    logo='https://raw.githubusercontent.com/brightway-lca/brightway-webapp/main/app/_media/logo_brightway_white.svg',\n    favicon='https://raw.githubusercontent.com/brightway-lca/brightway-webapp/main/app/_media/favicon.png',\n)\n\ngspec = pn.GridSpec(ncols=3, sizing_mode='stretch_both')\ngspec[:,0:1] = col1 # 1/3rd of the width\ngspec[:,1:3] = col2 # 2/3rds of the width\n\ntemplate.main.append(gspec)\ntemplate.servable()\n\nawait write_doc()
+  \nimport asyncio\n\nfrom panel.io.pyodide import init_doc, write_doc\n\ninit_doc()\n\nimport panel as pn\npn.extension(notifications=True)\npn.extension(design='material')\npn.extension('plotly')\npn.extension('tabulator')\n\n# plotting\nimport plotly\n\n# data science\nimport pandas as pd\nimport numpy as np\n\n# system\nimport os\n\n# brightway\nimport bw_graph_tools as bgt\nimport bw2io as bi\nimport bw2data as bd\nimport bw2calc as bc\n\n# type hints\nfrom bw2data.backends.proxies import Activity\nfrom bw_graph_tools.graph_traversal import Node\nfrom bw_graph_tools.graph_traversal import Edge\n\n\ndef brightway_wasm_database_storage_workaround() -> None:\n    """\n    Sets the Brightway project directory to \`/tmp/.\n    \n    The JupyterLite file system currently does not support storage of SQL database files\n    in directories other than \`/tmp/\`. This function sets the Brightway environment variable\n    \`BRIGHTWAY_DIR\` to \`/tmp/\` to work around this limitation.\n    \n    Notes\n    -----\n    - https://docs.brightway.dev/en/latest/content/faq/data_management.html#how-do-i-change-my-data-directory\n    - https://github.com/brightway-lca/brightway-live/issues/10\n    """\n    os.environ["BRIGHTWAY_DIR"] = "/tmp/"\n\n\ndef check_for_useeio_brightway_project(event):\n    """\n    Checks if the USEEIO-1.1 Brightway project is installed.\n    If not, installs it. Shows Panel notifications for the user.\n\n    Returns\n    -------\n    SQLiteBackend\n        bw2data.backends.base.SQLiteBackend of the USEEIO-1.1 database\n    """\n    if 'USEEIO-1.1' not in bd.projects:\n        notification_load = pn.state.notifications.info('Loading USEEIO database...')\n        bi.install_project(project_key="USEEIO-1.1", overwrite_existing=True)\n        notification_load.destroy()\n        pn.state.notifications.success('USEEIO database loaded!', duration=7000)\n    else:\n        pn.state.notifications.success('USEEIO database already loaded!', duration=7000)\n        pass\n    bd.projects.set_current(name='USEEIO-1.1')\n\n\ndef nodes_dict_to_dataframe(\n        nodes: dict,\n        uid_electricity: int = 53 # hardcoded for USEEIO\n    ) -> pd.DataFrame:\n    """\n    Returns a dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal.\n\n    Parameters\n    ----------\n    nodes : dict\n        A dictionary of nodes in the graph traversal.\n        Can be created by selecting the 'nodes' key from the dictionary\n        returned by the function \`bw_graph_tools.NewNodeEachVisitGraphTraversal.calculate()\`.\n\n    Returns\n    -------\n    pd.DataFrame\n        A dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal.\n    """\n    list_of_row_dicts = []\n    for current_node in nodes.values():\n\n        scope: int = 3\n        if current_node.unique_id == -1:\n            continue\n        elif current_node.unique_id == 0:\n            scope = 1\n        elif current_node.activity_datapackage_id == uid_electricity:\n            scope = 2\n        else:\n            pass\n        list_of_row_dicts.append(\n            {\n                'UID': current_node.unique_id,\n                'Scope': scope,\n                'Name': bd.get_node(id=current_node.activity_datapackage_id)['name'],\n                'SupplyAmount': current_node.supply_amount,\n                'BurdenIntensity': current_node.direct_emissions_score/current_node.supply_amount,\n                # 'Burden(Cumulative)': current_node.cumulative_score,\n                'Burden(Direct)': current_node.direct_emissions_score + current_node.direct_emissions_score_outside_specific_flows,\n                'Depth': current_node.depth,\n                'activity_datapackage_id': current_node.activity_datapackage_id,\n            }\n        )\n    return pd.DataFrame(list_of_row_dicts)\n\n\ndef edges_dict_to_dataframe(edges: list) -> pd.DataFrame:\n    """\n    To be added...\n    """\n    if len(edges) < 2:\n        return pd.DataFrame()\n    else:\n        list_of_row_dicts = []\n        for current_edge in edges:\n            list_of_row_dicts.append(\n                {\n                    'consumer_unique_id': current_edge.consumer_unique_id,\n                    'producer_unique_id': current_edge.producer_unique_id\n                }\n            )\n        return pd.DataFrame(list_of_row_dicts).drop(0)\n\n\ndef trace_branch(df: pd.DataFrame, start_node: int) -> list:\n    """\n    Given a dataframe of graph edges and a "starting node" (producer_unique_id), returns the branch of nodes that lead to the starting node.\n\n    For example:\n\n    | consumer_unique_id | producer_unique_id |\n    |--------------------|--------------------|\n    | 0                  | 1                  | # 1 is terminal producer node\n    | 0                  | 2                  |\n    | 0                  | 3                  |\n    | 2                  | 4                  | # 4 is terminal producer node\n    | 3                  | 5                  |\n    | 5                  | 6                  | # 6 is terminal producer node\n\n    For start_node = 6, the function returns [0, 3, 5, 6]\n\n    Parameters\n    ----------\n    df : pd.DataFrame\n        Dataframe of graph edges. Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.\n    start_node : int\n        The integer indicating the producer_unique_id starting node to trace back from.\n\n    Returns\n    -------\n    list\n        A list of integers indicating the branch of nodes that lead to the starting node.\n    """\n\n    branch: list = [start_node]\n\n    while True:\n        previous_node: int = df[df['producer_unique_id'] == start_node]['consumer_unique_id']\n        if previous_node.empty:\n            break\n        start_node: int = previous_node.values[0]\n        branch.insert(0, start_node)\n\n    return branch\n\n\ndef add_branch_information_to_edges_dataframe(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Adds 'branch' information to terminal nodes in a dataframe of graph edges.\n\n    For example:\n\n    | consumer_unique_id | producer_unique_id |\n    |--------------------|--------------------|\n    | 0                  | 1                  | # 1 is terminal producer node\n    | 0                  | 2                  |\n    | 0                  | 3                  |\n    | 2                  | 4                  | # 4 is terminal producer node\n    | 3                  | 5                  |\n    | 5                  | 6                  | # 6 is terminal producer node\n\n    | consumer_unique_id | producer_unique_id | branch       |\n    |--------------------|--------------------|--------------|\n    | 0                  | 1                  | [0, 1]       |\n    | 0                  | 2                  | [0, 2]       |\n    | 0                  | 3                  | [0, 3]       |\n    | 2                  | 4                  | [0, 2, 4]    |\n    | 3                  | 5                  | [0, 3, 5]    |\n    | 5                  | 6                  | [0, 3, 5, 6] |\n\n    Parameters\n    ----------\n    df_edges : pd.DataFrame\n        A dataframe of graph edges.\n        Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.\n\n    Returns\n    -------\n    pd.DataFrame\n        A dataframe of graph nodes with a column 'branch' that contains the branch of nodes that lead to the terminal producer node.\n    """\n    # initialize empty list to store branches\n    branches: list = []\n\n    for _, row in df.iterrows():\n        branch: list = trace_branch(df, int(row['producer_unique_id']))\n        branches.append({\n            'producer_unique_id': int(row['producer_unique_id']),\n            'Branch': branch\n        })\n\n    return pd.DataFrame(branches)\n\n\ndef create_user_input_columns(\n        df_original: pd.DataFrame,\n        df_user_input: pd.DataFrame,\n    ) -> pd.DataFrame:\n    """\n    Creates a new column in the 'original' DataFrame where only the\n    user-supplied values are kept. The other values are replaced by NaN.\n\n    For instance, given an "original" DataFrame of the kind:\n\n    | UID | SupplyAmount | BurdenIntensity |\n    |-----|--------------|-----------------|\n    | 0   | 1            | 0.1             |\n    | 1   | 0.5          | 0.5             |\n    | 2   | 0.2          | 0.3             |\n\n    and a "user input" DataFrame of the kind:\n\n    | UID | SupplyAmount | BurdenIntensity |\n    |-----|--------------|-----------------|\n    | 0   | 1            | 0.1             |\n    | 1   | 0            | 0.5             |\n    | 2   | 0.2          | 2.1             |\n\n    the function returns a DataFrame of the kind:\n\n    | UID | SupplyAmount | SupplyAmount_USER | BurdenIntensity | BurdenIntensity_USER |\n    |-----|--------------|-------------------|-----------------|----------------------|\n    | 0   | 1            | NaN               | 0.1             | NaN                  |\n    | 1   | 0.5          | 0                 | 0.5             | NaN                  |\n    | 2   | 0.2          | NaN               | 0.3             | 2.1                  |\n\n    Parameters\n    ----------\n    df_original : pd.DataFrame\n        Original DataFrame.\n\n    df_user_input : pd.DataFrame\n        User input DataFrame.