# API Documentation

For the full Python API documentation, please go to <https://api-docs.seclea.com>.&#x20;

If you just want to get straight into though, we have the basics here just for you!

### Installation

To install the `seclea_ai` package, run the following command:

```python
pip install seclea_ai
```

### Initialisation

To use the `seclea_ai` API, first import the `SecleaAI` class and create an instance with your project and organisation details:

```python
from seclea_ai import SecleaAI 

# NOTE - use the organisation name provided to you from which you received credentials. 
seclea = SecleaAI(project_name="Your AI Project Name", organization='')
```

### Uploading Datasets

To upload a dataset to the Seclea Platform, use the `upload_dataset()` method, providing the dataset, dataset name, and metadata:

```python
import pandas as pd 

# Load the data 
data = pd.read_csv('your_data.csv', index_col="index_column") 

# Define the metadata for the dataset. 
dataset_metadata = { 
# ... 
} 

seclea.upload_dataset(dataset=data, dataset_name="Your Dataset Name", metadata=dataset_metadata)
```

#### Uploading Datasets - as separate samples and labels

To upload dataset that is split into samples and labels, use the `upload_dataset_split()` method:

```python
# Upload the train and test dataset splits
seclea.upload_dataset_split(
    X=X_train, 
    y=y_train, dataset_name="Your Dataset Name - Train", 
    metadata={}, 
    transformations=train_transformations
    ) 
```

### Applying Dataset Transformations

To apply and record dataset transformations, use the `DatasetTransformation` class from the `seclea_ai.transformations` module:

<pre class="language-python"><code class="lang-python">from seclea_ai.transformations import DatasetTransformation 

<strong># Define the updates to the metadata 
</strong>processed_metadata = { 
# ... 
} 

# Define the transformations to the dataset 
processing_transformations = [ 
    DatasetTransformation(
        # Define the dataset transformations
        ) 
] 

# Upload the processed datasets
seclea.upload_dataset(dataset=processed_data, 
                    dataset_name="Your Processed Dataset Name", 
                    metadata=processed_metadata, 
                    transformations=processing_transformations
                    )
</code></pre>

### Training and Uploading Models

To upload a model using the `seclea_ai` API, follow these steps:

```python
from sklearn.model_selection import cross_val_score 
from sklearn.ensemble import RandomForestClassifier 

# Initialize a classifier 
classifier = RandomForestClassifier() 

# Cross-validate the classifier 
training_score = cross_val_score(classifier, X_train, y_train, cv=5) 

# Train the classifier on the full training set 
classifier.fit(X_train, y_train) 

# Upload the fully trained model
seclea.upload_training_run_split(model=classifier, 
                                X_train=X_train, 
                                y_train=y_train, 
                                X_test=X_test, 
                                y_test=y_test)
```

Note that this uses the `upload_training_run_split` function that takes datasets as samples and labels. If you prefer to reference a dataset that isn't split in this way you can use the `upload_training_run` function instead.

### Conclusion

By following the steps outlined in this documentation, you can efficiently integrate the `seclea_ai` API into your AI project, enabling seamless data and model management, as well as regulatory compliance and risk management through the Seclea Platform.

\ <br>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://documentations.seclea.com/seclea-user-documentation/python-api-seclea-ai/api-documentation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.