Merge pull request #1 from zenml-io/bugfix/line-length-fix

Fix line length bug

README.md

@@ -4,7 +4,7 @@ This repository contains a collection of templates from which a ZenML project
 can be generated: a collection of steps, pipelines, stack configurations and
 other artifacts and useful resources that can get you started with ZenML.
 
-🔥 **Do you have a personal project powered by ZenML that you would like to see here ?** At
+🔥 **Do you have a personal project powered by ZenML that you would like to see here?** At
 ZenML, we are looking for design partnerships and collaboration to help us
 better understand the real-world scenarios in which MLOps is being used and to
 build the best possible experience for our users. If you are interested in

copier.yaml

@@ -1,4 +1,4 @@
-# GLOBAL PROMPT --------------------------------
+--- # GLOBAL PROMPT --------------------------------
 
 template:
     type: str
@@ -42,39 +42,30 @@ email:
     when: "{{ open_source_license }}"
 auto_format:
     type: bool
-    help: >-
-        Auto-format the generated code with black, ruff and autoflake ? (NOTE:
-        you need to have these tools installed in your environment)
+    help: "Auto-format the generated code with black, ruff and autoflake? \n   (NOTE: you need to have these tools installed in your environment)"
     default: false
 
 # SUB-TEMPLATE PROMPT [starter] ---------------------
 use_step_params:
     type: bool
-    help: >-
-        Would you like to see step parameters being used in the generated ZenML
-        steps?
+    help: "Would you like to see step parameters being used in the generated \n   ZenML steps?"
     default: true
     when: "{{ template == 'starter' }}"
 use_custom_artifacts:
     type: bool
-    help: >-
-        Would you like to see custom artifact data types and materializers being
-        used in the generated ZenML steps and pipelines?
+    help: "Would you like to see custom artifact data types and materializers \n   being used in the generated ZenML steps and pipelines?"
     default: true
     when: "{{ template == 'starter' }}"
 configurable_dataset:
     type: bool
-    help: >-
-        Would you like to be able to select the dataset used in model training
-        at runtime through the CLI and as a data loader step parameter ?
+    help: "Would you like to be able to select the dataset used in model training\n   at runtime through the CLI and as a data loader step parameter?"
     default: true
     when: "{{ template == 'starter' and use_step_params }}"
 sklearn_dataset_name:
     type: str
-    help: >-
-        The generated ZenML project will be populated with some example code
-        featuring one of the UCI ML datasets from scikit-learn.
-        Which dataset would you like to see being used in the generated code?
+    help: "The generated ZenML project will be populated with some example code
+    \n   featuring one of the UCI ML datasets from scikit-learn. \n   Which
+    dataset would you like to see being used in the generated code?"
     choices:
         UCI Wine Data Set (classification): wine
         UCI Iris Data Set (classification): iris
@@ -83,6 +74,10 @@ sklearn_dataset_name:
     when: "{{ template == 'starter' }}"
 configurable_model:
     type: bool
+    help: "Would you like to be able to select the type of model used in model \n
+    training at runtime through the CLI and as a model trainer step \n
+    parameter?"
+    default: true
     help: >-
         Would you like to be able to select the type of model used in model
         training at runtime through the CLI and as a model trainer step
@@ -91,10 +86,8 @@ configurable_model:
     when: "{{ template == 'starter' and use_step_params }}"
 sklearn_model_name:
     type: str
-    help: >-
-        The generated ZenML project will be populated with some example code
-        featuring one of the scikit-learn classifier models.
-        Which model class would you like to see being used in the generated code?
+    help: "The generated ZenML project will be populated with some example code
+    \n   featuring one of the scikit-learn classifier models. \n   Which model class would you like to see being used in the generated code?"
     choices:
         Logistic Regression: LogisticRegression
         C-Support Vector Classification: SVC