\n    """\n    \n    df_merged = pd.merge(\n        df_original,\n        df_user_input[['UID', 'SupplyAmount', 'BurdenIntensity']],\n        on='UID',\n        how='left',\n        suffixes=('', '_USER')\n    )\n\n    for column_name in ['SupplyAmount', 'BurdenIntensity']:\n        df_merged[f'{column_name}_USER'] = np.where(\n            df_merged[f'{column_name}_USER'] != df_merged[f'{column_name}'],\n            df_merged[f'{column_name}_USER'],\n            np.nan\n        )\n\n    return df_merged\n\n\ndef update_burden_intensity_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Updates the burden intensity when user data is provided.\n\n    For instance, given a DataFrame of the kind:\n\n    | UID | BurdenIntensity | BurdenIntensity_USER |\n    |-----|-----------------|----------------------|\n    | 0   | 0.1             | NaN                  |\n    | 1   | 0.5             | 0.25                 |\n    | 2   | 0.3             | NaN                  |\n\n    the function returns a DataFrame of the kind:\n\n    | UID | BurdenIntensity |\n    |-----|-----------------|\n    | 0   | 0.1             |\n    | 1   | 0.25            |\n    | 2   | 0.3             |\n\n    Parameters\n    ----------\n    df : pd.DataFrame\n        Input dataframe.\n\n    Returns\n    -------\n    pd.DataFrame\n        Output dataframe.\n    """\n\n\n    df['BurdenIntensity'] = df['BurdenIntensity_USER'].combine_first(df['BurdenIntensity'])\n    df = df.drop(columns=['BurdenIntensity_USER'])\n\n    return df\n\n\ndef update_production_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Updates the production amount of all nodes which are upstream\n    of a node with user-supplied production amount.\n    If an upstream node has half the use-supplied production amount,\n    then the production amount of all downstream node is also halved.\n\n    For instance, given a DataFrame of the kind:\n\n    | UID | SupplyAmount | SupplyAmount_USER | Branch        |\n    |-----|--------------|-------------------|---------------|\n    | 0   | 1            | NaN               | NaN           |\n    | 1   | 0.5          | 0.25              | [0,1]         |\n    | 2   | 0.2          | NaN               | [0,1,2]       |\n    | 3   | 0.1          | NaN               | [0,3]         |\n    | 4   | 0.1          | 0.18              | [0,1,2,4]     |\n    | 5   | 0.05         | NaN               | [0,1,2,4,5]   |\n    | 6   | 0.01         | NaN               | [0,1,2,4,5,6] |\n\n    the function returns a DataFrame of the kind:\n\n    | UID | SupplyAmount      | Branch        |\n    |-----|-------------------|---------------|\n    | 0   | 1                 | NaN           |\n    | 1   | 0.25              | [0,1]         | NOTA BENE!\n    | 2   | 0.2 * (0.25/0.5)  | [0,1,2]       |\n    | 3   | 0.1               | [0,3]         |\n    | 4   | 0.18              | [0,1,2,4]     | NOTA BENE!\n    | 5   | 0.05 * (0.1/0.18) | [0,1,2,4,5]   |\n    | 6   | 0.01 * (0.1/0.18) | [0,1,2,4,5,6] |\n\n    Notes\n    -----\n\n    As we can see, the function updates production only\n    for those nodes upstream of a node with 'production_user':\n\n    - Node 2 is upstream of node 1, which has a 'production_user' value.\n    - Node 3 is NOT upstream of node 1. It is upstream of node 0, but node 0 does not have a 'production_user' value.\n\n    As we can see, the function always takes the "most recent"\n    'production_user' value upstream of a node:\n\n    - Node 5 is upstream of node 4, which has a 'production_user' value.\n    - Node 4 is upstream of node 1, which also has a 'production_user' value.\n\n    In this case, the function takes the 'production_user' value of node 4, not of node 1.\n\n    Parameters\n    ----------\n    df : pd.DataFrame\n        Input DataFrame. Must have the columns 'production', 'production_user' and 'branch'.\n\n    Returns\n    -------\n    pd.DataFrame\n        Output DataFrame.\n    """\n\n    df_filtered = df[~df['SupplyAmount_USER'].isna()]\n    dict_user_input = df_filtered.set_index('UID').to_dict()['SupplyAmount_USER']\n    \n    """\n    For the example DataFrame from the docstrings above,\n    the dict_user_input would be:\n\n    dict_user_input = {\n        1: 0.25,\n        4: 0.18\n    }\n    """\n\n    df = df.copy(deep=True)\n    def multiplier(row):\n        if not isinstance(row['Branch'], list):\n            return row['SupplyAmount']\n        elif (\n            row['UID'] == row['Branch'][-1] and\n            np.isnan(row['SupplyAmount_USER'])\n        ):\n            return row['SupplyAmount']\n        elif (\n            row['UID'] == row['Branch'][-1] and not\n            np.isnan(row['SupplyAmount_USER'])\n        ):\n            return row['SupplyAmount_USER']\n        else:\n            for branch_UID in reversed(row['Branch']):\n                if branch_UID in dict_user_input:\n                    return row['SupplyAmount'] * dict_user_input[branch_UID]\n\n    df['SupplyAmount_EDITED'] = df.apply(multiplier, axis=1)\n\n    df.drop(columns=['SupplyAmount_USER'], inplace=True)\n    df['SupplyAmount'] = df['SupplyAmount_EDITED']\n    df.drop(columns=['SupplyAmount_EDITED'], inplace=True)\n\n    return df\n\n\ndef update_burden_based_on_user_data(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Updates the environmental burden of nodes\n    by multiplying the burden intensity and the supply amount.