Update Github Actions

.devcontainer/devcontainer.json

@@ -4,6 +4,9 @@
   "name": "Python 3",
   // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
   "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "features": {
+    "ghcr.io/devcontainers-contrib/features/poetry:2": {}
+  },
 
   "remoteEnv": {
     "SPECKLE_TOKEN": "foobar"
@@ -19,7 +22,7 @@
   // "forwardPorts": [],
 
   // Use 'postCreateCommand' to run commands after the container is created.
-  "postCreateCommand": "cp .env.example .env && python -m venv .venv && . .venv/bin/activate && pip install --upgrade pip && pip install .[dev]",
+  "postCreateCommand": "cp .env.example .env && POETRY_VIRTUALENVS_IN_PROJECT=true poetry install --no-root",
 
   // Configure tool-specific properties.
   "customizations": {
@@ -37,4 +40,4 @@
 
   // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
   // "remoteUser": "root"
-}
+}

.env.example

@@ -1,4 +1 @@
-SPECKLE_TOKEN="mytoken"
-SPECKLE_SERVER_URL="http://127.0.0.1:3000"
-SPECKLE_PROJECT_ID=""
-SPECKLE_AUTOMATION_ID=""
+SPECKLE_TOKEN=mytoken

.github/dependabot.yml

@@ -4,7 +4,3 @@ updates:
     directory: "/"
     schedule:
       interval: "daily"
-  - package-ecosystem: "pip"
-    directory: "/"
-    schedule:
-      interval: "weekly"

.github/workflows/main.yml

@@ -1,9 +1,9 @@
-name: "build and deploy Speckle functions"
+name: 'build and deploy Speckle functions'
 on:
   workflow_dispatch:
   push:
     tags:
-      - "*"
+      - '*'
 
 jobs:
   publish-automate-function-version: # make sure the action works on a clean machine without building
@@ -11,13 +11,19 @@ jobs:
       FUNCTION_SCHEMA_FILE_NAME: functionSchema.json
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v6.0.2
+      - uses: actions/checkout@v4.1.1
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.13"
-      - name: Install dependencies
-        run: |
-          pip install .[dev]
+          python-version: '3.11'
+      - name: Install and configure Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.3.2
+          virtualenvs-create: false
+          virtualenvs-in-project: false
+          installer-parallel: true
+      - name: Restore dependencies
+        run: poetry install --no-root
       - name: Extract functionInputSchema
         id: extract_schema
         run: |
@@ -25,8 +31,8 @@ jobs:
       - name: Speckle Automate Function - Build and Publish
         uses: specklesystems/speckle-automate-github-composite-action@0.8.1
         with:
-          speckle_automate_url: ${{ env.SPECKLE_AUTOMATE_URL || vars.SPECKLE_AUTOMATE_URL || 'https://automate.speckle.dev' }}
+          speckle_automate_url: ${{ env.SPECKLE_AUTOMATE_URL || 'https://automate.speckle.dev' }} 
           speckle_token: ${{ secrets.SPECKLE_FUNCTION_TOKEN }}
           speckle_function_id: ${{ secrets.SPECKLE_FUNCTION_ID }}
           speckle_function_input_schema_file_path: ${{ env.FUNCTION_SCHEMA_FILE_NAME }}
-          speckle_function_command: "python -u main.py run"
+          speckle_function_command: 'python -u main.py run'

.vscode/launch.json

@@ -6,7 +6,7 @@
   "configurations": [
     {
       "name": "Speckle Automate function",
-      "type": "debugpy",
+      "type": "python",
       "request": "launch",
       "program": "main.py",
       "console": "integratedTerminal",

Dockerfile

@@ -1,16 +1,16 @@
 # We use the official Python 3.11 image as our base image and will add our code to it. For more details, see https://hub.docker.com/_/python
-FROM python:3.13-slim
+FROM python:3.11-slim
+
+# We install poetry to generate a list of dependencies which will be required by our application
+RUN pip install poetry
 
 # We set the working directory to be the /home/speckle directory; all of our files will be copied here.
 WORKDIR /home/speckle
 
