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remove images from blueprint card architecture (#337)
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thesteady authored Jan 18, 2024
1 parent d1c5b1c commit 778c742
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Showing 50 changed files with 1 addition and 30 deletions.
14 changes: 1 addition & 13 deletions test_use_cases.py
Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@
"properties": {"label": {"type": "string"}, "link": {"type": "string"}},
},
},
"required": ["gtmId", "title", "description", "cardType", "imageName", "icon"],
"required": ["gtmId", "title", "description", "cardType", "icon"],
}


Expand All @@ -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"))
Expand Down Expand Up @@ -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(".")
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17 changes: 0 additions & 17 deletions use_cases/gretel.json
Original file line number Diff line number Diff line change
Expand Up @@ -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",
Expand Down Expand Up @@ -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": {
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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": {
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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": {
Expand All @@ -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",
Expand All @@ -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": {
Expand All @@ -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": {
Expand All @@ -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": {
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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": {
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