\n\n    Parameters\n    ----------\n    df : pd.DataFrame\n        Input dataframe.\n\n    Returns\n    -------\n    pd.DataFrame\n        Output dataframe.\n    """\n\n    df['Burden(Direct)'] = df['SupplyAmount'] * df['BurdenIntensity']\n    return df\n\n\ndef determine_edited_rows(df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Determines which rows have been edited by the user.\n\n    For instance, given a DataFrame of the kind:\n\n    | UID | SupplyAmount_USER | BurdenIntensity_USER |\n    |-----|-------------------|----------------------|\n    | 0   | NaN               | NaN                  |\n    | 1   | 0.25              | NaN                  |\n    | 2   | NaN               | 2.1                  |\n    | 3   | NaN               | NaN                  |\n\n    the function returns a DataFrame of the kind:\n\n    | UID | SupplyAmount_USER | BurdenIntensity_USER | Edited? |\n    |-----|-------------------|----------------------|---------|\n    | 0   | NaN               | NaN                  | False   |\n    | 1   | 0.25              | NaN                  | True    |\n    | 2   | NaN               | 2.1                  | True    |\n    | 3   | NaN               | NaN                  | False   |\n    """\n    df['Edited?'] = df[['SupplyAmount_USER', 'BurdenIntensity_USER']].notnull().any(axis=1)\n    return df\n\n\ndef create_plotly_figure_piechart(data_dict: dict) -> plotly.graph_objects.Figure:\n    marker_colors = []\n    for label in data_dict.keys():\n        if label == 'Scope 1':\n            marker_colors.append('#33cc33')  # Color for Scope 1\n        elif label == 'Scope 2':\n            marker_colors.append('#ffcc00')  # Color for Scope 2\n        elif label == 'Scope 3':\n            marker_colors.append('#3366ff')  # Color for Scope 3\n        else:\n            marker_colors.append('#000000')  # Default color for other labels\n\n    plotly_figure = plotly.graph_objects.Figure(\n        data=[\n            plotly.graph_objects.Pie(\n                labels=list(data_dict.keys()),\n                values=list(data_dict.values()),\n                marker=dict(colors=marker_colors)  # Set the colors for the pie chart\n            )\n        ]\n    )\n    plotly_figure.update_traces(\n        marker=dict(\n            line=dict(color='#000000', width=2)\n        )\n    )\n    plotly_figure.update_layout(\n        autosize=True,\n        height=300,\n        legend=dict(\n            orientation="v",\n            yanchor="auto",\n            y=1,\n            xanchor="right",\n            x=-0.3\n        ),\n        margin=dict(\n            l=50,\n            r=50,\n            b=0,\n            t=0,\n            pad=0\n        ),\n    )\n    return plotly_figure\n\n\ndef generate_table_filename() -> str:\n    """\n    Generates a string to be used a filename for downloading the tabulator.value DataFrame.\n\n    Returns\n    -------\n    str\n        Filename string.\n    """\n    str_filename: str = (\n        "activity='"\n        + panel_lca_class_instance.chosen_activity['name'].replace(' ', '_').replace(';', '') .replace(',', '')\n        + "'_method='"\n        + '-'.join(panel_lca_class_instance.chosen_method.name).replace(' ', '-')\n        + "'_cutoff=" \n        + str(panel_lca_class_instance.graph_traversal_cutoff).replace('.', ',') \n        + ".csv"\n    )\n    return str_filename\n\n\ndef determine_scope_emissions(df: pd.DataFrame):\n        """\n        Determines the scope 1/2/3 emissions from the graph traversal nodes dataframe.\n        """\n        dict_scope = {\n            'Scope 1': 0,\n            'Scope 2': 0,\n            'Scope 3': 0\n        }\n        \n        dict_scope['Scope 1'] = df.loc[(df['Scope'] == 1)]['Burden(Direct)'].values.sum()\n        dict_scope['Scope 2'] = df.loc[(df['Scope'] == 2)]['Burden(Direct)'].values.sum()\n        dict_scope['Scope 3'] = df['Burden(Direct)'].sum() - dict_scope['Scope 1'] - dict_scope['Scope 2']\n\n        return dict_scope\n\nclass panel_lca_class:\n    """\n    This class is used to store all the necessary information for the LCA calculation.\n    It provides methods to populate the database and perform Brightway LCA calculations.\n    All methods can be bound to a button click event.\n\n    Notes\n    -----\n    https://discourse.holoviz.org/t/update-global-variable-through-function-bound-with-on-click/\n    """\n    brightway_wasm_database_storage_workaround()\n    def __init__(self):\n        self.db_name = 'USEEIO-1.1'\n        self.db = None\n        self.list_db_products = []\n        self.dict_db_methods = {}\n        self.list_db_methods = []\n        self.chosen_activity = ''\n        self.chosen_method = ''\n        self.chosen_method_unit = ''\n        self.chosen_amount = 0\n        self.lca = None\n        self.scope_dict = {'Scope 1':0, 'Scope 2':0, 'Scope 3':0}\n        self.graph_traversal_cutoff = 1\n        self.graph_traversal = {}\n        self.df_graph_traversal_nodes = None\n        self.df_graph_traversal_edges = None\n        self.df_tabulator_from_traversal = None\n        self.df_tabulator_from_user = None\n        self.df_tabulator = None # nota bene: gets updated automatically when cells in the tabulator are edited # https://panel.holoviz.org/reference/widgets/Tabulator.html#editors-editing\n        self.bool_user_provided_data = False\n\n\n    def set_db(self, event):\n        """\n        Checks if the USEEIO-1.1 Brightway project is installed.