-# Copy pyproject.toml first to leverage Docker layer caching
-COPY pyproject.toml /home/speckle/
-
-# Install the required Python packages (production dependencies only)
-RUN pip install --no-cache-dir .
-
 # Copy all of our code and assets from the local directory into the /home/speckle directory of the container.
 # We also ensure that the user 'speckle' owns these files, so it can access them
 # This assumes that the Dockerfile is in the same directory as the rest of the code
 COPY . /home/speckle
+
+# Using poetry, we generate a list of requirements, save them to requirements.txt, and then use pip to install them
+RUN poetry export --format requirements.txt --output /home/speckle/requirements.txt && pip install --requirement /home/speckle/requirements.txt

README.md

@@ -1,57 +1,27 @@
 # Speckle Automate function template - Python
 
-This template repository is for a Speckle Automate function written in Python
+This is a template repository for a Speckle Automate functions written in python
 using the [specklepy](https://pypi.org/project/specklepy/) SDK to interact with Speckle data.
 
-This template contains the full scaffolding required to publish a function to the Automate environment.
-It also has some sane defaults for development environment setups.
+This template contains the full scaffolding required to publish a function to the automate environment.
+Also has some sane defaults for a development environment setups.
 
 ## Getting started
 
 1. Use this template repository to create a new repository in your own / organization's profile.
-1. Register the function
+
+Register the function 
 
 ### Add new dependencies
 
-To add new Python package dependencies to the project, edit the `pyproject.toml` file:
-
-**For packages your function needs to run** (like pandas, requests, etc.):
-```toml
-dependencies = [
-    "specklepy==3.0.0",
-    "pandas==2.1.0",  # Add production dependencies here
-]
-```
-
-**For development tools** (like testing or formatting tools):
-```toml
-[project.optional-dependencies]
-dev = [
-    "black==23.12.1",
-    "pytest-mock==3.11.1",  # Add development dependencies here
-    # ... other dev tools
-]
-```
-
-**How to decide which section?**
-- If your `main.py` (or other function logic) imports it → `dependencies`
-- If it's just a tool to help you code → `[project.optional-dependencies].dev`
-
-Example:
-```python
-# In your main.py
-import pandas as pd  # ← This goes in dependencies
-import specklepy     # ← This goes in dependencies
-
-# You won't import these in main.py:
-# pytest, black, mypy ← These go in [project.optional-dependencies].dev
-```
+To add new python package dependencies to the project, use:
+`$ poetry add pandas`
 
 ### Change launch variables
 
-Describe how the launch.json should be edited.
+describe how the launch.json should be edited
 
-### GitHub Codespaces
+### Github Codespaces
 
 Create a new repo from this template, and use the create new code.
 
@@ -59,81 +29,37 @@ Create a new repo from this template, and use the create new code.
 
 1. [Create](https://automate.speckle.dev/) a new Speckle Automation.
 1. Select your Speckle Project and Speckle Model.
-1. Select the deployed Speckle Function.
+1. Select the existing Speckle Function named [`Random comment on IFC beam`](https://automate.speckle.dev/functions/e110be8fad).
 1. Enter a phrase to use in the comment.
 1. Click `Create Automation`.
 
-## Getting Started with Creating Your Own Speckle Function
+## Getting Started with creating your own Speckle Function
 
 1. [Register](https://automate.speckle.dev/) your Function with [Speckle Automate](https://automate.speckle.dev/) and select the Python template.
 1. A new repository will be created in your GitHub account.
-1. Make changes to your Function in `main.py`. See below for the Developer Requirements and instructions on how to test.
+1. Make changes to your Function in `main.py`. See below for the Developer Requirements, and instructions on how to test.
 1. To create a new version of your Function, create a new [GitHub release](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository) in your repository.
 
 ## Developer Requirements
 
 1. Install the following:
-    - [Python 3.11+](https://www.python.org/downloads/)
-1. Run the following to set up your development environment:
-    ```bash
-    python -m venv .venv
-    # On Windows
-    .venv\Scripts\activate
-    # On macOS/Linux
-    source .venv/bin/activate
-
-    pip install --upgrade pip
-    pip install .[dev]
-    ```
-
-**What this installs:**
-- All the packages your function needs to run (`dependencies`)
-- Plus development tools like testing and code formatting (`[project.optional-dependencies].dev`)
-
-**Why separate sections?**
-- `dependencies`: Only what gets deployed with your function (lightweight)
-- `dev` dependencies: Extra tools to help you write better code locally
+    - [Python 3](https://www.python.org/downloads/)
+    - [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer)
+1. Run `poetry shell && poetry install` to install the required Python packages.
 
