diff --git a/README.md b/README.md
index 6f6b235..23f39a6 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,55 @@
# NPLinker Web Application
-This is the [NPLinker](https://nplinker.github.io/nplinker/latest/) web application (webapp), developed with [Plotly Dash](https://dash.plotly.com/). It enables interactive visualization of NPLinker predictions and is designed to be run locally or in a containerized environment. [A lightweight online demo](https://nplinker-webapp.onrender.com) is available for quick exploration.
+👉 **[Webapp Live Demo](https://nplinker-webapp.onrender.com)**
-
- 
-
+This is the [NPLinker](https://nplinker.github.io/nplinker/latest/) web application (webapp), developed with [Plotly Dash](https://dash.plotly.com/), which enables you to visualize NPLinker predictions in an interactive way.
-
- 
-
+NPLinker is a Python framework for data mining microbial natural products by integrating genomics and metabolomics data. For a deep understanding of NPLinker, please refer to the [original paper](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008920).
-
- 
-
+## Online Demo
+
+A live demo of the NPLinker webapp is automatically deployed to [Render](https://render.com/) from `main` branch.
+You can try out the webapp directly in your browser [here](https://nplinker-webapp.onrender.com).
+
+### Getting Started with Demo Data
+
+The webapp includes a convenient **"Load Demo Data"** button that automatically loads some sample data for you to try. Simply:
+1. Open the [live demo link](https://nplinker-webapp.onrender.com/)
+2. Click the **"Load Demo Data"** button below the file uploader
+3. The app will automatically download and process the sample dataset from [`tests/data/mock_obj_data.pkl`](https://github.com/NPLinker/nplinker-webapp/blob/main/tests/data/mock_obj_data.pkl)
+4. Start exploring natural product linking features!
-NPLinker is a Python framework for data mining microbial natural products by integrating genomics and metabolomics data.
+This demo web server is intended only for lightweight demo purposes. For full functionality, including large-scale data processing and persistent storage, please install the application locally or via Docker as described below.
-For a deep understanding of NPLinker, please refer to the [original paper](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008920).
+
+⚠️ Demo Server Limitations
+Please note the following limitations of the hosted demo:
+
+* **Cold start delay**: Free-tier apps on Render sleep after 15 minutes of inactivity and may take 20–30 seconds to wake up.
+* **Performance**: This is a minimal deployment on a free tier and is not optimized for large datasets or concurrent users.
+* **File size limits**: The demo data button loads a small sample dataset suitable for testing. Uploading large datasets via the file uploader may lead to errors or timeouts.
+* **No persistent storage**: Uploaded files are not saved between sessions.
+
+
+## Using the webapp
+
+### Input Data
+
+The webapp accepts data generated by NPLinker and saved as described in the [NPLinker quickstart section](https://nplinker.github.io/nplinker/latest/quickstart/). For testing purposes, a small sample dataset is provided in [`tests/data/mock_obj_data.pkl`](https://github.com/NPLinker/nplinker-webapp/blob/main/tests/data/mock_obj_data.pkl) that can be used to try out the webapp.
+
+Please note that links between genomic and metabolomic data must currently be computed using the NPLinker API separately, as this functionality is not yet implemented in the webapp (see [issue #19](https://github.com/NPLinker/nplinker-webapp/issues/19)). If no links are present in your data, the scoring table will be disabled.
-## Prerequisites
+## Installation
Before installing NPLinker webapp, ensure you have:
-- [Python 3.10](https://www.python.org/downloads/)
+- [Python ≥3.10](https://www.python.org/downloads/)
- [Git](https://git-scm.com/downloads)
+- [Conda](https://docs.conda.io/en/latest/miniconda.html)
-## Installation Options
+You can install and run the NPLinker webapp in two ways: directly on your local machine or using Docker.
-You can install and run the NPLinker dashboard in two ways: directly on your local machine or using Docker.