\n        If not, installs it and sets is as current project.\n        Else just sets the current project to USEEIO-1.1.\n        """\n        check_for_useeio_brightway_project(event)\n        self.db = bd.Database(self.db_name)\n\n\n    def set_list_db_products(self, event):\n        """\n        Sets \`list_db_products\` to a list of product names from the database for use in the autocomplete widget.\n        """\n        self.list_db_products = [node['name'] for node in self.db if 'product' in node['type']]\n    \n\n    def set_methods_objects(self, event):\n        """\n        dict_methods = {\n            'HRSP': ('Impact Potential', 'HRSP'),\n            'OZON': ('Impact Potential', 'OZON'),\n            ...\n        }\n        """\n        dict_methods = {i[-1]:[i] for i in bd.methods}\n\n        """\n        dict_method_names = {\n            'HRSP': 'Human Health: Respiratory effects',\n            'OZON': 'Ozone Depletion',\n            ...\n        }\n        """\n        # hardcoded for better Pyodide performance\n        dict_methods_names = {\n            "HRSP": "Human Health - Respiratory Effects",\n            "OZON": "Ozone Depletion",\n            "HNC": "Human Health Noncancer",\n            "WATR": "Water",\n            "METL": "Metals",\n            "EUTR": "Eutrophication",\n            "HTOX": "Human Health Cancer and Noncancer",\n            "LAND": "Land",\n            "NREN": "Nonrenewable Energy",\n            "ETOX": "Freshwater Aquatic Ecotoxicity",\n            "PEST": "Pesticides",\n            "REN": "Renewable Energy",\n            "MINE": "Minerals and Metals",\n            "GCC": "Global Climate Change",\n            "ACID": "Acid Rain",\n            "HAPS": "Hazardous Air Pollutants",\n            "HC": "Human Health Cancer",\n            "SMOG": "Smog Formation",\n            "ENRG": "Energy"\n        }\n        # path_impact_categories_names: str = '../app/_data/USEEIO_impact_categories_names.csv'\n        # dict_methods_names = {}\n        # with open(path_impact_categories_names, mode='r', newline='', encoding='utf-8-sig') as file:\n        #    reader = csv.reader(file)\n        #    dict_methods_names = {rows[0]: rows[1] for rows in reader}\n\n        """\n        dict_methods_units = {\n            'HRSP': '[kg PM2.5 eq]',\n            'OZON': '[kg O3 eq]',\n            ...\n        }\n        """\n        # hardcoded for better Pyodide performance\n        dict_methods_units = {\n            "HRSP": "[kg PM2.5 eq]",\n            "OZON": "[kg O3 eq]",\n            "HNC": "[CTUh]",\n            "WATR": "[m3]",\n            "METL": "[kg]",\n            "EUTR": "[kg N eq]",\n            "HTOX": "[CTUh]",\n            "LAND": "[m2*yr]",\n            "NREN": "[MJ]",\n            "ETOX": "[CTUe]",\n            "PEST": "[kg]",\n            "REN": "[MJ]",\n            "MINE": "[kg]",\n            "GCC": "[kg CO2 eq]",\n            "ACID": "[kg SO2 eq]",\n            "HAPS": "[kg]",\n            "HC": "[CTUh]",\n            "SMOG": "[kg O3 eq]",\n            "ENRG": "[MJ]"\n        }\n        # path_impact_categories_units: str = '../app/_data/USEEIO_impact_categories_units.csv'\n        # dict_methods_units = {}\n        # with open(path_impact_categories_units, mode='r', newline='', encoding='utf-8-sig') as file:\n        #    reader = csv.reader(file)\n        #    dict_methods_units = {rows[0]: str('[')+rows[1]+str(']') for rows in reader}\n\n        """\n        dict_methods_enriched = {\n            'HRSP': [('Impact Potential', 'HRSP'), 'Human Health - Respiratory effects', '[kg PM2.5 eq]'],\n            'OZON': [('Impact Potential', 'OZON'), 'Ozone Depletion', '[kg O3 eq]'],\n            ...\n        }\n        """\n        dict_methods_enriched = {\n            key: [dict_methods[key][0], dict_methods_names[key], dict_methods_units[key]]\n            for key in dict_methods\n        }\n\n        """\n        list_methods_for_autocomplete = [\n            ('HRSP', 'Human Health: Respiratory effects', '[kg PM2.5 eq]'),\n            ('OZON', 'Ozone Depletion', '[kg O3 eq]'),\n            ...\n        ]\n        """\n        list_methods_for_autocomplete = [(key, value[1], value[2]) for key, value in dict_methods_enriched.items()]\n\n        self.dict_db_methods = dict_methods_enriched\n        self.list_db_methods = list_methods_for_autocomplete\n\n\n    def set_chosen_activity(self, event):\n        """\n        Sets \`chosen_activity\` to the \`bw2data.backends.proxies.Activity\` object of the chosen product from the autocomplete widget.