Update links to product homepage.

CITATION.cff

@@ -19,7 +19,7 @@ authors:
     email: [email protected]
     affiliation: 'e:fs TechHub GmbH'
 repository-code: 'https://github.com/EFS-OpenSource/Thetis'
-url: 'https://efs-techhub.com/loesungen/thetis'
+url: 'https://efs-techhub.com/efs-portfolio/loesungen/thetis'
 abstract: >-
   Service to examine data processing pipelines (e.g.,
   machine learning or deep learning pipelines) for

README.md

@@ -23,7 +23,7 @@ the right wheel for installation.
 
 ## Get License
 
-Download a FREE license at [efs-techhub.com](https://efs-techhub.com/loesungen/thetis) (until August 31st). <br />
+Download a FREE license at [efs-techhub.com](https://efs-techhub.com/efs-portfolio/loesungen/thetis) (until August 31st). <br />
 Place the license file either in the working directory of your application or at:
 
 * Windows: `<User>\\AppData\\Local\\Thetis\\license.dat`
@@ -112,7 +112,7 @@ The library simply expects two Pandas data frames:
   and predictions must match.
 
 For details of the library configuration, see section [Configuration](https://efs-opensource.github.io/thetis/build/html/configuration.html). For the current example, you can download
-the demo configuration file at [efs-techhub.com](https://efs-techhub.com/loesungen/thetis).
+the demo configuration file at [efs-techhub.com](https://efs-techhub.com/efs-portfolio/loesungen/thetis).
 
 The final rating and recommendations for mitigation strategies can be found in the `result` JSON-like dictionary
 for the different evaluation aspects:
@@ -127,7 +127,7 @@ for the different evaluation aspects:
 
 Thetis is also capable to evaluate AI safety for modern (image-based) object detectors.
 We utilize a [Faster R-CNN by Torchvision](https://pytorch.org/vision/main/models/faster_rcnn.html) in conjunction
-with a demo data set ([Download here](https://efs-techhub.com/loesungen/thetis)) to demonstrate the evaluation process for
+with a demo data set ([Download here](https://efs-techhub.com/efs-portfolio/loesungen/thetis)) to demonstrate the evaluation process for
 object detectors. You can easily adapt your own use-case by following the instructions below:
 
 
@@ -155,7 +155,7 @@ categories = np.array(weights.meta["categories"])
 
 Note that the model is pre-trained on the MS COCO data set with several categories. In our example, we only
 work with the categories "person", "bicycle", and "car". In the next step, download and extract
-the [Demo Detection Data Set](https://efs-techhub.com/loesungen/thetis) which is artificially generated using
+the [Demo Detection Data Set](https://efs-techhub.com/efs-portfolio/loesungen/thetis) which is artificially generated using
 the [Carla simulation engine](https://carla.org/). After download and extraction, we can load the JSON annotation
 files and run inference with the Torchvision model:
 
@@ -260,7 +260,7 @@ result = thetis(
 
 For details of the library configuration, see section [Configuration](https://efs-opensource.github.io/thetis/build/html/configuration.html). For the current example, the configuration
 file is shipped with the demo data set. Alternatively, you can download
-the demo configuration file at [efs-techhub.com](https://efs-techhub.com/loesungen/thetis).
+the demo configuration file at [efs-techhub.com](https://efs-techhub.com/efs-portfolio/loesungen/thetis).
 
 The final rating and recommendations for mitigation strategies can be found in the `result` JSON-like dictionary
 for the different evaluation aspects:

docs/source/configuration.rst

@@ -4,7 +4,7 @@ Configuration
 =============
 
 Thetis needs a YAML configuration file that specifies several aspects, e.g., the task, available classes, requested
-evaluation aspects, etc. `Download an exemplary configuration file <https://efs-techhub.com/loesungen/thetis>`__ or copy/paste the
+evaluation aspects, etc. `Download an exemplary configuration file <https://efs-techhub.com/efs-portfolio/loesungen/thetis>`__ or copy/paste the
 following configuration file. An explanation for each configuration aspect can be found below.
 
 

docs/source/quickstart.rst

@@ -30,7 +30,7 @@ Prerequisites
 
      $ pip install thetis
 
-2. | Download a FREE license at `efs-techhub.com <https://efs-techhub.com/loesungen/thetis>`__ (until August 31st).
+2. | Download a FREE license at `efs-techhub.com <https://efs-techhub.com/efs-portfolio/loesungen/thetis>`__ (until August 31st).
    | Place the license file either in the working directory of your application or at:
 
    a. Windows: :code:`<User>\\AppData\\Local\\Thetis\\license.dat`
@@ -124,7 +124,7 @@ The library simply expects two Pandas data frames:
   and predictions must match.
 
 For details of the library configuration, see section :ref:`Configuration`. For the current example, you can download
-the demo configuration file at `efs-techhub.com <https://efs-techhub.com/loesungen/thetis>`__.
+the demo configuration file at `efs-techhub.com <https://efs-techhub.com/efs-portfolio/loesungen/thetis>`__.
 
 The final rating and recommendations for mitigation strategies can be found in the :code:`result` JSON-like dictionary
 for the different evaluation aspects:
@@ -140,7 +140,7 @@ Object Detection Example (Image-based)
 
 Thetis is also capable to evaluate AI safety for modern (image-based) object detectors.
 We utilize a `Faster R-CNN by Torchvision <https://pytorch.org/vision/main/models/faster_rcnn.html>`__ in conjunction
-with a demo data set (`Download here <https://efs-techhub.com/loesungen/thetis>`__) to demonstrate the evaluation process for
+with a demo data set (`Download here <https://efs-techhub.com/efs-portfolio/loesungen/thetis>`__) to demonstrate the evaluation process for
 object detectors. You can easily adapt your own use-case by following the instructions below:
 
 
@@ -171,7 +171,7 @@ First, we need to load and initialize the `Faster R-CNN by Torchvision <https://
 
 Note that the model is pre-trained on the MS COCO data set with several categories. In our example, we only
 work with the categories "person", "bicycle", and "car". In the next step, download and extract
-the `Demo Detection Data Set <https://efs-techhub.com/loesungen/thetis>`__ which is artificially generated using
+the `Demo Detection Data Set <https://efs-techhub.com/efs-portfolio/loesungen/thetis>`__ which is artificially generated using
 the `Carla simulation engine <https://carla.org/>`__. After download and extraction, we can load the JSON annotation
 files and run inference with the Torchvision model:
 
@@ -281,7 +281,7 @@ with the configuration to the Thetis evaluation routine:
 
 For details of the library configuration, see section :ref:`Configuration`. For the current example, the configuration
 file is shipped with the demo data set. Alternatively, you can download
-the demo configuration file at `efs-techhub.com <https://efs-techhub.com/loesungen/thetis>`__.
+the demo configuration file at `efs-techhub.com <https://efs-techhub.com/efs-portfolio/loesungen/thetis>`__.
 
 The final rating and recommendations for mitigation strategies can be found in the :code:`result` JSON-like dictionary
 for the different evaluation aspects:

pyproject.toml

@@ -49,7 +49,7 @@ file = "README.md"
 content-type = "text/markdown"
 
 [project.urls]
-Homepage = "https://efs-techhub.com/loesungen/thetis"
+Homepage = "https://efs-techhub.com/efs-portfolio/loesungen/thetis"
 GitHub = "https://github.com/EFS-OpenSource/Thetis"
 Documentation = "https://efs-opensource.github.io/Thetis"