 ## Building and Testing
 
-The code can be tested locally by running `pytest`.
-
-### Alternative dependency managers
-
-This template uses the modern **PEP 621** standard in `pyproject.toml`, which works with all modern Python dependency managers:
-
-#### Using Poetry
-```bash
-poetry install  # Automatically reads pyproject.toml
-```
-
-#### Using uv
-```bash
-uv sync  # Automatically reads pyproject.toml
-```
-
-#### Using pip-tools
-```bash
-pip-compile pyproject.toml  # Generate requirements.txt from pyproject.toml
-pip install -r requirements.txt
-```
-
-#### Using pdm
-```bash
-pdm install  # Automatically reads pyproject.toml
-```
-
-**Advantage**: All tools read the same `pyproject.toml` file, so there's no need to keep multiple files in sync!
+The code can be tested locally by running `poetry run pytest`.
 
 ### Building and running the Docker Container Image
 
-Running and testing your code on your machine is a great way to develop your Function; the following instructions are a bit more in-depth and only required if you are having issues with your Function in GitHub Actions or on Speckle Automate.
+Running and testing your code on your own machine is a great way to develop your Function; the following instructions are a bit more in-depth and only required if you are having issues with your Function in GitHub Actions or on Speckle Automate.
 
 #### Building the Docker Container Image
 
-The GitHub Action packages your code into the format required by Speckle Automate. This is done by building a Docker Image, which Speckle Automate runs. You can attempt to build the Docker Image locally to test the building process.
+Your code is packaged by the GitHub Action into the format required by Speckle Automate. This is done by building a Docker Image, which is then run by Speckle Automate. You can attempt to build the Docker Image yourself to test the building process locally.
 
-To build the Docker Container Image, you must have [Docker](https://docs.docker.com/get-docker/) installed.
+To build the Docker Container Image, you will need to have [Docker](https://docs.docker.com/get-docker/) installed.
 
 Once you have Docker running on your local machine:
 
@@ -147,7 +73,7 @@ Once you have Docker running on your local machine:
 
 #### Running the Docker Container Image
 
-Once the GitHub Action has built the image, it is sent to Speckle Automate. When Speckle Automate runs your Function as part of an Automation, it will run the Docker Container Image. You can test that your Docker Container Image runs correctly locally.
+Once the image has been built by the GitHub Action, it is sent to Speckle Automate. When Speckle Automate runs your Function as part of an Automation, it will run the Docker Container Image. You can test that your Docker Container Image runs correctly by running it locally.
 
 1. To then run the Docker Container Image, run the following command:
 
@@ -161,14 +87,14 @@ Once the GitHub Action has built the image, it is sent to Speckle Automate. When
 
 Let's explain this in more detail:
 
-`docker run—-rm speckle_automate_python_example` tells Docker to run the Docker Container Image we built earlier. `speckle_automate_python_example` is the name of the Docker Container Image. The `--rm` flag tells Docker to remove the container after it has finished running, freeing up space on your machine.
+`docker run --rm speckle_automate_python_example` tells Docker to run the Docker Container Image that we built earlier. `speckle_automate_python_example` is the name of the Docker Container Image that we built earlier. The `--rm` flag tells docker to remove the container after it has finished running, this frees up space on your machine.
 
-The line `python -u main.py run` is the command run inside the Docker Container Image. The rest of the command is the arguments passed to the command. The arguments are:
+The line `python -u main.py run` is the command that is run inside the Docker Container Image. The rest of the command is the arguments that are passed to the command. The arguments are:
 
 - `'{"projectId": "1234", "modelId": "1234", "branchName": "myBranch", "versionId": "1234", "speckleServerUrl": "https://speckle.xyz", "automationId": "1234", "automationRevisionId": "1234", "automationRunId": "1234", "functionId": "1234", "functionName": "my function", "functionLogo": "base64EncodedPng"}'` - the metadata that describes the automation and the function.
-- `{}` - the input parameters for the function the Automation creator can set. Here, they are blank, but you can add your parameters to test your function.
-- `yourSpeckleServerAuthenticationToken`—the authentication token for the Speckle Server that the Automation can connect to. This is required to interact with the Speckle Server, for example, to get data from the Model.
+- `{}` - the input parameters for the function that the Automation creator is able to set. Here they are blank, but you can add your own parameters to test your function.
+- `yourSpeckleServerAuthenticationToken` - the authentication token for the Speckle Server that the Automation can connect to. This is required to be able to interact with the Speckle Server, for example to get data from the Model.
 
