Fix paths due to removal of python-client

.gitattributes

@@ -1,3 +1,3 @@
 # Set the default behavior, in case people don't have core.autocrlf set.
 * text=auto
-python-client/docs/** linguist-documentation
+docs/** linguist-documentation

.github/dependabot.yml

@@ -5,18 +5,13 @@
 
 version: 2
 updates:
-  - package-ecosystem: "gradle"
-    directory: "/backend"
-    schedule:
-      interval: "monthly"
-
   - package-ecosystem: "npm"
-    directory: "/frontend"
+    directory: "/"
     schedule:
       interval: "monthly"
 
   - package-ecosystem: "pip"
-    directory: "/python-client"
+    directory: "/"
     schedule:
       interval: "monthly"
 

.github/workflows/build-python.yml

@@ -82,7 +82,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
           cache: true
 
-      - name: Set up Pandoc (needed for python-client doc)
+      - name: Set up Pandoc (needed for doc)
         uses: r-lib/actions/setup-pandoc@v2
         with:
           pandoc-version: '3.1.7' # https://github.com/jgm/pandoc/releases
@@ -92,7 +92,7 @@ jobs:
         if: ${{ github.event_name == 'pull_request' || inputs.use-cache }}
         with:
           path: ~/.giskard
-          key: ${{ matrix.os }}-${{ matrix.python-version }}-python-test-resources-${{ hashFiles('python-client/tests/fixtures/**/*py')}}
+          key: ${{ matrix.os }}-${{ matrix.python-version }}-python-test-resources-${{ hashFiles('tests/fixtures/**/*py')}}
           restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-python-giskard-test-resources
 
       - name: Install dependencies

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ repos:
         files: '^.*\.py$'
         args:
           - "--config"
-          - "python-client/pyproject.toml"
+          - "pyproject.toml"
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.0.272
@@ -15,6 +15,6 @@ repos:
         files: '^.*\.py$'
         args:
           - "--config"
-          - "python-client/pyproject.toml"
+          - "pyproject.toml"
           - "--fix"
           - "--exit-non-zero-on-fix"

.readthedocs.yaml

@@ -7,10 +7,10 @@ build:
   jobs:
     post_create_environment:
       - pip install pdm
-      - pdm export -p python-client --without-hashes -G doc,server -o requirements-sphinx.txt
+      - pdm export --without-hashes -G doc,server -o requirements-sphinx.txt
 
 sphinx:
-  configuration: python-client/docs/conf.py
+  configuration: docs/conf.py
 
 python:
   install:

README.dev.md

@@ -1,6 +1,6 @@
 # Giskard developer readme
 
-🚧  **WORK IN PROGRESS** 🚧 
+🚧  **WORK IN PROGRESS** 🚧
 This readme is in process of creation, some information can be subject to change
 
 ## Requirements
@@ -19,7 +19,7 @@ After running the command above there will be 3 modules running:
 - backend
 - ml-worker
 
-> When running Giskard in "dev mode" (as above) there's an H2 database that's used instead of a real one, the default location of this H2 database is `$HOME/giskard-home/database` 
+> When running Giskard in "dev mode" (as above) there's an H2 database that's used instead of a real one, the default location of this H2 database is `$HOME/giskard-home/database`
 
 To build the project use
 ```shell
@@ -58,7 +58,7 @@ docker-compose logs backend
 ## Python client
 To install the latest version of python library `giskard` the following script can be used:
 ```shell
-sh -c "$(curl -fsSL https://raw.githubusercontent.com/Giskard-AI/giskard/main/python-client/scripts/install-giskard-client-dev.sh)"
+sh -c "$(curl -fsSL https://raw.githubusercontent.com/Giskard-AI/giskard/main/scripts/install-giskard-client-dev.sh)"
 ```
 it will use the default `pip` available in the environment. To install `giskard` using a specific `pip` you can modify the command above, like
 ```shell

