Skip to main content
When building AI applications, looking at data and labeling successes and failures is an important part of the development process. This is an example of a labeling workflow using the Oxen Python API to fetch rows from a data frame one by one, then writing the results back to the same data frame. We will be labeling the text of an SMS message as “spam” or “ham” depending on the content. The interface is built with native Marimo UI components. Feel free to download the code from this Notebook and run it in your own repository to follow along. The final result will look like this: User Interface

​
Fetching the Rows

The RemoteRepo class along with the DataFrame class make it easy to fetch and write data from a data frame in a repository. Specify the namespace, repository name, and path to the data frame in order to fetch data. In order to write data back to the data frame, we need to specify a workspace_name when instantiating the DataFrame class. This is because the data frame will be written back to a temporary workspace before being committed. This allows you to see the changes in the UI before writing them to the commit history. 
from oxen import RemoteRepo, DataFrame

# REPLACE WITH YOUR REPOSITORY
repo_name = "username/repo_name"
path = "data.tsv"
repo = RemoteRepo(repo_name)
remote_df = DataFrame(repo, path, workspace_name="labeling_workflow")
In order to fetch the rows, we can use the get_row method. This will return a Row object at the index specified. 
row = remote_df.get_row(0)
To know the number of rows in the data frame, we can use the size() function to determine the width and height of the data frame. 
width, height = remote_df.size()

​
Iterating through the data frame

Let’s add some helper functions to increment and decrement the index, and get the row at the current index. 
def increment_index():
    set_index(lambda v: v+1)

def decrement_index() -> int:
    set_index(lambda v: max(0, v - 1))

def get_row(remote_df, idx):
    data = remote_df.get_row(idx)
    return data[0]

​
Updating the Rows

The label_picker will call the update_category function when the user selects a new label. This function will update the category in the data frame. 
def update_category(remote_df, id, category):
    remote_df.update_row(id, {"category": category})

​
Setting up the UI

We will keep track of which row is being labeled using the mo.state reactive state variable. This sets up a getter and setter for the state variable. 
import marimo as mo

get_index, set_index = mo.state(0)
Then we can use a radio button for the categories and a few buttons to move between rows. 
# Get the current index from the state variable
idx = get_index()

# Get the row at the current index
row = remote_df.get_row(idx)

# Create a radio button for the user to select the label
label_picker = mo.ui.radio(
    ["spam", "ham"],
    value=data["category"],
    on_change=lambda v: update_category(remote_df, data['_oxen_id'], v),
)

# Create a button to move to the next row
next_button = mo.ui.button(label="next", on_change=lambda _: increment_index())

# Create a button to move to the previous row
previous_button = mo.ui.button(label="previous", on_change=lambda _: decrement_index())

# Display the UI
mo.vstack([
    mo.md("# Spam or Ham?"),
    mo.md(f"Example: {idx}"),
    mo.md(f"ID: {data['_oxen_id']}"),
    mo.md(f"[View Changes]({remote_df.workspace_url()})"),
    mo.ui.text_area(value=data['text'], full_width=True),
    label_picker,
    mo.hstack([previous_button, next_button], justify="center")
])

​
Viewing changes

The changes will be written back to a temporary workspace. We can view the changes by clicking the “View Changes” link in the UI. This is populated with the workspace_url method on the DataFrame class. 
# https://oxen.ai/{namespace}/{repo_name}/workspaces/{workspace_id}/file/{path}
remote_df.workspace_url()
Under the hood, we have indexed the data frame into a temporary read/write workspace. You can see the changes in the Oxen Diff UI and confirm them before committing. Oxen Diff Once you are happy with the changes, you can commit the changes to the data frame and they will be written back to the original data frame and added to the commit history. 
remote_df.commit("Updated the categories from the labeling tool")

​
Full Code

The full code is available here.