Add User information in ModelCard

README.md

@@ -16,20 +16,15 @@ The Patra Toolkit is a component of the Patra ModelCards framework designed to s
 
 ## Features
 
-1. **Encourages Accountability**  
-   - Incorporate essential model information (metadata, dataset details, fairness, explainability) at training time, ensuring AI models remain transparent from development to deployment.
+- **Encourages Accountability**: Incorporate essential model information (metadata, dataset details, fairness, explainability) at training time, ensuring AI models remain transparent from development to deployment.
 
-2. **Semi-Automated Capture**  
-   - Automated *Fairness* and *Explainability* scanners compute demographic parity, equal odds, SHAP-based feature importances, etc., for easy integration into Model Cards.
+- **Semi-Automated Capture**: Automated *Fairness* and *Explainability* scanners compute demographic parity, equal odds, SHAP-based feature importances, etc., for easy integration into Model Cards.
 
-3. **Machine-Actionable Model Cards**  
-   - Produce a structured JSON representation for ingestion into the Patra Knowledge Base. Ideal for advanced queries on model selection, provenance, versioning, or auditing.
+- **Machine-Actionable Model Cards**: Produce a structured JSON representation for ingestion into the Patra Knowledge Base. Ideal for advanced queries on model selection, provenance, versioning, or auditing.
 
-4. **Flexible Repository Support**  
-   - Pluggable backends for storing models/artifacts on **Hugging Face** or **GitHub**, unifying the model publishing workflow.
+- **Flexible Repository Support**: Pluggable backends for storing models/artifacts on **Hugging Face** or **GitHub**, unifying the model publishing workflow.
 
-5. **Versioning & Model Relationship Tracking**  
-   - Maintain multiple versions of a model with recognized edges (e.g., `revisionOf`, `alternateOf`) using embedding-based similarity. This ensures clear lineages and easy forward/backward provenance.
+- **Versioning & Model Relationship Tracking**: Maintain multiple versions of a model with recognized edges (e.g., `revisionOf`, `alternateOf`) using embedding-based similarity. This ensures clear lineages and easy forward/backward provenance.
 
 ## Getting Started
 
@@ -52,20 +47,28 @@ Find the descriptions of the Model Card parameters in the [schema descriptions d
 from patra_toolkit import ModelCard
 
 mc = ModelCard(
-  name="UCI Adult Data Analysis model using Tensorflow",
-  version="0.1",
-  short_description="UCI Adult Data analysis using Tensorflow for demonstration of Patra Model Cards.",
-  full_description="We have trained a ML model using the tensorflow framework to predict income for the UCI Adult Dataset. We leverage this data to run the Patra model cards to capture metadata about the model as well as fairness and explainability metrics.",
-  keywords="uci adult, tensorflow, explainability, fairness, patra",
-  author="Sachith Withana",
-  input_type="Tabular",
-  category="classification",
-  foundational_model="None"
+    name="UCI Adult Data Analysis model using Tensorflow",
+    version="0.1",
+    short_description="UCI Adult Data analysis using Tensorflow for demonstration of Patra Model Cards.",
+    full_description="We have trained a ML model using the tensorflow framework to predict income for the UCI Adult Dataset. We leverage this data to run the Patra model cards to capture metadata about the model as well as fairness and explainability metrics.",
+    keywords="uci adult, tensorflow, explainability, fairness, patra",
+    input_type="Tabular",
+    category="classification",
+    foundational_model="None"
 )
 
 # Add Model Metadata
 mc.input_data = 'https://archive.ics.uci.edu/dataset/2/adult'
 mc.output_data = 'https://huggingface.co/Data-to-Insight-Center/UCI-Adult'
+
+# Add User Information
+mc.populate_user(
+    username="neelk",
+    orcid="0000-0002-1234-5678",
+    name="Neelesh Karthikeyan",
+    institution="Indiana University",
+    email="neelk@iu.edu"
+)
 ```
 
 ### Initialize an AI/ML Model
@@ -117,7 +120,7 @@ mc.validate()
 mc.save(<file_path>)
 ```
 
-## Submit
+## Submit Model Card
 
 Use `mc.submit()` to either upload just a model card, an AI model along with the model card, just the artifacts, or all at once!
 
@@ -139,7 +142,7 @@ If a name-version conflict arises, increment `mc.version`. In case of failure, `
 
 ---
 
-## Authentication with TACC Credentials
+### Authentication with TACC Credentials
 
 To authenticate against a Patra server hosted in TAPIS, use Patra's built-in `authenticate()` method to obtain an access token:
 
@@ -149,11 +152,7 @@ from patra_toolkit import ModelCard
 mc = ModelCard(...)
 
 tapis_token = mc.authenticate(username="<your_tacc_username>", password="<your_tacc_password>")
-```
-
-This will print and return a valid `X-Tapis-Token` (JWT). You can then pass this token to `mc.submit()`:
 
-```python
 mc.submit(
     patra_server_url=<tapis_hosted_patra_server_url>,
     model=<trained_model>,

docs/index.rst

@@ -60,30 +60,36 @@ Usage
 
 Create a Model Card
 ^^^^^^^^^^^^^^^^^^^
-
-Find the descriptions of the Model Card parameters in the
-``docs/schema_description.md``.
+Find the descriptions of the Model Card parameters in the `schema descriptions document <./docs/schema_description.md>`_.
 