README.md

@@ -24,7 +24,7 @@
 </p>
 <h3 align="center">
    <a href="https://docs.giskard.ai/en/latest/getting-started/quickstart.html"><b>Documentation</b></a> &bull;
-   <a href="https://www.giskard.ai/knowledge-categories/blog/?utm_source=github&utm_medium=github&utm_campaign=github_readme&utm_id=readmeblog"><b>Blog</b></a> &bull;  
+   <a href="https://www.giskard.ai/knowledge-categories/blog/?utm_source=github&utm_medium=github&utm_campaign=github_readme&utm_id=readmeblog"><b>Blog</b></a> &bull;
   <a href="https://www.giskard.ai/?utm_source=github&utm_medium=github&utm_campaign=github_readme&utm_id=readmeblog"><b>Website</b></a> &bull;
   <a href="https://gisk.ar/discord"><b>Discord Community</b></a> &bull;
   <a href="https://www.giskard.ai/about?utm_source=github&utm_medium=github&utm_campaign=github_readme&utm_id=readmeblog#advisors"><b>Advisors</b></a>
@@ -38,7 +38,7 @@ pip install "giskard[server]>=2.0.0b" -U
 ```
 We officially support Python 3.8, 3.9, 3.10 and 3.11.
 ## Try in Colab 📙
-[Open Colab notebook](https://colab.research.google.com/github/giskard-ai/giskard/blob/main/python-client/docs/getting-started/quickstart.ipynb)
+[Open Colab notebook](https://colab.research.google.com/github/giskard-ai/giskard/blob/main/docs/getting-started/quickstart.ipynb)
 
 ______________________________________________________________________
 
@@ -49,7 +49,7 @@ ______________________________________________________________________
 Giskard is a Python library that automatically **detects vulnerabilities** of AI models, from tabular models to LLM, including: performance biases, data leakage, spurious correlation, hallucination, toxicity, security issues and many more.
 
 It's a powerful tool that helps data scientists **save time and effort** drilling down on model issues, and produce more **reliable and trustworthy models**.
- 
+
 <p align="center">
   <img src="readme/scan_example.gif" alt="Scan Example" width="800">
 </p>
@@ -156,7 +156,7 @@ If you are interested in learning more about Giskard's premium offering, please
 
 ## 1. Start the Giskard server
 
-To start the **Giskard server**, run the following command: 
+To start the **Giskard server**, run the following command:
 ```sh
 giskard server start
 ```
@@ -171,10 +171,10 @@ You can then **upload the test suite** created using the `giskard` Python librar
 - Create more domain-specific tests relevant to your use-case
 - Share results, and collaborate with your team to integrate business feedback
 
-1. First, make sure Giskard server is installed 
+1. First, make sure Giskard server is installed
     <details>
       <summary>How to check if the Giskard server is running</summary>
-      
+
       - check if http://localhost:19000 is running
       - or use `giskard server status`
     </details>
@@ -190,9 +190,9 @@ You can then **upload the test suite** created using the `giskard` Python librar
     client = giskard.GiskardClient(
         url="http://localhost:19000", key=key  # URL of your Giskard instance
     )
-    
+
     my_project = client.create_project("my_project", "PROJECT_NAME", "DESCRIPTION")
-    
+
     # Upload to the current project
     test_suite.upload(client, "my_project")
     ```
@@ -203,12 +203,12 @@ You can then **upload the test suite** created using the `giskard` Python librar
 <details>
   <summary>What is a ML worker?</summary>
 
-  Giskard executes your model using a worker that runs the model directly in your Python environment containing all the dependencies required by your model. You can either execute the ML worker from a local notebook, a Colab notebook or a terminal. 
+  Giskard executes your model using a worker that runs the model directly in your Python environment containing all the dependencies required by your model. You can either execute the ML worker from a local notebook, a Colab notebook or a terminal.
   </details>
 
 <details>
   <summary>How to get the API key</summary>
-  
+
   Access the API key in the Settings tab of the Giskard server.
 </details>
 

docs/README.md

@@ -1,5 +1,5 @@
 ## Building the docs locally (continuously)
 ```
 cd giskard
-./gradlew :python-client:sphinx-autobuild
+pdm run watch-doc
 ```

docs/conf.py

@@ -103,8 +103,8 @@
     "top_of_page_button": "edit",
     "source_repository": "https://github.com/Giskard-AI/giskard",
