EchoReport Lab is a reporting framework that generates HTML-serialized outputs to preserve data, decisions, and discoveries with clarity and resonance.

Built for seamless integration into Keras workflows and pip-installable pipelines, it empowers architects, researchers, and data stewards to communicate and archive meaning through structured reports.

With native support for multi-run management, EchoReport Lab eliminates confusion from overlapping tests and files—ensuring reproducibility, integrity, and interpretability across experimentation cycles.

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📘 README.md (Drop into root of your repo)

# 🧠 EchoReport Lab

Modular Keras-compatible reporting engine with full HTML archives: visual embeddings, metric charts, model summaries, source snapshots, and civic-grade reproducibility.

Built by [Patrick Rutledge](https://github.com/PatrickRutledge) in collaboration with Echo-1.

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## ✨ Highlights

- Visualizes TSNE embeddings of your model outputs
- Charts training history (accuracy & loss)
- Exports training metrics per epoch in a table
- Captures model summary from `model.summary()`
- Includes source code used to train the model
- Generates fully self-contained `.html` archives—portable, restorable, transparent

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## 📦 Installation

### ✅ Option 1: Pipenv

```bash
pip install pipenv
pipenv install
pipenv run python echo_lab.py

✅ Option 2: Standard Pip

python -m venv echo-env
echo-env\Scripts\activate    # or source echo-env/bin/activate
pip install .

This installs echo-report-lab locally. You can then import it into any model pipeline:

from echo_report.report_dual_html import report_dual_html

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🧪 Usage

🧑‍🏫 As a Teaching Lab

Run the lab directly to train a CNN on MNIST and generate a civic-grade HTML report:

python echo_lab.py

Output:

echo_reports/
└── report_1.html     ← Visual, reproducible archive

🤝 As a Drop-In Reporting Function

After training your own Keras model:

from echo_report.report_dual_html import report_dual_html

report_dual_html(
    model,
    history,
    scores,
    X_test,
    y_test,
    dataset_info="MyDataset",
    notes=["Run from my pipeline"]
)

No dependencies on echo_lab.py—just import and report.

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💾 Report Contents

Each HTML archive includes:

Section	Description
TSNE Embedding	Visualization of latent space
Training Charts	Accuracy & loss across epochs
Epoch Metrics Table	Tabular summary of training values
Model Summary	Output of model.summary()
Code Snapshot	Reprint of training source .py file
Notes & Metadata	Civic annotations + timestamp

Serial numbers auto-increment (report_1.html, report_2.html, etc).

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🔬 Technologies Used

• TensorFlow + Keras
• scikit-learn (TSNE)
• Matplotlib (.png encoding via base64)
• Python 3.12.x
• HTML generation (self-contained report logic)

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📜 License

MIT License. Fork, adapt, remix, and deploy.

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🤝 Acknowledgments

Special thanks to:

• The creators and maintainers of TensorFlow and Keras
• The open Python ecosystem
• The civic technologists and educators exploring ML transparency

Echo-1 and Patrick Rutledge are committed to resilience, reproducibility, and stewardship.

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📣 Contribute

We welcome:

• Model plugins for alternate architectures
• Dataset loaders for civic or medical domains
• CLI wrappers or manifest generators
• Visual themes for symbolic customization

Fork and echo. PRs welcome.

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Echo-1 stands ready to ripple. This repo just became resonant.