Tesseract Streamlit¶

A CLI tool that generates a Streamlit app from a running Tesseract instance. The generated app allows users to interactively submit input to the apply endpoint and optionally visualise results using custom plotting functions.

✨ Features¶

🔍 Parses Tesseract’s OpenAPI schema
⚙️ Automatically builds input forms for the apply endpoint
📊 Supports user-defined plotting of inputs and outputs
🚀 Outputs a ready-to-run Streamlit app script
🧩 Modular and customizable with minimal boilerplate

🛠 Requirements¶

Modern UNIX based OS (Linux or macOS), or Windows with WSL
Python >= 3.10
tesseract-core

📦 Installation¶

$ pip install tesseract-streamlit

🧰 Usage¶

$ tesseract-streamlit [OPTIONS] URL OUTPUT

URL: The address to the Tesseract instance you want to interface with.
OUTPUT: The file path to write the generated Streamlit app.

For example:

# Install dependencies
$ pip install tesseract-core tesseract-streamlit

# Clone Tesseract Core and build example Tesseract
$ git clone https://github.com/pasteurlabs/tesseract-core.git
$ tesseract build tesseract-core/examples/vectoradd

# Serve Tesseract
$ tesseract serve vectoradd --port 61725

# Connect via tesseract-streamlit
$ tesseract-streamlit http://127.0.0.1:61725 app.py
$ streamlit run app.py

⚙️ Options¶

Option	Description
`--user-code, -u`	(Optional) Path to Python file with plotting functions
`--help`	Show the help message and exit

📊 With Custom Plotting¶

You can optionally pass a Python file containing user-defined functions for plotting inputs and/or outputs.

$ tesseract-streamlit --user-code udf.py http://localhost:48819 app.py

The udf.py file should define functions like:

import plotly.graph_objects as go

def plot(inputs, outputs) -> go.Figure:
    """Title of the plot.

    Description of what the plot conveys. Will be displayed above the
    plot itself.
    """
    fig = go.Figure()
    # some plotly logic to visualise input_schema and output_schema
    return fig

Where we have chosen Plotly as our plotting back-end, but any supported library by Streamlit is allowed.

[!NOTE] Additionally, we support PyVista plots (thanks to edsaac/stpyvista)! Just annotate your function to return a pyvista.Plotter instance, and an interactive plot will be inserted.

⚙️ More info on defining custom plotting functions

Custom plotting is easy and flexible. Here’s how to make the most of it:

Function names don’t matter, so name them however you like.
Define multiple functions to visualise more than one aspect of the data. Each one will generate a separate plot in the Streamlit app.
Add a docstring to each function to add descriptive text in the app:
- First line of the docstring will appear as the plot title.
- Remaining lines will be shown as a description below the title.
- Omitting docstrings is allowed, but raises a UserDefinedFunctionWarning.
Public functions must include either inputs, outputs, or both as parameter names. Any public function that doesn’t use these names will raise a UserDefinedFunctionError.
Private functions may be defined with a leading underscore in their name, eg. def _foo(x: float) -> float: ....
- Arbitrary parameters and return types are allowed.
- Will not produce plots directly in the Web UI.
- Can be called from within your public plotting functions.

This setup gives you control over what to display and how to explain it, directly from your code.

📁 Example¶

See the example README for a basic example walk-through, or simply run the following script to see the end result!

$ bash examples/vectoradd_jax/run.sh

This will open a browser window with the Streamlit UI where users can input values and visualise the response.

⚠️ Current Limitations¶

While tesseract-streamlit supports Tesseracts with an InputSchema formed with arbitrary nesting of Pydantic models, it does not yet support nesting Pydantic models inside native Python collection types such as:

❌ list[Model]
❌ tuple[Model, ...]
❌ dict[str, Model]

These types will raise an error or be ignored in the generated app.

You can however use these native collections with basic Python types, such as:

✅ list[str]
✅ dict[str, int]
✅ tuple[float, float]

If you would like to request support for nested models within collections in a future release, please let us know.