-
-### Option 1: Local Installation
+### Option 1: Local Installation (using Conda)
Follow these steps to install the application directly on your system:
@@ -39,16 +59,13 @@ Follow these steps to install the application directly on your system:
cd nplinker-webapp
```
-2. **Set up a virtual environment**
+2. **Set up a conda environment**
```bash
- # Create a virtual environment
- python3.10 -m venv venv
-
- # Activate the virtual environment
- # For Windows:
- venv\Scripts\activate
- # For macOS/Linux:
- source venv/bin/activate
+ # Create a new conda environment with Python 3.10
+ conda create -n nplinker-webapp python=3.10
+
+ # Activate the environment
+ conda activate nplinker-webapp
```
3. **Install dependencies**
@@ -61,19 +78,22 @@ Follow these steps to install the application directly on your system:
python app/main.py
```
-5. **Access the dashboard**
+5. **Access the webapp**
Open your web browser and navigate to `http://0.0.0.0:8050/`
-#### Troubleshooting Local Installation
+
+Troubleshooting Local Installation
-Common issues and solutions:
+#### Common issues and solutions
- **Port already in use**: If port 8050 is already in use, modify the port in `app/main.py` by changing `app.run_server(debug=True, port=8050)`
- **Package installation errors**: Make sure you're using Python 3.10 and that your pip is up-to-date
If you encounter other problems, please check the [Issues](https://github.com/NPLinker/nplinker-webapp/issues) page or create a new issue.
+
+
### Option 2: Docker Installation
Using Docker is the quickest way to get started with NPLinker webapp. Make sure you have [Docker](https://www.docker.com/) installed on your system before proceeding:
@@ -88,11 +108,12 @@ Using Docker is the quickest way to get started with NPLinker webapp. Make sure
docker run -p 8050:8050 ghcr.io/nplinker/nplinker-webapp:latest
```
-3. **Access the dashboard**
+3. **Access the webapp**
Open your web browser and navigate to `http://0.0.0.0:8050/`
-#### Docker Image Information
+
+Docker Image Information
- **Available Tags**:
- `latest`: The most recent build
@@ -102,32 +123,24 @@ Using Docker is the quickest way to get started with NPLinker webapp. Make sure
- **More Details**: For additional information about the Docker image, see its [GitHub Container Registry page](https://github.com/NPLinker/nplinker-webapp/pkgs/container/nplinker-webapp).
-## Online Demo
+
-A live demo of the NPLinker webapp is automatically deployed to [Render](https://render.com/) and updated every time the `main` branch is updated.
+### Filtering Table Data
-You can try out the dashboard directly in your browser here:
+The "Candidate Links" tables support data filtering to help you focus on relevant results. You can enter filter criteria directly into each column’s filter cell by hovering over the cell.
-👉 **[Live Demo](https://nplinker-webapp.onrender.com)**
+For numeric columns like "Average Score" or "# Links":
+- `34.6` or `= 34.6` (exact match)
+- `> 30` (greater than)
+- `<= 50` (less than or equal to)
-### ⚠️ Demo Limitations
+For text columns like "BGC Classes" or "MiBIG IDs":
+- `Polyketide` or `contains Polyketide` (contains text)
+- `= Polyketide` (exact match)
-Please note the following limitations of the hosted demo:
+Multiple filters can be applied simultaneously across different columns to narrow down results.
-* **Cold start delay**: Free-tier apps on Render sleep after 15 minutes of inactivity and may take 20–30 seconds to wake up.
-* **Performance**: This is a minimal deployment on a free tier and is not optimized for large datasets or concurrent users.
-* **File size limits**: Use the small sample data provided in [`tests/data/mock_obj_data.pkl`](https://github.com/NPLinker/nplinker-webapp/blob/main/tests/data/mock_obj_data.pkl) for testing. Uploading large datasets may lead to errors or timeouts.
-* **No persistent storage**: Uploaded files are not saved between sessions.
-
-This hosted version is intended only for lightweight demo purposes. For full functionality, including large-scale data processing and persistent storage, please install the application locally or via Docker as described above.
