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setup.py

@@ -1,9 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name="autocatalogai",
-    version="0.1.0",
-    author="Md Mohsin",
-    author_email="siam.mohsin2005@gmail.com",
-    packages=find_packages()
-)

template.py

@@ -1,80 +0,0 @@
-import os
-from pathlib import Path
-
-project_name = "AutoCatalogAI"
-list_of_files = [
-    f"{project_name}/app/app.py",
-    f"{project_name}/app/inference.py",
-    f"{project_name}/app/templates/index.html",
-
-    f"{project_name}/autocatalog/__init__.py",
-
-    f"{project_name}/autocatalog/data/__init__.py",
-    f"{project_name}/autocatalog/data/dataset.py",
-    f"{project_name}/autocatalog/data/preprocessing.py",
-
-    f"{project_name}/autocatalog/models/__init__.py",
-    f"{project_name}/autocatalog/models/multitask_clip.py",
-    f"{project_name}/autocatalog/models/heads.py",
-
-    f"{project_name}/autocatalog/training/__init__.py",
-    f"{project_name}/autocatalog/training/train.py",
-    f"{project_name}/autocatalog/training/losses.py",
-
-    f"{project_name}/autocatalog/evaluation/__init__.py",
-    f"{project_name}/autocatalog/evaluation/evaluate.py",
-    f"{project_name}/autocatalog/evaluation/metrics.py",
-    f"{project_name}/autocatalog/evaluation/error_analysis.py",
-
-    f"{project_name}/autocatalog/inference/__init__.py",
-    f"{project_name}/autocatalog/inference/predictor.py",
-    f"{project_name}/autocatalog/inference/catalog_generator.py",
-
-    f"{project_name}/autocatalog/utils/__init__.py",
-    f"{project_name}/autocatalog/utils/config.py",
-    f"{project_name}/autocatalog/utils/logger.py",
-    f"{project_name}/autocatalog/utils/seed.py",
-
-    f"{project_name}/configs/config.yaml",
-
-    f"{project_name}/data/processed/train.csv",
-    f"{project_name}/data/processed/val.csv",
-    f"{project_name}/data/processed/test.csv",
-
-    f"{project_name}/artifacts/models/.gitkeep",
-    f"{project_name}/artifacts/evaluation/.gitkeep",
-    f"{project_name}/artifacts/plots/.gitkeep",
-    f"{project_name}/artifacts/examples/.gitkeep",
-
-    f"{project_name}/notebooks/01_dataset_experiment.ipynb",
-
-    f"{project_name}/scripts/prepare_dataset.py",
-    f"{project_name}/scripts/train_baseline.py",
-    f"{project_name}/scripts/train_multitask_clip.py",
-    f"{project_name}/scripts/evaluate_model.py",
-    f"{project_name}/scripts/predict_image.py",
-
-    f"{project_name}/tests/test_dataset.py",
-    f"{project_name}/tests/test_model.py",
-    f"{project_name}/tests/test_inference.py",
-
-    f"{project_name}/.gitignore",
-    f"{project_name}/README.md",
-    f"{project_name}/model_card.md",
-    f"{project_name}/requirements.txt",
-    f"{project_name}/Dockerfile",
-    f"{project_name}/setup.py",
-]
-
-for filepath in list_of_files:
-    filepath = Path(filepath)
-    filedir, filename = os.path.split(filepath)
-
-    if filedir:
-        os.makedirs(filedir, exist_ok=True)
-
-    if not filepath.exists():
-        filepath.touch()
-        print(f"Created: {filepath}")
-    else:
-        print(f"Already exists: {filepath}")

tests/test_catalog_generator.py

@@ -0,0 +1,26 @@
+from autocatalog.inference.catalog_generator import (
+    generate_catalog_output,
+)
+
+
+def test_catalog_output():
+    predictions = {
+        "gender": "Men",
+        "masterCategory": "Apparel",
+        "subCategory": "Topwear",
+        "articleType": "Tshirts",
+        "baseColour": "Black",
+        "season": "Summer",
+        "usage": "Casual",
+    }
+
+    output = generate_catalog_output(predictions)
+
+    assert output["suggested_title"] == (
+        "Men Black Casual Tshirts"
+    )
+
+    assert "black tshirts" in output["search_tags"]
+
+    assert output["json_export"]["category"] == "Apparel"
+    assert output["json_export"]["color"] == "Black"

tests/test_preprocessing.py

@@ -0,0 +1,21 @@
+import numpy as np
+from PIL import Image
+
+from autocatalog.data.preprocessing import (
+    COLOR_FEATURE_DIM,
+    extract_color_features,
+)
+
+
+def test_color_feature_shape_and_values():
+    image = Image.new(
+        "RGB",
+        (128, 128),
+        color=(255, 0, 0),
+    )
+
+    features = extract_color_features(image)
+
+    assert features.shape == (COLOR_FEATURE_DIM,)
+    assert features.dtype == np.float32
+    assert np.isfinite(features).all()