 .. code-block:: python
 
    from patra_toolkit import ModelCard
 
    mc = ModelCard(
-     name="UCI Adult Data Analysis model using Tensorflow",
-     version="0.1",
-     short_description="UCI Adult Data analysis using Tensorflow for demonstration of Patra Model Cards.",
-     full_description="We have trained a ML model using the tensorflow framework to predict income for the UCI Adult Dataset. We leverage this data to run the Patra model cards to capture metadata about the model as well as fairness and explainability metrics.",
-     keywords="uci adult, tensorflow, explainability, fairness, patra",
-     author="Sachith Withana",
-     input_type="Tabular",
-     category="classification",
-     foundational_model="None"
+       name="UCI Adult Data Analysis model using Tensorflow",
+       version="0.1",
+       short_description="UCI Adult Data analysis using Tensorflow for demonstration of Patra Model Cards.",
+       full_description="We have trained a ML model using the tensorflow framework to predict income for the UCI Adult Dataset. We leverage this data to run the Patra model cards to capture metadata about the model as well as fairness and explainability metrics.",
+       keywords="uci adult, tensorflow, explainability, fairness, patra",
+       input_type="Tabular",
+       category="classification",
+       foundational_model="None"
    )
 
    # Add Model Metadata
    mc.input_data = 'https://archive.ics.uci.edu/dataset/2/adult'
    mc.output_data = 'https://huggingface.co/Data-to-Insight-Center/UCI-Adult'
 
+   # Add User Information
+   mc.populate_user(
+       username="neelk",
+       orcid="0000-0002-1234-5678",
+       name="Neelesh Karthikeyan",
+       institution="Indiana University",
+       email="neelk@iu.edu"
+   )
+
 Initialize an AI/ML Model
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -141,22 +147,50 @@ Validate and Save the Model Card
 Submit
 ------
 
-Use ``mc.submit()`` to register only a model card, an AI model along with the model card,
-just the artifacts, or all at once!
+Use ``mc.submit()`` to either upload just a model card, an AI model along with the model card, just the artifacts, or all at once!
 
 .. code-block:: python
 
    mc.submit(
        patra_server_url=<patra_server_url>,
        model=<trained_model>,
-       file_format="pt", # or "h5"
-       model_store="huggingface", # or "github"
+       file_format="pt",  # or "h5"
+       model_store="huggingface",  # or "github"
        inference_labels="labels.txt",
-       artifacts=[<artifact1_path>, <artifact2_path>]
+       artifacts=[<artifact1_path>, <artifact2_path>],
+       token=<optional_token>  # optional authentication token
+   )
+
+The ``token`` parameter is **optional**. If your hosted Patra server requires authentication, provide a valid token.
+
+If a name-version conflict arises, increment ``mc.version``. In case of failure, ``submit()`` attempts partial rollbacks to avoid orphaned uploads.
+
+----
+
+Authentication with TACC Credentials
+====================================
+
+To authenticate against a Patra server hosted in TAPIS, use Patra's built-in ``authenticate()`` method to obtain an access token:
+
+.. code-block:: python
+
+   from patra_toolkit import ModelCard
+
+   mc = ModelCard(...)
+
+   tapis_token = mc.authenticate(username="<your_tacc_username>", password="<your_tacc_password>")
+
+This will print and return a valid ``X-Tapis-Token`` (JWT). You can then pass this token to ``mc.submit()``:
+
+.. code-block:: python
+
+   mc.submit(
+       patra_server_url=<tapis_hosted_patra_server_url>,
+       model=<trained_model>,
+       token=tapis_token
    )
 
-If a name-version conflict arises, increment ``mc.version``. In case of failure,
-``submit()`` attempts partial rollbacks to avoid orphaned uploads.
+----
 
 Examples
 --------

examples/notebooks/CNN_Example.ipynb

@@ -306,7 +306,6 @@
     "            short_description=\"CNN model trained on CIFAR-10 dataset for image classification.\",\n",
     "            full_description=\"We have trained a Convolutional Neural Network (CNN) model using TensorFlow to classify images from the CIFAR-10 dataset. The dataset consists of 10 classes of images, including airplane, automobile, bird, cat, deer, dog, frog, horse, ship,and truck.\",\n",
     "            keywords=\"cifar-10, tensorflow, cnn, image classification, deep learning\",\n",
-    "            author=\"Isuru Gamage\",\n",
     "            input_type=\"Image\",\n",
     "            category=\"classification\",\n",
     "            foundational_model=\"None\"\n",