 ## Resources
 
-- [Learn](https://speckle.guide/dev/python.html) more about SpecklePy and interacting with Speckle from Python.
+- [Learn](https://speckle.guide/dev/python.html) more about SpecklePy, and interacting with Speckle from Python.

example.function_inputs.json

@@ -2,19 +2,19 @@
   "speckleToken": "YOUR SPEKCLE TOKEN",
   "functionInputs": {
     "whisperMessage": "you are doing something weird",
-    "forbiddenSpeckleType": "wall"
+    "forbiddenSpeckleType": ""
   },
   "automationRunData": {
     "project_id": "project id",
+    "model_id": "model id",
+    "branch_name": "branch name",
+    "version_id": "version id",
     "speckle_server_url": "https://latest.speckle.systems",
     "automation_id": "automation id",
+    "automation_revision_id": "automation revision id",
     "automation_run_id": "automation run id",
-    "function_run_id": "function run id",
-    "triggers": [
-      {
-        "payload": { "modelId": "model id", "versionId": "version id" },
-        "triggerType": "versionCreation"
-      }
-    ]
+    "function_id": "function id",
+    "function_name": "function name",
+    "function_logo": null
   }
 }

flatten.py

@@ -6,22 +6,8 @@ from specklepy.objects import Base
 
 
 def flatten_base(base: Base) -> Iterable[Base]:
-    """Flatten a base object into an iterable of bases.
-
-    This function recursively traverses the `elements` or `@elements` attribute of the
-    base object, yielding each nested base object.
-
-    Args:
-        base (Base): The base object to flatten.
-
-    Yields:
-        Base: Each nested base object in the hierarchy.
-    """
-    # Attempt to get the elements attribute, fallback to @elements if necessary
-    elements = getattr(base, "elements", getattr(base, "@elements", None))
-
-    if elements is not None:
-        for element in elements:
+    """Take a base and flatten it to an iterable of bases."""
+    if hasattr(base, "elements"):
+        for element in base["elements"]:
             yield from flatten_base(element)
-
     yield base

main.py

@@ -1,6 +1,6 @@
-"""This module contains the function's business logic.
+"""This module contains the business logic of the function.
 
-Use the automation_context module to wrap your function in an Automate context helper.
+Use the automation_context module to wrap your function in an Autamate context helper
 """
 
 from pydantic import Field, SecretStr
@@ -14,14 +14,14 @@ from flatten import flatten_base
 
 
 class FunctionInputs(AutomateBase):
-    """These are function author-defined values.
+    """These are function author defined values.
 
     Automate will make sure to supply them matching the types specified here.
     Please use the pydantic model schema to define your inputs:
     https://docs.pydantic.dev/latest/usage/models/
     """
 
-    # An example of how to use secret values.
+    # an example how to use secret values
     whisper_message: SecretStr = Field(title="This is a secret message")
     forbidden_speckle_type: str = Field(
         title="Forbidden speckle type",
@@ -39,13 +39,13 @@ def automate_function(
     """This is an example Speckle Automate function.
 
     Args:
-        automate_context: A context-helper object that carries relevant information
+        automate_context: A context helper object, that carries relevant information
             about the runtime context of this function.
-            It gives access to the Speckle project data that triggered this run.
-            It also has convenient methods for attaching results to the Speckle model.
+            It gives access to the Speckle project data, that triggered this run.
+            It also has conveniece methods attach result data to the Speckle model.
         function_inputs: An instance object matching the defined schema.
     """
-    # The context provides a convenient way to receive the triggering version.
+    # the context provides a conveniet way, to receive the triggering version
     version_root_object = automate_context.receive_version()
 
     objects_with_forbidden_speckle_type = [
@@ -56,11 +56,11 @@ def automate_function(
     count = len(objects_with_forbidden_speckle_type)
 
     if count > 0:
-        # This is how a run is marked with a failure cause.
+        # this is how a run is marked with a failure cause
         automate_context.attach_error_to_objects(
             category="Forbidden speckle_type"
-            f" ({function_inputs.forbidden_speckle_type})",
-            affected_objects=objects_with_forbidden_speckle_type,
+            " ({function_inputs.forbidden_speckle_type})",
+            object_ids=[o.id for o in objects_with_forbidden_speckle_type if o.id],
             message="This project should not contain the type: "
             f"{function_inputs.forbidden_speckle_type}",
         )
@@ -70,15 +70,15 @@ def automate_function(
             f"{function_inputs.forbidden_speckle_type}"
         )
 
-        # Set the automation context view to the original model/version view
-        # to show the offending objects.
+        # set the automation context view, to the original model / version view
+        # to show the offending objects
         automate_context.set_context_view()
 
     else:
         automate_context.mark_run_success("No forbidden types found.")
 