-
-## Using the Dashboard
-
-### Input Data
-
-The dashboard accepts data generated by NPLinker and saved as described in the [NPLinker quickstart section](https://nplinker.github.io/nplinker/latest/quickstart/). For testing purposes, a small sample dataset is provided in [`tests/data/mock_obj_data.pkl`](https://github.com/NPLinker/nplinker-webapp/blob/main/tests/data/mock_obj_data.pkl) that can be used to try out the webapp.
-
-Please note that links between genomic and metabolomic data must currently be computed using the NPLinker API separately, as this functionality is not yet implemented in the webapp (see [issue #19](https://github.com/NPLinker/nplinker-webapp/issues/19)). If no links are present in your data, the scoring table will be disabled.
+For a full list of supported filter operators, see the [official Plotly documentation](https://dash.plotly.com/datatable/filtering#filtering-operators).
## Contributing
diff --git a/app/callbacks.py b/app/callbacks.py
index 9686c57..d955d67 100644
--- a/app/callbacks.py
+++ b/app/callbacks.py
@@ -12,6 +12,7 @@
import dash_uploader as du
import pandas as pd
import plotly.graph_objects as go
+import requests
from dash import ALL
from dash import MATCH
from dash import Dash
@@ -19,7 +20,6 @@
from dash import Output
from dash import State
from dash import callback_context as ctx
-from dash import clientside_callback
from dash import dcc
from dash import html
from app.config import GM_FILTER_DROPDOWN_BGC_CLASS_OPTIONS_PRE_V4
@@ -47,15 +47,8 @@
TEMP_DIR = tempfile.mkdtemp()
du.configure_upload(app, TEMP_DIR)
-clientside_callback(
- """
- (switchOn) => {
- document.documentElement.setAttribute('data-bs-theme', switchOn ? 'light' : 'dark');
- return window.dash_clientside.no_update
- }
- """,
- Output("color-mode-switch", "id"),
- Input("color-mode-switch", "value"),
+DEMO_DATA_URL = (
+ "https://github.com/NPLinker/nplinker-webapp/blob/main/tests/data/mock_obj_data.pkl?raw=true"
)
@@ -95,6 +88,55 @@ def upload_data(status: du.UploadStatus) -> tuple[str, str | None, None]:
return "No file uploaded", None, None
+@app.callback(
+ Output("dash-uploader-output", "children", allow_duplicate=True),
+ Output("file-store", "data", allow_duplicate=True),
+ Output("loading-spinner-container", "children", allow_duplicate=True),
+ Input("demo-data-button", "n_clicks"),
+ prevent_initial_call=True,
+)
+def load_demo_data(n_clicks):
+ """Load demo data from GitHub repository.
+
+ Args:
+ n_clicks: Number of times the demo data button has been clicked.
+
+ Returns:
+ A tuple containing a message string and the file path (if successful).
+ """
+ if n_clicks is None:
+ raise dash.exceptions.PreventUpdate
+
+ try:
+ # Download the demo data
+ response = requests.get(DEMO_DATA_URL, timeout=30)
+ response.raise_for_status()
+
+ # Save to temporary file
+ demo_file_path = os.path.join(TEMP_DIR, f"demo_data_{uuid.uuid4()}.pkl")
+ with open(demo_file_path, "wb") as f:
+ f.write(response.content)
+
+ # Validate the pickle file
+ with open(demo_file_path, "rb") as f:
+ pickle.load(f)
+
+ file_size_mb = len(response.content) / (1024 * 1024)
+
+ return (
+ f"Successfully loaded demo data: demo_data.pkl [{round(file_size_mb, 2)} MB]",
+ str(demo_file_path),
+ None,
+ )
+
+ except requests.exceptions.RequestException as e:
+ return f"Error downloading demo data: Network error - {str(e)}", None, None
+ except (pickle.UnpicklingError, EOFError, AttributeError):
+ return "Error: Downloaded file is not a valid pickle file.", None, None
+ except Exception as e:
+ return f"Error loading demo data: {str(e)}", None, None
+
+
@app.callback(
Output("processed-data-store", "data"),
Output("processed-links-store", "data"),
@@ -103,12 +145,13 @@ def upload_data(status: du.UploadStatus) -> tuple[str, str | None, None]:
prevent_initial_call=True,
)
def process_uploaded_data(
- file_path: Path | str | None,
+ file_path: Path | str | None, cleanup: bool = True
) -> tuple[str | None, str | None, str | None]:
"""Process the uploaded pickle file and store the processed data.
