Add screenshot of dashboard to readme (#612)

* Add dashboard screenshot

* Add link to dashboard on main readme

* Tweak text

* Add dashboard to readme

* Fix url

* Fix linting path

* Update url to main

.github/workflows/linting.yml

@@ -33,4 +33,4 @@ jobs:
 
       - name: Check code style
         run: |
-          ruff dianna dashboard tests
+          ruff dianna tests

README.md

@@ -154,6 +154,14 @@ dianna.visualization.plot_image(explanation[labels.index(class_a)], original_dat
 ```
 ![image](https://user-images.githubusercontent.com/6087314/155557077-e2052094-d8ac-49d3-a840-0160256d53a6.png)
 
+## Dashboard
+
+Explore your trained model explained using the DIANNA dashboard. [Click here](https://github.com/dianna-ai/dianna/tree/main/dianna/dashboard) for more information.
+
+<a href="https://github.com/dianna-ai/dianna/tree/main/dianna/dashboard" target="_blank">
+  <img width="300" align="center" alt="Dianna dashboard screenshot" src="https://raw.githubusercontent.com/dianna-ai/dianna/main/dianna/dashboard/dashboard-screenshot.png">
+</a>
+
 ## Datasets
 DIANNA comes with simple datasets. Their main goal is to provide intuitive insight into the working of the XAI methods. They can be used as benchmarks for evaluation and comparison of existing and new XAI methods.
 

dianna/dashboard/readme.md

@@ -1,7 +1,8 @@
 # DIANNA dashboard
 
+![Dianna dashboard screenshot](./dashboard-screenshot.png)
 
-A dashboard was created for DIANNA using [streamlit](https://streamlit.io/) that can be used for simple exploration of your trained model explained by DIANNA. The dashboard produces the visual explanation of your selected XAI method. Additionally it allows you to compare the results of different XAI methods, as well as explanations of the top ranked predicted labels.
+The DIANNA dashboard can be used for simple exploration of your trained model explained by DIANNA. The dashboard produces the visual explanation of your selected XAI method. Additionally it allows you to compare the results of different XAI methods, as well as explanations of the top ranked predicted labels. The dashboard was created using [streamlit](https://streamlit.io/).
 
 To open the dashboard, you can install dianna via `pip install -e .[dashboard]` and run:
 
@@ -17,7 +18,6 @@ streamlit run Home.py
 
 Open the link on which the app is running. Note that you are running the dashboard *only locally*. The data you use in the dashboard is your local data, and it is *not* uploaded to any server.
 
-
 ## How to use the dashboard
 
 The dashboard will automatically open in the welcome page tab. In the sidebar you can open the image or text pages.