-    # If the function generates file results, this is how it can be
-    # attached to the Speckle project/model
+    # if the function generates file results, this is how it can be
+    # attached to the Speckle project / model
     # automate_context.store_file_result("./report.pdf")
 
 
@@ -94,10 +94,10 @@ def automate_function_without_inputs(automate_context: AutomationContext) -> Non
 
 # make sure to call the function with the executor
 if __name__ == "__main__":
-    # NOTE: always pass in the automate function by its reference; do not invoke it!
+    # NOTE: always pass in the automate function by its reference, do not invoke it!
 
-    # Pass in the function reference with the inputs schema to the executor.
+    # pass in the function reference with the inputs schema to the executor
     execute_automate_function(automate_function, FunctionInputs)
 
-    # If the function has no arguments, the executor can handle it like so
+    # if the function has no arguments, the executor can handle it like so
     # execute_automate_function(automate_function_without_inputs)

mise.toml

@@ -1,2 +0,0 @@
-[tools]
-python = '3.13'

pyproject.toml

@@ -1,37 +1,33 @@
-[project]
-name = "speckle-automate-function"
+[tool.poetry]
+name = "speckle-automate-py"
 version = "0.1.0"
-requires-python = ">=3.13"
-authors = [{ name = "Speckle Systems", email = "hello@speckle.systems" }]
-maintainers = [{ name = "Speckle Systems", email = "hello@speckle.systems" }]
-description = "A Speckle Automate function template using specklepy"
+description = "Example function for Speckle Automate using specklepy"
+authors = ["Gergő Jedlicska <gergo@jedlicska.com>"]
 readme = "README.md"
-license = "Apache-2.0"
-keywords = ["speckle", "automate", "bim", "aec"]
 
-dependencies = ["specklepy==3.1.0"]
+[tool.poetry.dependencies]
+python = "^3.11"
+specklepy = "2.17.17"
 
-[project.optional-dependencies]
-dev = [
-    "mypy==1.13.0",
-    "pytest==7.4.4",
-    "ruff==0.11.12",
-]
+[tool.poetry.group.dev.dependencies]
+black = "^23.3.0"
+mypy = "^1.3.0"
+ruff = "^0.0.271"
+pytest = "^7.4.2"
+# specklepy = { path = "../specklepy", develop = true }
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
 
 [tool.ruff]
-exclude = [".venv", "**/*.yml"]
-
-[tool.ruff.lint]
 select = [
-    "E",  # pycodestyle
-    "F",  # pyflakes
-    "UP", # pyupgrade
-    "D",  # pydocstyle
-    "I",  # isort
+  "E",  # pycodestyle
+  "F",  # pyflakes
+  "UP", # pyupgrade
+  "D",  # pydocstyle
+  "I",  # isort
 ]
 
-[tool.ruff.lint.pydocstyle]
+[tool.ruff.pydocstyle]
 convention = "google"
-
-[tool.setuptools]
-py-modules = []

tests/__init__.py

@@ -1 +0,0 @@
-"""Tests for the automate function."""

tests/test_function.py

@@ -1,31 +1,168 @@
 """Run integration tests with a speckle server."""
+import os
+import secrets
+import string
 
-from pydantic import SecretStr
+from specklepy.logging.exceptions import SpeckleException
+
+import pytest
+from gql import gql
 from speckle_automate import (
     AutomationContext,
     AutomationRunData,
     AutomationStatus,
     run_function,
 )
-from speckle_automate.fixtures import *  # noqa: F403
+from specklepy.api import operations
+from specklepy.api.client import SpeckleClient
+from specklepy.objects.base import Base
+from specklepy.transports.server import ServerTransport
 