Args:
file_path: Path to the uploaded pickle file.
+ cleanup: Flag to indicate whether to clean up the file after processing.
Returns:
JSON string of processed data or None if processing fails.
@@ -128,7 +171,12 @@ def process_bgc_class(bgc_class: tuple[str, ...] | None) -> list[str]:
return ["Unknown"]
return list(bgc_class) # Convert tuple to list
- processed_data: dict[str, Any] = {"n_bgcs": {}, "gcf_data": [], "mf_data": []}
+ processed_data: dict[str, Any] = {
+ "gcf_data": [],
+ "n_bgcs": {},
+ "class_bgcs": {},
+ "mf_data": [],
+ }
for gcf in gcfs:
sorted_bgcs = sorted(gcf.bgcs, key=lambda bgc: bgc.id)
@@ -149,6 +197,12 @@ def process_bgc_class(bgc_class: tuple[str, ...] | None) -> list[str]:
processed_data["n_bgcs"][len(gcf.bgcs)] = []
processed_data["n_bgcs"][len(gcf.bgcs)].append(gcf.id)
+ for bgc_class_list in bgc_classes:
+ for bgc_class in bgc_class_list:
+ if bgc_class not in processed_data["class_bgcs"]:
+ processed_data["class_bgcs"][bgc_class] = []
+ processed_data["class_bgcs"][bgc_class].append(gcf.id)
+
for mf in mfs:
sorted_spectra = sorted(mf.spectra, key=lambda spectrum: spectrum.id)
processed_data["mf_data"].append(
@@ -246,6 +300,12 @@ def process_mg_link(mf, gcf, methods_data):
except Exception as e:
print(f"Error processing file: {str(e)}")
return None, None, None
+ finally:
+ try:
+ if cleanup and file_path and os.path.exists(file_path):
+ os.remove(file_path)
+ except Exception as e:
+ print(f"Cleanup failed for {file_path}: {e}")
@app.callback(
@@ -266,7 +326,7 @@ def process_mg_link(mf, gcf, methods_data):
Output("gm-filter-accordion-component", "value", allow_duplicate=True),
Output("gm-scoring-accordion-component", "value", allow_duplicate=True),
Output("gm-results-table-column-toggle", "value", allow_duplicate=True),
- # MG tab outputs
+ Output("gm-graph-x-axis-selector", "value"),
Output("mg-tab", "disabled"),
Output("mg-filter-accordion-control", "disabled"),
Output("mg-filter-blocks-id", "data", allow_duplicate=True),
@@ -326,6 +386,7 @@ def disable_tabs_and_reset_blocks(
[],
[],
default_gm_column_value,
+ "n_bgcs",
# MG tab - disabled
True,
True,
@@ -374,6 +435,7 @@ def disable_tabs_and_reset_blocks(
[],
[],
default_gm_column_value,
+ "n_bgcs",
# MG tab - enabled with initial blocks
False,
False,
@@ -397,50 +459,116 @@ def disable_tabs_and_reset_blocks(
@app.callback(
Output("gm-graph", "figure"),
Output("gm-graph", "style"),
- [Input("processed-data-store", "data")],
+ Output("gm-graph-selector-container", "style"),
+ [Input("processed-data-store", "data"), Input("gm-graph-x-axis-selector", "value")],
)
-def gm_plot(stored_data: str | None) -> tuple[dict | go.Figure, dict]:
+def gm_plot(stored_data: str | None, x_axis_selection: str) -> tuple[dict | go.Figure, dict, dict]:
"""Create a bar plot based on the processed data.
Args:
stored_data: JSON string of processed data or None.
+ x_axis_selection: Selected x-axis type ('n_bgcs' or 'class_bgcs').
Returns:
- Tuple containing the plot figure, style, and a status message.
+ Tuple containing the plot figure, style for graph, and style for selector.
"""
if stored_data is None:
- return {}, {"display": "none"}
- data = json.loads(stored_data)
- n_bgcs = data["n_bgcs"]
+ return {}, {"display": "none"}, {"display": "none"}
- x_values = sorted(map(int, n_bgcs.keys()))
- y_values = [len(n_bgcs[str(x)]) for x in x_values]
- hover_texts = [
- f"GCF IDs: {', '.join(str(gcf_id) for gcf_id in n_bgcs[str(x)])}" for x in x_values
- ]
+ data = json.loads(stored_data)
- # Adjust bar width based on number of data points
- bar_width = 0.4 if len(x_values) <= 5 else None
- # Create the bar plot
- fig = go.Figure(
- data=[
- go.Bar(
- x=x_values,
- y=y_values,
- text=hover_texts,
- hoverinfo="text",
- textposition="none",
- width=bar_width,
- )
+ if x_axis_selection == "n_bgcs":
+ n_bgcs = data["n_bgcs"]
+ x_values = sorted(map(int, n_bgcs.keys()))
+ y_values = [len(n_bgcs[str(x)]) for x in x_values]
+ hover_texts = [
+ f"GCF IDs: {', '.join(str(gcf_id) for gcf_id in n_bgcs[str(x)])}" for x in x_values
]
- )
- # Update layout
- fig.update_layout(
- xaxis_title="# BGCs",
- yaxis_title="# GCFs",
- xaxis=dict(type="category"),
- )
- return fig, {"display": "block"}
+
+ # Adjust bar width based on number of data points
+ bar_width = 0.4 if len(x_values) <= 5 else None
+ # Create the bar plot
+ fig = go.Figure(
+ data=[
+ go.Bar(
+ x=x_values,
+ y=y_values,
+ text=hover_texts,
+ hoverinfo="text",
+ textposition="none",
+ width=bar_width,
+ )
+ ]
+ )
+ # Update layout
+ fig.update_layout(
+ xaxis_title="# BGCs",
+ yaxis_title="# GCFs",
+ xaxis=dict(type="category"),
+ )
+
+ else: # x_axis_selection == "class_bgcs"
+ class_bgcs = data["class_bgcs"]
+
+ # Count unique GCF IDs for each class
+ class_gcf_counts = {}
+ for bgc_class, gcf_ids in class_bgcs.items():
+ # Count unique GCF IDs
+ class_gcf_counts[bgc_class] = len(set(gcf_ids))
+
+ # Sort classes by count for better visualization
+ sorted_classes = sorted(class_gcf_counts.items(), key=lambda x: x[1], reverse=True)
+ x_values = [item[0] for item in sorted_classes]
+ y_values = [item[1] for item in sorted_classes]
+
+ # Generate hover texts with line breaks for better readability
+ hover_texts = []
+ for bgc_class in x_values:
+ # Get unique GCF IDs for this class
+ unique_gcf_ids = sorted(list(set(class_bgcs[bgc_class])))
+
+ # Format GCF IDs with line breaks every 10 items
+ formatted_gcf_ids = ""
+ for i, gcf_id in enumerate(unique_gcf_ids):
+ formatted_gcf_ids += gcf_id
+ # Add comma if not the last item
+ if i < len(unique_gcf_ids) - 1:
+ formatted_gcf_ids += ", "
+ # Add line break after every 10 items (but not for the last group)
+ if (i + 1) % 10 == 0 and i < len(unique_gcf_ids) - 1:
+ formatted_gcf_ids += "
"
+
+ hover_text = f"Class: {bgc_class}
GCF IDs: {formatted_gcf_ids}"
+ hover_texts.append(hover_text)
+
+ # Adjust bar width based on number of data points
+ bar_width = 0.4 if len(x_values) <= 5 else None
+ # Create the bar plot
+ fig = go.Figure(
+ data=[
+ go.Bar(
+ x=x_values,
+ y=y_values,
+ text=hover_texts,
+ hoverinfo="text",
+ textposition="none",
+ width=bar_width,
+ )
+ ]
+ )
+
+ # Update layout
+ fig.update_layout(
+ xaxis_title="BGC Classes",