\n        """\n        self.chosen_activity: Activity = bd.utils.get_node(\n            database = self.db_name,\n            name = widget_autocomplete_product.value,\n            type = 'product',\n            location = 'United States'\n        )\n\n\n    def set_chosen_method_and_unit(self, event):\n        """\n        Sets \`chosen_method\` to the (tuple) corresponding to the chosen method string from the select widget.\n\n        Example:\n        --------\n        widget_select_method.value = ('HRSP', 'Human Health: Respiratory effects', '[kg PM2.5 eq]')\n        widget_select_method.value[0] = 'HRSP'\n        dict_db_methods = {'HRSP': [('Impact Potential', 'HRSP'), 'Human Health - Respiratory effects', '[kg PM2.5 eq]']}\n        dict_db_methods['HRSP'][0] = ('Impact Potential', 'HRSP') # which is the tuple that bd.Method needs\n        """\n        self.chosen_method = bd.Method(self.dict_db_methods[widget_select_method.value[0]][0])\n        self.chosen_method_unit = widget_select_method.value[2]\n\n\n    def set_chosen_amount(self, event):\n        """\n        Sets \`chosen_amount\` to the float value from the float input widget.\n        """\n        self.chosen_amount = widget_float_input_amount.value\n\n\n    def perform_lca(self, event):\n        """\n        Performs the LCA calculation using the chosen product, method, and amount.\n        Sets the \`lca\` attribute to an instance of the \`bw2calc.LCA\` object.\n        """\n        self.lca = bc.LCA( \n            demand={self.chosen_activity: self.chosen_amount}, \n            method = self.chosen_method.name\n        )\n        self.lca.lci()\n        self.lca.lcia()\n\n\n    def set_graph_traversal_cutoff(self, event):\n        """\n        Sets the \`graph_traversal_cutoff\` attribute to the float value from the float slider widget.\n        Note that the value is divided by 100 to convert from percentage to decimal.\n        """\n        self.graph_traversal_cutoff = widget_float_slider_cutoff.value / 100\n\n\n    def perform_graph_traversal(self, event):\n        widget_cutoff_indicator_statictext.value = self.graph_traversal_cutoff * 100\n        self.graph_traversal: dict = bgt.NewNodeEachVisitGraphTraversal.calculate(self.lca, cutoff=self.graph_traversal_cutoff)\n        self.df_graph_traversal_nodes: pd.DataFrame = nodes_dict_to_dataframe(self.graph_traversal['nodes'])\n        self.df_graph_traversal_edges: pd.DataFrame = edges_dict_to_dataframe(self.graph_traversal['edges'])\n        if self.df_graph_traversal_edges.empty:\n            return\n        else:\n            self.df_graph_traversal_edges = add_branch_information_to_edges_dataframe(self.df_graph_traversal_edges)\n            self.df_tabulator_from_traversal = pd.merge(\n                self.df_graph_traversal_nodes,\n                self.df_graph_traversal_edges,\n                left_on='UID',\n                right_on='producer_unique_id',\n                how='left')\n\n\nbrightway_wasm_database_storage_workaround()\npanel_lca_class_instance = panel_lca_class()\n\n# COLUMN 1 ####################################################################\n\ndef button_action_load_database(event):\n    panel_lca_class_instance.set_db(event)\n    panel_lca_class_instance.set_list_db_products(event)\n    panel_lca_class_instance.set_methods_objects(event)\n    widget_autocomplete_product.options = panel_lca_class_instance.list_db_products\n    widget_select_method.options = panel_lca_class_instance.list_db_methods\n    widget_select_method.value = [item for item in panel_lca_class_instance.list_db_methods if 'GCC' in item[0]][0] # global warming as default value\n\n\ndef button_action_perform_lca(event):\n    if widget_autocomplete_product.value == '':\n        pn.state.notifications.error('Please select a reference product first!', duration=5000)\n        return\n    else:\n        panel_lca_class_instance.df_graph_traversal_nodes = pd.DataFrame()\n        widget_plotly_figure_piechart.object = create_plotly_figure_piechart({'null':0})\n        pn.state.notifications.info('Calculating LCA score...', duration=5000)\n        pass\n    panel_lca_class_instance.set_chosen_activity(event)\n    panel_lca_class_instance.set_chosen_method_and_unit(event)\n    panel_lca_class_instance.set_chosen_amount(event)\n    panel_lca_class_instance.perform_lca(event)\n    pn.state.notifications.success('Completed LCA score calculation!', duration=5000)\n    widget_number_lca_score.format = f'{{value:,.3f}} {panel_lca_class_instance.chosen_method_unit}'\n    perform_graph_traversal(event)\n    perform_scope_analysis(event)\n\n\ndef perform_graph_traversal(event):\n    pn.state.notifications.info('Performing Graph Traversal...', duration=5000)\n    panel_lca_class_instance.bool_user_provided_data = False\n    panel_lca_class_instance.set_graph_traversal_cutoff(event)\n    