 from main import FunctionInputs, automate_function
 
 
-def test_function_run(
-    test_automation_run_data: AutomationRunData, test_automation_token: str
+def crypto_random_string(length: int) -> str:
+    """Generate a semi crypto random string of a given length."""
+    alphabet = string.ascii_letters + string.digits
+    return "".join(secrets.choice(alphabet) for _ in range(length))
+
+
+def register_new_automation(
+    project_id: str,
+    model_id: str,
+    speckle_client: SpeckleClient,
+    automation_id: str,
+    automation_name: str,
+    automation_revision_id: str,
 ):
+    """Register a new automation in the speckle server."""
+    query = gql(
+        """
+        mutation CreateAutomation(
+            $projectId: String! 
+            $modelId: String! 
+            $automationName: String!
+            $automationId: String! 
+            $automationRevisionId: String!
+        ) {
+                automationMutations {
+                    create(
+                        input: {
+                            projectId: $projectId
+                            modelId: $modelId
+                            automationName: $automationName 
+                            automationId: $automationId
+                            automationRevisionId: $automationRevisionId
+                        }
+                    )
+                }
+            }
+        """
+    )
+    params = {
+        "projectId": project_id,
+        "modelId": model_id,
+        "automationName": automation_name,
+        "automationId": automation_id,
+        "automationRevisionId": automation_revision_id,
+    }
+    speckle_client.httpclient.execute(query, params)
+
+
+@pytest.fixture()
+def speckle_token() -> str:
+    """Provide a speckle token for the test suite."""
+    env_var = "SPECKLE_TOKEN"
+    token = os.getenv(env_var)
+    if not token:
+        raise ValueError(f"Cannot run tests without a {env_var} environment variable")
+    return token
+
+
+@pytest.fixture()
+def speckle_server_url() -> str:
+    """Provide a speckle server url for the test suite, default to localhost."""
+    return os.getenv("SPECKLE_SERVER_URL", "http://127.0.0.1:3000")
+
+
+@pytest.fixture()
+def test_client(speckle_server_url: str, speckle_token: str) -> SpeckleClient:
+    """Initialize a SpeckleClient for testing."""
+    test_client = SpeckleClient(
+        speckle_server_url, speckle_server_url.startswith("https")
+    )
+    test_client.authenticate_with_token(speckle_token)
+    return test_client
+
+
+@pytest.fixture()
+def test_object() -> Base:
+    """Create a Base model for testing."""
+    root_object = Base()
+    root_object.foo = "bar"
+    return root_object
+
+
+@pytest.fixture()
+def automation_run_data(
+    test_object: Base, test_client: SpeckleClient, speckle_server_url: str
+) -> AutomationRunData:
+    """Set up an automation context for testing."""
+    project_id = test_client.stream.create("Automate function e2e test")
+    branch_name = "main"
+
+    model = test_client.branch.get(project_id, branch_name, commits_limit=1)
+    model_id: str = model.id
+
+    root_obj_id = operations.send(
+        test_object, [ServerTransport(project_id, test_client)]
+    )
+    version_id = test_client.commit.create(project_id, root_obj_id)
+    if isinstance(version_id, SpeckleException):
+        raise version_id
+
+    automation_name = crypto_random_string(10)
+    automation_id = crypto_random_string(10)
+    automation_revision_id = crypto_random_string(10)
+
+    register_new_automation(
+        project_id,
+        model_id,
+        test_client,
+        automation_id,
+        automation_name,
+        automation_revision_id,
+    )
+
+    automation_run_id = crypto_random_string(10)
+    function_id = crypto_random_string(10)
+    function_revision = crypto_random_string(10)
+    return AutomationRunData(
+        project_id=project_id,
+        model_id=model_id,
+        branch_name=branch_name,
+        version_id=version_id,
+        speckle_server_url=speckle_server_url,
+        automation_id=automation_id,
+        automation_revision_id=automation_revision_id,
+        automation_run_id=automation_run_id,
+        function_id=function_id,
+        function_name=crypto_random_string(10),
+        function_logo=None,
+    )
+
+
+def test_function_run(automation_run_data: AutomationRunData, speckle_token: str):
     """Run an integration test for the automate function."""
     automation_context = AutomationContext.initialize(
-        test_automation_run_data, test_automation_token
+        automation_run_data, speckle_token
     )
     automate_sdk = run_function(
         automation_context,
         automate_function,
         FunctionInputs(
-            forbidden_speckle_type="None",
-            whisper_message=SecretStr("testing automatically"),
+            forbidden_speckle_type="Base", whisper_message="testing automatically"
         ),
     )
 
-    assert automate_sdk.run_status == AutomationStatus.SUCCEEDED
+    assert automate_sdk.run_status == AutomationStatus.FAILED