+ yaxis_title="# GCFs",
+ xaxis=dict(
+ type="category",
+ # Add more space for longer class names
+ tickangle=-45 if len(x_values) > 5 else 0,
+ ),
+ )
+
+ return fig, {"display": "block"}, {"display": "block"}
# ------------------ Common Filter and Table Functions ------------------ #
@@ -1451,9 +1579,9 @@ def scoring_create_initial_block(block_id: str, tab_prefix: str = "gm") -> dmc.G
"type": f"{tab_prefix}-scoring-dropdown-ids-cutoff-met",
"index": block_id,
},
- label="Cutoff",
- placeholder="Insert cutoff value as a number",
- value="0.05",
+ label="Scoring method's cutoff (>=)",
+ placeholder="Insert the minimum cutoff value to be considered",
+ value="0",
className="custom-textinput",
)
],
@@ -1571,9 +1699,9 @@ def scoring_display_blocks(
"type": f"{tab_prefix}-scoring-dropdown-ids-cutoff-met",
"index": new_block_id,
},
- label="Cutoff",
- placeholder="Insert cutoff value as a number",
- value="0.05",
+ label="Scoring method's cutoff (>=)",
+ placeholder="Insert the minimum cutoff value to be considered",
+ value="0",
className="custom-textinput",
),
],
@@ -1615,7 +1743,7 @@ def scoring_update_placeholder(
# Callback was not triggered by user interaction, don't change anything
raise dash.exceptions.PreventUpdate
if selected_value == "METCALF":
- return ({"display": "block"}, "Cutoff", "0.05")
+ return ({"display": "block"}, "Cutoff", "0")
else:
# This case should never occur due to the Literal type, but it satisfies mypy
return ({"display": "none"}, "", "")
diff --git a/app/layouts.py b/app/layouts.py
index 290d47c..280f338 100644
--- a/app/layouts.py
+++ b/app/layouts.py
@@ -103,10 +103,14 @@ def create_results_table(table_id, no_sort_columns):
columns=[],
data=[],
editable=False,
- filter_action="none",
+ filter_action="native",
+ filter_options={"placeholder_text": " filter data..."},
+ style_filter={
+ "backgroundColor": "#f8f9fa",
+ },
sort_action="native",
virtualization=True,
- fixed_rows={"headers": True}, # Keep headers visible when scrolling
+ fixed_rows={"headers": True},
sort_mode="single",
sort_as_null=["None", ""],
sort_by=[],
@@ -158,7 +162,25 @@ def create_results_table(table_id, no_sort_columns):
border: 1px solid #FF6E42;
box-shadow: 2px 2px 5px rgba(0, 0, 0, 0.1);
""",
- }
+ },
+ {
+ "selector": ".dash-filter input::placeholder",
+ "rule": "opacity: 1 !important; text-align: left !important;",
+ },
+ {
+ "selector": ".dash-filter input",
+ "rule": "text-align: left !important; width: 100% !important;",
+ },
+ # Hide the filter type indicators completely
+ {
+ "selector": ".dash-filter--case",
+ "rule": "display: none !important;",
+ },
+ # Adjust padding to fill the space where indicators were
+ {
+ "selector": ".dash-filter",
+ "rule": "padding-left: 0 !important;",
+ },
]
+ [
{
@@ -553,10 +575,41 @@ def create_tab_content(prefix, filter_title, checkl_options, no_sort_columns):
# Add graph component only for GM tab
components = []
if prefix == "gm":
- graph = dcc.Graph(id="gm-graph", className="mt-5 mb-3", style={"display": "none"})
+ # Add x-axis selector dropdown above the graph
+ graph_with_selector = html.Div(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ html.Div(
+ [
+ html.Label("Select X-axis: ", className="me-2"),
+ dcc.Dropdown(
+ id="gm-graph-x-axis-selector",
+ options=[
+ {"label": "# BGCs", "value": "n_bgcs"},
+ {"label": "BGC Classes", "value": "class_bgcs"},
+ ],
