Merge pull request #866 from dianna-ai/865-Updating-READMEs-issues-81…

…5-863

Updating READMEs (issues 815 and 863)

dianna/dashboard/Home.py

@@ -1,5 +1,6 @@
 import importlib
 import streamlit as st
+from _shared import add_sidebar_goddess_logo
 from _shared import data_directory
 from streamlit_option_menu import option_menu
 
@@ -38,7 +39,7 @@
 
 # Display the content of the selected page
 if selected == "Home":
-
+    add_sidebar_goddess_logo()
     _, col, _ = st.columns([1, 3, 1])
     with col:
         st.markdown("""#""")
@@ -90,10 +91,8 @@
         You can then select the explainer you want to use and set its hyperparameters.
 
         ### More information
-
-        - [Source code](https://github.com/dianna-ai/dianna)
+        - [Research Software directory page](https://research-software-directory.org/software/dianna)
         - [Documentation](https://dianna.readthedocs.io/)
-        - [XAI choice](https://blog.esciencecenter.nl/how-to-find-your-artificial-intelligence-explainer-dbb1ac608009)
         """,
                     unsafe_allow_html=True)
 

dianna/dashboard/_shared.py

@@ -47,6 +47,9 @@ def add_sidebar_logo():
     """Upload DIANNA logo to sidebar element."""
     st.sidebar.image(str(data_directory / 'logo.png'))
 
+def add_sidebar_goddess_logo():
+    """Upload DIANNA goddess logo to sidebar element."""
+    st.sidebar.image(str(data_directory / 'Diana_goddess.png'))
 
 def _methods_checkboxes(*, choices: Sequence, key):
     """Get methods from a horizontal row of checkboxes and the corresponding parameters."""

dianna/dashboard/readme.md

@@ -1,9 +1,7 @@
 # DIANNA dashboard
 
-<!-- ![Dianna dashboard screenshot](./dashboard-screenshot.png) -->  
-_Dianna dashboard screenshot here_
-
-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/).
+The DIANNA dashboard (powered by [streamlit](https://streamlit.io/) <img width="25" alt="Streamlit Logo" src="https://github.com/user-attachments/assets/2cac7d5d-c11a-48fe-b58e-71b15baaa163">) can be used for explanation of ONNX models trained for the tasks and datasets presented in several (marked with <img width="25" alt="Streamlit Logo" src="https://github.com/user-attachments/assets/2cac7d5d-c11a-48fe-b58e-71b15baaa163">) of the DIANNA [tutorials](../../tutorials/README.md). 
+The dashboard shows the visual explanation of a models' decision on a selected data item by a selected XAI method (explainer). Results of different explainers can be compared, as well as explanations of the top predicted labels. 
 
 To open the dashboard, you can install dianna via `pip install -e '.[dashboard]'` and run:
 
@@ -21,26 +19,8 @@ Open the link on which the app is running. Note that you are running the dashboa
 
 ## 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.
-
-### Images
-
-- Click on select image and select an image saved on your computer for which you want the DIANNA explanation.
-- Click on select model and select your trained model used for the prediction and explanation.
-- Click on select labels and select a text file containing the labels of your trained model. Labels should be separated by line breaks in the file, no other separators should be used. Make sure that both the <ins>labels</ins> and <ins>ordering of the labels</ins> is correct in correspondence to your trained model.
-- Check the XAI methods you want to use, multiple methods can be used at the same time for comparison.
-- Your image explanation will start loading.
-
-Additionally, you can:
-- Select the number of top results you which to show the explanation for, i.e. if you use 2, the dashboard will show not only the explanation for the main prediction for the selected image, but also for the second most likely prediction.
-- Set the method specific settings to the selected XAI methods. If you don't change these, the default values are used.
-
-### Text
-
-- Input the text you want to use in the "Input string" box. Press enter to update the explanation.
-- Click on select model and select your trained model used for the prediction and explanation.
-- Click on select labels and select a text file containing the labels of your trained model. Labels should be separated by line breaks in the file, no other separators should be used. Make sure that both the <ins>labels</ins> and <ins>ordering of the labels</ins> is correct in correspondence to your trained model.
-- Check the XAI methods you want to use, multiple methods can be used at the same time for comparison.
-- Your image explanation will start loading.
+This [video](https://youtu.be/9VM5acip2s8) shows you how to use the DIANNA dashboard for some of the <img width="25" alt="Streamlit Logo" src="https://github.com/user-attachments/assets/2cac7d5d-c11a-48fe-b58e-71b15baaa163"> [tutorials](../../tutorials/README.md#summary-of-all-tutorials) to interactively explore the use cases from some of DIANNA's . 
+Similarly, you can use it with your own data, models and lables.
 
-Additionally, you can set the method specific settings to the selected XAI methods. If you don't change these, the default values are used.
+--------------------------------------------------------------------------
+![image](https://github.com/user-attachments/assets/1a98920e-f75e-468c-bf1f-f6e8bd2273ad)

setup.cfg

@@ -115,7 +115,7 @@ notebooks =
     torchvision
     ipywidgets
     freetype-py
-    transformers
+    transformers<4.46.0
     xgboost
 
 [options.entry_points]