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.

Project links

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Credits

Author: The tesseract-streamlit team @ Pasteur Labs

GitHub Statistics

Data verified by PyPI on Aug 5, 2025
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

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Python <3.14, >=3.10

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