+ value="n_bgcs", # Default value
+ clearable=False,
+ style={"width": "200px"},
+ ),
+ ],
+ className="d-flex align-items-center",
+ ),
+ width=12,
+ )
+ ],
+ id="gm-graph-selector-container",
+ ),
+ dcc.Graph(id="gm-graph"),
+ ],
+ className="mt-5 mb-3",
+ )
+
components = [
dbc.Col(filter_accordion, width=10, className="mx-auto dbc"),
- dbc.Col(graph, width=10, className="mx-auto"),
+ dbc.Col(graph_with_selector, width=10, className="mx-auto dbc"),
dbc.Col(data_table, width=10, className="mx-auto"),
dbc.Col(scoring_accordion, width=10, className="mx-auto dbc"),
]
@@ -575,19 +628,6 @@ def create_tab_content(prefix, filter_title, checkl_options, no_sort_columns):
# ------------------ Nav Bar ------------------ #
-color_mode_switch = html.Span(
- [
- dbc.Label(className="fa fa-moon", html_for="color-mode-switch"),
- dbc.Switch(
- id="color-mode-switch",
- value=False,
- className="d-inline-block ms-1",
- persistence=True,
- ),
- dbc.Label(className="fa fa-sun", html_for="color-mode-switch"),
- ],
- className="p-2",
-)
navbar = dbc.Row(
dbc.Col(
dbc.NavbarSimple(
@@ -596,10 +636,6 @@ def create_tab_content(prefix, filter_title, checkl_options, no_sort_columns):
dbc.NavItem(
dbc.NavLink("About", href="https://github.com/NPLinker/nplinker-webapp"),
),
- dbc.NavItem(
- color_mode_switch,
- className="mt-1 p-1",
- ),
],
brand="NPLinker Webapp",
color="primary",
@@ -632,6 +668,21 @@ def create_tab_content(prefix, filter_title, checkl_options, no_sort_columns):
className="d-flex justify-content-center",
)
),
+ # Demo data button
+ dbc.Row(
+ dbc.Col(
+ html.Div(
+ dbc.Button(
+ "Load Demo Data",
+ id="demo-data-button",
+ color="primary",
+ className="mt-3",
+ ),
+ className="d-flex justify-content-center",
+ ),
+ className="d-flex justify-content-center",
+ )
+ ),
dcc.Store(id="file-store"), # Store to keep the file contents
dcc.Store(id="processed-data-store"), # Store to keep the processed data
dcc.Store(id="processed-links-store"), # Store to keep the processed links
diff --git a/mypy.ini b/mypy.ini
index 202ac4e..227993a 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -2,4 +2,5 @@
python_version = 3.10
warn_return_any = true
warn_unused_configs = true
-ignore_missing_imports = true
\ No newline at end of file
+ignore_missing_imports = true
+disable_error_code = import-untyped
\ No newline at end of file
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
index 595fc9c..536d57c 100644
--- a/tests/test_callbacks.py
+++ b/tests/test_callbacks.py
@@ -1,4 +1,5 @@
import json
+import pickle
import uuid
from pathlib import Path
from unittest.mock import patch
@@ -15,6 +16,7 @@
from app.callbacks import gm_table_toggle_selection
from app.callbacks import gm_table_update_datatable
from app.callbacks import gm_toggle_download_button
+from app.callbacks import load_demo_data
from app.callbacks import mg_filter_add_block
from app.callbacks import mg_filter_apply
from app.callbacks import mg_generate_excel
@@ -44,7 +46,7 @@ def mock_uuid4():
@pytest.fixture
def processed_data():
# Use the actual process_uploaded_data function to get the processed data
- return process_uploaded_data(MOCK_FILE_PATH)
+ return process_uploaded_data(MOCK_FILE_PATH, cleanup=False)
@pytest.fixture
@@ -104,17 +106,46 @@ def test_upload_data():
assert path_string == str(MOCK_FILE_PATH)
+def test_load_demo_data():
+ """Test the load_demo_data callback function."""