panel_lca_class_instance.perform_graph_traversal(event)\n    panel_lca_class_instance.df_tabulator = panel_lca_class_instance.df_tabulator_from_traversal.copy()\n    widget_tabulator.value = panel_lca_class_instance.df_tabulator\n    column_editors = {\n        colname: None\n        for colname in panel_lca_class_instance.df_tabulator.columns\n        if colname not in ['Scope', 'SupplyAmount', 'BurdenIntensity']\n    }\n    column_editors['Scope'] = {'type': 'list', 'values': [1, 2, 3]}\n    widget_tabulator.editors = column_editors\n    pn.state.notifications.success('Graph Traversal Complete!', duration=5000)\n\n\n\ndef perform_scope_analysis(event):\n    pn.state.notifications.info('Performing Scope Analysis...', duration=5000)\n    panel_lca_class_instance.scope_dict = determine_scope_emissions(df=widget_tabulator.value)\n    widget_plotly_figure_piechart.object = create_plotly_figure_piechart(panel_lca_class_instance.scope_dict)\n    filename_download.value = generate_table_filename()\n    widget_number_lca_score.value = panel_lca_class_instance.df_tabulator['Burden(Direct)'].sum()\n    pn.state.notifications.success('Scope Analysis Complete!', duration=5000)\n\n\ndef update_data_based_on_user_input(event):\n    pn.state.notifications.info('Updating Supply Chain based on User Input...', duration=5000)\n    panel_lca_class_instance.df_tabulator_from_user = create_user_input_columns(\n        df_original=panel_lca_class_instance.df_tabulator_from_traversal,\n        df_user_input=panel_lca_class_instance.df_tabulator_from_user,\n    )\n    panel_lca_class_instance.df_tabulator_from_user = determine_edited_rows(df=panel_lca_class_instance.df_tabulator_from_user)\n    panel_lca_class_instance.df_tabulator_from_user = update_production_based_on_user_data(df=panel_lca_class_instance.df_tabulator_from_user)\n    panel_lca_class_instance.df_tabulator_from_user = update_burden_intensity_based_on_user_data(df=panel_lca_class_instance.df_tabulator_from_user)\n    panel_lca_class_instance.df_tabulator_from_user = update_burden_based_on_user_data(panel_lca_class_instance.df_tabulator_from_user)\n    panel_lca_class_instance.df_tabulator = panel_lca_class_instance.df_tabulator_from_user.copy()\n    widget_tabulator.value = panel_lca_class_instance.df_tabulator\n    pn.state.notifications.success('Completed Updating Supply Chain based on User Input!', duration=5000)\n\ndef button_action_scope_analysis(event):\n    if panel_lca_class_instance.lca is None:\n        pn.state.notifications.error('Please perform an LCA Calculation first!', duration=5000)\n        return\n    else:\n        # if the user has not yet performed graph traversal, or changed the cutoff value,\n        # then perform graph traversal and scope analysis\n        if (\n            panel_lca_class_instance.df_graph_traversal_nodes.empty or\n            widget_float_slider_cutoff.value / 100 != panel_lca_class_instance.graph_traversal_cutoff\n        ):\n            perform_graph_traversal(event)\n            perform_scope_analysis(event)\n        # if the user has already provided input data, we reset the dataframe\n        elif panel_lca_class_instance.bool_user_provided_data == True:\n            pn.state.notifications.error('You are not allowed to edit a table which has already been re-computed on your input! Resetting the table...', duration=5000)\n            perform_graph_traversal(event)\n            perform_scope_analysis(event)\n        # if the user has overriden either supply or burden intensity values in the table,\n        # then update upstream values based on user input\n        elif (\n            (panel_lca_class_instance.df_tabulator['SupplyAmount'] != panel_lca_class_instance.df_tabulator_from_traversal['SupplyAmount']).any() or \n            (panel_lca_class_instance.df_tabulator['BurdenIntensity'] != panel_lca_class_instance.df_tabulator_from_traversal['BurdenIntensity']).any()\n        ):\n            panel_lca_class_instance.bool_user_provided_data = True\n            panel_lca_class_instance.df_tabulator_from_user = panel_lca_class_instance.df_tabulator.copy()\n            update_data_based_on_user_input(event)\n            perform_scope_analysis(event)\n        elif (\n            (panel_lca_class_instance.df_tabulator['Scope'] != panel_lca_class_instance.df_tabulator_from_traversal['Scope']).any()\n        ):\n            perform_scope_analysis(event)\n\n\nwidget_button_load_db = pn.widgets.Button( \n    name='Load USEEIO Database',\n    icon='database-plus',\n    button_type='primary',\n    sizing_mode='stretch_width'\n)\nwidget_button_load_db.on_click(button_action_load_database)\n\nwidget_autocomplete_product = pn.widgets.AutocompleteInput( \n    name='Reference Product/Product/Service',\n    options=[],\n    case_sensitive=False,\n    search_strategy='includes',\n    placeholder='Start typing your product name here...',\n    sizing_mode='stretch_width'\n)\n\nmarkdown_method_documentation = pn.pane.Markdown("""\nThe impact assessment methods are documented [in Table 3](https://www.nature.com/articles/s41597-022-01293-7/tables/4) of the [USEEIO release article](https://doi.org/10.1038/s41597-022-01293-7).