new datasets and docs change

docs/credits.mdx

@@ -22,6 +22,8 @@ dependencies from the AgML project configuration.
 | dict2xml | >=1.7.6 |
 | ipywidgets | >=8.1.5 |
 | rich | >=14.0.0 |
+| datasets | * |
+| transformers | * |
 
 ## Development dependencies
 
@@ -40,21 +42,4 @@ dependencies from the AgML project configuration.
 | coverage | >=7.6.1 |
 | interrogate | >=1.7.0 |
 
-## Docs dependencies
-
-| Project | Version |
-| --- | --- |
-| mkdocs | >=1.5.0 |
-| mkdocs-material | >=9.5.0 |
-| markdown-callouts | >=0.4.0 |
-| markdown-exec | >=1.7.0 |
-| mkdocs-gen-files | >=0.5.0 |
-| mkdocs-literate-nav | >=0.6.0 |
-| mkdocs-coverage | >=1.0.0 |
-| mkdocstrings | >=0.24.0 |
-| mkdocstrings-python | >=1.8.0 |
-| mkdocs-git-revision-date-localized-plugin | >=1.2.0 |
-| mkdocs-minify-plugin | >=0.8.0 |
-
-For the fully generated credits (including resolved versions and licenses), refer to the
-upstream generator in the AgML repository at `scripts/docs/gen_credits.py`.
+For the full dependency list with resolved versions and licenses, see [pyproject.toml](https://github.com/Project-AgML/AgML/blob/main/pyproject.toml) in the AgML repository.

docs/index.mdx

@@ -108,6 +108,10 @@ train_ds.as_torch_dataset()
 
 You're now ready to use AgML for training your own models! Luckily, AgML comes with a training module that enables quick-start training of standard deep learning models on agricultural datasets. Training a grape detection model is as simple as the following code:
 
+:::warning Deprecation Notice
+`agml.models` is deprecated and will be removed in a future release. The example below still works but will emit a `DeprecationWarning` at import time.
+:::
+
 ```python
 import agml
 import agml.models
@@ -126,6 +130,29 @@ model = agml.models.DetectionModel(num_classes=loader.num_classes)
 model.run_training(loader)
 ```
 
+## HuggingFace Integration
+
+AgML now includes a `HuggingFaceDataLoader` for loading datasets directly from the [Hugging Face Hub](https://huggingface.co/datasets) into native HuggingFace `DatasetDict` objects, compatible with `transformers` and `diffusers` pipelines.
+
+```python
+from agml.data import HuggingFaceDataLoader
+
+# Load a dataset from the Hub
+loader = HuggingFaceDataLoader("org/dataset-name")
+
+# Load a specific config/subset
+loader = HuggingFaceDataLoader("org/dataset-name", config="augmented")
+
+# Split into train/val/test with optional stratification
+dataset = loader.split(val_size=0.1, test_size=0.1, stratify_cols="label")
+# Returns a DatasetDict with 'train', 'val', and 'test' splits
+
+# Access the underlying DatasetDict at any time
+dataset = loader.dataset
+```
+
+The `HuggingFaceDataLoader` automatically casts image-like columns to the HuggingFace `Image` type for decoded pixel access. Stratified splitting is supported across one or more columns using a composite key.
+
 ## Public Datasets
 
 AgML contains a wide variety of public datasets from various locations across the world: