[TIDY] Remove images from blueprint card architecture

test_use_cases.py

@@ -73,7 +73,7 @@
             "properties": {"label": {"type": "string"}, "link": {"type": "string"}},
         },
     },
-    "required": ["gtmId", "title", "description", "cardType", "imageName", "icon"],
+    "required": ["gtmId", "title", "description", "cardType", "icon"],
 }
 
 
@@ -91,7 +91,6 @@ def test_use_cases():
         assert card_is_valid
         gtm_ids.append(card.get("gtmId"))
         titles.append(card.get("title"))
-        validate_images_exist(card.get("imageName"))
         validate_icons_exist(card.get("icon"))
         validate_config_file_exists(card.get("defaultConfig"))
         validate_details_file_exists(card.get("detailsFileName"))
@@ -121,17 +120,6 @@ def validate_unique(field_values):
     assert len(field_values) == len(set(field_values))
 
 
-def validate_images_exist(image_name):
-    dir_path = "use_cases/images"
-    split_name = image_name.split(".")
-    two_x = split_name[0] + "@2x." + split_name[1]
-    three_x = split_name[0] + "@3x." + split_name[1]
-
-    assert (Path(__file__).parent / dir_path / image_name).is_file()
-    assert (Path(__file__).parent / dir_path / two_x).is_file()
-    assert (Path(__file__).parent / dir_path / three_x).is_file()
-
-
 def validate_icons_exist(icon_name):
     dir_path = "use_cases/icons"
     split_name = icon_name.split(".")

use_cases/gretel.json

@@ -5,7 +5,6 @@
       "title": "Generate synthetic tabular data",
       "description": "Create highly accurate synthetic tabular training data for ML models.",
       "cardType": "Console",
-      "imageName": "synthetics.png",
       "icon": "synthetics.png",
       "detailsFileName": "synthetic.md",
       "modelType": "synthetics",
@@ -33,7 +32,6 @@
       "title": "Synthesize any normalized SQL database ",
       "description": "Use Gretel Relational to synthesize or transform data in a relational database without losing foreign key relationships.",
       "cardType": "Notebook",
-      "imageName": "relational-db.png",
       "icon": "relational-db.png",
       "detailsFileName": "relational-db.md",
       "button1": {
@@ -51,7 +49,6 @@
       "description": "Safeguard sensitive information and uphold compliance standards by automatically removing private information from datasets.",
       "cardType": "Console",
       "tag": "New",
-      "imageName": "transform-v2.png",
       "icon": "transform.png",
       "modelType": "transform_v2",
       "modelCategory": "transform",
@@ -70,7 +67,6 @@
       "title": "Create provably private versions of sensitive data",
       "description": "Use Gretel Tabular DP, our graph-based generative model, to generate synthetic data with strong differential privacy guarantees.",
       "cardType": "Console",
-      "imageName": "tabular-dp.png",
       "icon": "tabular-dp.png",
       "modelType": "tabular_dp",
       "modelCategory": "synthetics",
@@ -89,7 +85,6 @@
       "title": "Generate synthetic text data using GPT",
       "description": "Quickly generate diverse text examples for natural language tasks, simplifying the generation of labeled datasets.",
       "cardType": "Console",
-      "imageName": "natural-lang-gpt.png",
       "icon": "natural-lang-gpt.png",
       "detailsFileName": "gpt-natural-language.md",
       "modelType": "gpt_x",
@@ -117,7 +112,6 @@
       "title": "Auto-tune synthetic data parameters",
       "description": "Automate hyperparameter sweeps to create the best synthetic data for your task.",
       "cardType": "Notebook",
-      "imageName": "benchmark.png",
       "icon": "benchmark.png",
       "detailsFileName": "gretel-tuner.md",
       "button1": {
@@ -134,7 +128,6 @@
       "title": "Generate high-quality time series data",
       "description": "Use DoppelGANger to create accurate and correlated time-series data.",
       "cardType": "Console",
-      "imageName": "timeseries.png",
       "icon": "timeseries.png",
       "modelType": "timeseries_dgan",
       "modelCategory": "synthetics",
@@ -153,7 +146,6 @@
       "title": "Synthesize data + evaluate ML performance",
       "description": "Evaluate synthetic data performance on classification and regression models.",
       "cardType": "Console",
-      "imageName": "downstream-accuracy.png",
       "icon": "downstream-accuracy.png",
       "modelType": "evaluate",
       "modelCategory": "synthetics",
@@ -172,7 +164,6 @@
       "title": "Synthesize data at scale",
       "description": "Create large volumes of synthetic records for demos and scale testing.",
       "cardType": "Console",
-      "imageName": "amplify.png",
       "icon": "amplify.png",
       "modelType": "amplify",
       "modelCategory": "synthetics",
@@ -191,7 +182,6 @@
       "title": "Boost minority class data",
       "description": "Train a model on a dataset with sparse instances of the minority class, and conditionally generate additional minority samples.",
       "cardType": "Notebook",
-      "imageName": "minority-classes.png",
       "icon": "minority-classes.png",
       "detailsFileName": "boost-minority-class.md",
       "button1": {
@@ -208,7 +198,6 @@
       "title": "Synthesize highly dimensional data",
       "description": "Train a synthetic ACTGAN model designed to easily handle datasets with 50k+ records and 100+ columns.",
       "cardType": "Console",
-      "imageName": "high-dimensional.png",
       "icon": "high-dimensional.png",
       "modelType": "actgan",
       "modelCategory": "synthetics",
@@ -227,7 +216,6 @@
       "title": "Evaluate classification and regression",
       "description": "Validate the quality of synthetic data for classification/regression tasks.",
       "cardType": "Notebook",
-      "imageName": "downstream-accuracy.png",
       "icon": "downstream-accuracy.png",
       "detailsFileName": "evaluate-downstream-accuracy-notebook.md",
       "button1": {
@@ -244,7 +232,6 @@
       "title": "Automatic anonymization for GDPR ",
       "description": "Use our Transform and Synthetics generative AI-based models to anonymize sensitive data to meet GDPR standards.",
       "cardType": "Notebook",
-      "imageName": "gdpr-anonymization.png",
       "icon": "gdpr-anonymization.png",
       "detailsFileName": "gdpr-anonymization.md",
       "button1": {
@@ -261,7 +248,6 @@
       "title": "Benchmark multiple synthetic models",
       "description": "Evaluate multiple synthetic data models on a standardized benchmark with a single function.",
       "cardType": "Notebook",
-      "imageName": "benchmark.png",
       "icon": "benchmark.png",
       "detailsFileName": "benchmark.md",
       "button1": {
@@ -278,7 +264,6 @@
       "title": "Generate text including song lyrics using GPT",
       "description": "Create natural language text data including song lyrics.",
       "cardType": "Console",
-      "imageName": "natural-lang-gpt.png",
       "icon": "natural-lang-gpt.png",
       "modelType": "gpt_x",
       "modelCategory": "synthetics",
@@ -297,7 +282,6 @@
       "title": "Remove sensitive data from unstructured text",
       "description": "Use Named Entity Recognition (NER) to detect and redact personally identifiable data in free text fields.",
       "cardType": "Console",
-      "imageName": "transform-v1.png",
       "icon": "transform.png",
       "modelType": "transform",
       "modelCategory": "transform",
@@ -317,7 +301,6 @@
       "description": "Fine-tune Gretel Image Synthetics on your domain specific images",
       "cardType": "Notebook",
       "tag": "Labs",
-      "imageName": "image-synthetics.png",
       "icon": "image-synthetics.png",
       "detailsFileName": "image-synthetics-preview.md",
       "button1": {