+
+ # Test with no clicks - should prevent update
+ with pytest.raises(dash.exceptions.PreventUpdate):
+ load_demo_data(None)
+
+ # Test with actual click - should load demo data
+ result = load_demo_data(1)
+ message, file_path, spinner = result
+
+ # Check that the function returns expected format
+ assert isinstance(message, str)
+ assert isinstance(file_path, (str, type(None)))
+ assert spinner is None
+
+ # If successful, should contain success message and valid file path
+ if file_path is not None:
+ assert "Successfully loaded demo data" in message
+ assert "demo_data_" in file_path
+ # Verify the file actually exists and is valid
+ with open(file_path, "rb") as f:
+ data = pickle.load(f)
+ assert data is not None
+ else:
+ # If failed, should contain error message
+ assert "Error" in message
+
+
@pytest.mark.parametrize("input_path", [None, Path("non_existent_file.pkl")])
def test_process_uploaded_data_invalid_input(input_path):
- processed_data, processed_links, _ = process_uploaded_data(input_path)
+ processed_data, processed_links, _ = process_uploaded_data(input_path, cleanup=False)
assert processed_data is None
assert processed_links is None
def test_process_uploaded_data_structure():
- processed_data, processed_links, _ = process_uploaded_data(MOCK_FILE_PATH)
+ processed_data, processed_links, _ = process_uploaded_data(MOCK_FILE_PATH, cleanup=False)
processed_data_no_links, processed_links_no_links, _ = process_uploaded_data(
- MOCK_FILE_PATH_NO_LINKS
+ MOCK_FILE_PATH_NO_LINKS, cleanup=False
)
assert processed_data is not None
@@ -242,6 +273,25 @@ def test_process_uploaded_data_structure():
assert isinstance(gcf["BGC Classes"], list)
+def test_process_uploaded_data_cleanup(tmp_path):
+ """Ensure that temporary file is deleted when cleanup=True."""
+ temp_file = tmp_path / "temp_data.pkl"
+
+ dummy_data = (None, [], None, [], None, None)
+ with open(temp_file, "wb") as f:
+ pickle.dump(dummy_data, f)
+
+ # Confirm file exists
+ assert temp_file.exists()
+
+ # Call the function with cleanup=True (default)
+ processed_data, _, _ = process_uploaded_data(temp_file, cleanup=True)
+
+ # File should be deleted after processing
+ assert not temp_file.exists()
+ assert processed_data is not None # Sanity check: function still processed the file
+
+
def test_disable_tabs(mock_uuid):
default_gm_column_value = (
[GM_RESULTS_TABLE_CHECKL_OPTIONAL_COLUMNS[0]]
@@ -273,6 +323,7 @@ def test_disable_tabs(mock_uuid):
[],
[],
default_gm_column_value,
+ "n_bgcs",
# MG tab - disabled
True,
True,
@@ -312,6 +363,7 @@ def test_disable_tabs(mock_uuid):
gm_filter_accordion_value,
gm_scoring_accordion_value,
gm_results_table_column_toggle,
+ gm_graph_dropdown,
# MG tab outputs
mg_tab_disabled,
mg_filter_accordion_disabled,
@@ -348,6 +400,7 @@ def test_disable_tabs(mock_uuid):
assert gm_filter_accordion_value == []
assert gm_scoring_accordion_value == []
assert gm_results_table_column_toggle == default_gm_column_value
+ assert gm_graph_dropdown == "n_bgcs"
# Assert MG tab outputs
assert mg_tab_disabled is False