\n""")\n\nwidget_select_method = pn.widgets.Select( \n    name='Impact Assessment Method',\n    options=[],\n    sizing_mode='stretch_width',\n\n)\n\nwidget_float_input_amount = pn.widgets.FloatInput( \n    name='(Monetary) Amount of Reference Product [USD]',\n    value=100,\n    step=1,\n    start=0,\n    sizing_mode='stretch_width'\n)\n\nwidget_button_lca = pn.widgets.Button( \n    name='Compute LCA Score',\n    icon='calculator',\n    button_type='primary',\n    sizing_mode='stretch_width'\n)\nwidget_button_lca.on_click(button_action_perform_lca)\n\nwidget_float_slider_cutoff = pn.widgets.EditableFloatSlider(\n    name='Graph Traversal Cut-Off [%]',\n    start=1,\n    end=50,\n    step=1,\n    value=10,\n    sizing_mode='stretch_width'\n)\n\nmarkdown_cutoff_documentation = pn.pane.Markdown("""\n[A cut-off of 10%](https://docs.brightway.dev/projects/graphtools/en/latest/content/api/bw_graph_tools/graph_traversal/new_node_each_visit/index.html) means that only those processes responsible or 90% of impact will be computed.\n""")\n        \nwidget_button_graph = pn.widgets.Button(\n    name='Update Data based on User Input',\n    icon='chart-donut-3',\n    button_type='primary',\n    sizing_mode='stretch_width'\n)\nwidget_button_graph.on_click(button_action_scope_analysis)\n\nwidget_number_lca_score = pn.indicators.Number(\n    name='LCA Impact Score',\n    font_size='30pt',\n    title_size='20pt',\n    value=0,\n    format='{value:,.3f}',\n    margin=0\n)\n\nwidget_plotly_figure_piechart = pn.pane.Plotly(\n    create_plotly_figure_piechart(\n        {'Scope 1': 0}\n    )\n)\n\ncol1 = pn.Column(\n    '# LCA Settings',\n    widget_button_load_db,\n    widget_autocomplete_product,\n    markdown_method_documentation,\n    widget_select_method,\n    widget_float_input_amount,\n    markdown_cutoff_documentation,\n    widget_float_slider_cutoff,\n    widget_button_lca,\n    widget_button_graph,\n    pn.Spacer(height=10),\n    widget_number_lca_score,\n    widget_plotly_figure_piechart,\n)\n\n# COLUMN 2 ####################################################################\n\n\ndef highlight_tabulator_cells(tabulator_row):\n    """\n    Applies a background color to all rows where the 'Edited?' column is True.\n\n    See Also\n    --------\n    - https://stackoverflow.com/a/48306463\n    - https://discourse.holoviz.org/t/dynamic-update-of-tabulator-style\n    """\n    if tabulator_row['Edited?'] == True:\n        return ['background-color: orange'] * len(tabulator_row)\n    else:\n        return [''] * len(tabulator_row)\n    \n\nwidget_tabulator = pn.widgets.Tabulator(\n    pd.DataFrame([['']], columns=['Data will appear here after calculations...']),\n    theme='site',\n    show_index=False,\n    hidden_columns=['activity_datapackage_id', 'producer_unique_id'],\n    layout='fit_data_stretch',\n    sizing_mode='stretch_width'\n)\nwidget_tabulator.style.apply(highlight_tabulator_cells, axis=1)\n\nfilename_download, button_download = widget_tabulator.download_menu(\n    text_kwargs={'name': 'Filename', 'value': 'filename.csv'},\n    button_kwargs={'name': 'Download Table'}\n)\nfilename_download.sizing_mode = 'stretch_width'\nbutton_download.align = 'center'\nbutton_download.icon = 'download'\n\nwidget_cutoff_indicator_statictext = pn.widgets.StaticText(\n    name='Includes processes responsible for amount of emissions [%]',\n    value=None\n)\n\ncol2 = pn.Column(\n    pn.Row('# Table of Upstream Processes', filename_download, button_download),\n    widget_tabulator\n)\n\n# SITE ######################################################################\n\ncode_open_window = """\nwindow.open("https://github.com/brightway-lca/brightway-webapp/blob/main/README.md")\n"""\nbutton_about = pn.widgets.Button(name="Learn more about this prototype...", button_type="success")\nbutton_about.js_on_click(code=code_open_window)\n\nheader = pn.Row(\n    button_about,\n    pn.HSpacer(),\n    pn.pane.SVG(\n        'https://raw.githubusercontent.com/brightway-lca/brightway-webapp/main/app/_media/logo_PSI-ETHZ-WISER_white.svg',\n        #height=50,\n        margin=0,\n        align="center"\n    ),\n    sizing_mode="stretch_width",\n)\n\ntemplate = pn.template.MaterialTemplate(\n    header=header,\n    title='Brightway WebApp (Carbon Accounting)',\n    header_background='#2d853a', # green\n    logo='https://raw.githubusercontent.com/brightway-lca/brightway-webapp/main/app/_media/logo_brightway_white.svg',\n    favicon='https://raw.githubusercontent.com/brightway-lca/brightway-webapp/main/app/_media/favicon.png',\n)\n\ngspec = pn.GridSpec(ncols=3, sizing_mode='stretch_both')\ngspec[:,0:1] = col1 # 1/3rd of the width\ngspec[:,1:3] = col2 # 2/3rds of the width\n\ntemplate.main.append(gspec)\ntemplate.servable()\n\nawait write_doc()
   `
 
   try {