     "source_branch": "main",
-    "source_directory": "python-client/docs/",
-    "source_edit_link": "https://github.com/Giskard-AI/giskard/edit/main/python-client/docs/{filename}",
+    "source_directory": "docs/",
+    "source_edit_link": "https://github.com/Giskard-AI/giskard/edit/main/docs/{filename}",
 }
 
 add_function_parentheses = False
@@ -121,8 +121,8 @@
 .. raw:: html
 
     <div class="open-in-colab__wrapper">
-    <a href="https://colab.research.google.com/github/giskard-ai/giskard/blob/""" + branch + """/python-client/docs/{{ env.doc2path(env.docname, base=None) }}" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" style="display: inline; margin: 0" alt="Open In Colab"/></a>
-    <a href="https://github.com/giskard-ai/giskard/tree/""" + branch + """/python-client/docs/{{ env.doc2path(env.docname, base=None) }}" target="_blank"><img src="https://img.shields.io/badge/github-view%20source-black.svg" style="display: inline; margin: 0" alt="View Notebook on GitHub"/></a>
+    <a href="https://colab.research.google.com/github/giskard-ai/giskard/blob/""" + branch + """/docs/{{ env.doc2path(env.docname, base=None) }}" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" style="display: inline; margin: 0" alt="Open In Colab"/></a>
+    <a href="https://github.com/giskard-ai/giskard/tree/""" + branch + """/docs/{{ env.doc2path(env.docname, base=None) }}" target="_blank"><img src="https://img.shields.io/badge/github-view%20source-black.svg" style="display: inline; margin: 0" alt="View Notebook on GitHub"/></a>
     </div>
 """
 # fmt: on
@@ -171,5 +171,5 @@ def linkcode_resolve(domain, info):
     else:
         linespec = ""
 
-    filename = fn.split("python-client", 1)[-1]
-    return f"https://github.com/Giskard-AI/giskard/blob/feature/ai-test-v2-merged/python-client{filename}{linespec}"
+    filename = fn.split("main", 1)[-1]
+    return f"https://github.com/Giskard-AI/giskard/blob/main/{filename}{linespec}"

docs/guides/scan/index.rst

@@ -30,7 +30,7 @@ Scan your model to detect vulnerabilities
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. hint::
 
-    If you want to try this interactively, you can find a ready-to-use `notebook here <https://colab.research.google.com/github/giskard-ai/giskard/blob/main/python-client/docs/getting-started/quickstart.ipynb>`_.
+    If you want to try this interactively, you can find a ready-to-use `notebook here <https://colab.research.google.com/github/giskard-ai/giskard/blob/main/docs/getting-started/quickstart.ipynb>`_.
 
 To scan your model, wrap first your `model <../wrap_model/index.md>`_ & `dataset <../wrap_dataset/index.md>`_:
 
@@ -117,7 +117,7 @@ You can then upload the test suite to the local Giskard server. This will enable
 
 .. code-block:: python
 
-    # Uploading the test suite will automatically save the model, dataset, tests, slicing & transformation functions inside the Giskard server that you previously installed locally, or on your internal servers. 
+    # Uploading the test suite will automatically save the model, dataset, tests, slicing & transformation functions inside the Giskard server that you previously installed locally, or on your internal servers.
     # Create a Giskard client after having installed the Giskard server (see documentation)
 
     from giskard import GiskardClient

docs/tutorials/libraries/tensorflow/classification_tfhub.md

@@ -10,7 +10,7 @@ from giskard import Dataset, Model
 ```
 ## Wrap dataset
 ```python
-data_filtered = pd.read_csv('https://raw.githubusercontent.com/Giskard-AI/giskard/python-client/tests/tensorflow/test_text_classification_tfhub.csv').dropna(axis=0)
+data_filtered = pd.read_csv('https://raw.githubusercontent.com/Giskard-AI/giskard/main/tests/models/tensorflow/test_text_classification_tfhub.csv').dropna(axis=0)
 
 classification_labels_mapping = {'REGULATION': 0, 'INTERNAL': 1, 'CALIFORNIA CRISIS': 2, 'INFLUENCE': 3}
 
@@ -22,8 +22,8 @@ x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y,
 test_df = pd.DataFrame(list(zip(list(x_test), list(y_test))), columns=["Content", "Target"])
 ```
 ```python
-wrapped_dataset = Dataset(test_df.head(), 
-                               name="test dataset", 
+wrapped_dataset = Dataset(test_df.head(),
+                               name="test dataset",
                                target="Target")
 ```
 ## Wrap model