tesseract-streamlit 0.1.0

Tesseract Streamlit automatically generates Streamlit apps from Tesseract APIs.

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.

⚙️ 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.