README.md

metadata

title: GeneTypeClassifier – ML Model for Gene Sequence Classification
emoji: 🧬
colorFrom: purple
colorTo: green
sdk: streamlit
sdk_version: 1.40.0
app_file: app.py
pinned: false
license: mit

GeneTypeClassifier — Fast gene type prediction with a trained Gradient Boosting pipeline

A lightweight Gradio app to classify gene records using a pre-trained Gradient Boosting model. Point it at a nucleotide sequence and a short description, and get a predicted gene type.

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Table of Contents

• Demo
• Features
• Installation / Setup
• Usage
• Configuration / Options
• Contributing
• License
• Acknowledgements / Credits

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Demo

Below are real assets from ./demo/:

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Features

• Pretrained model: Ships with models/gradient_boosting_pipeline.pkl and models/label_encoder.pkl.
• Simple UI: Gradio interface for quick local testing and sharing.
• Deterministic preprocessing: get_kmers() utility for k-mer tokenization baked into the pipeline serialization context.
• Reproducible setup: Minimal, pinned requirements.txt.

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Installation / Setup

# Create a virtual environment
python -m venv .venv

# Activate it
# On Linux/Mac:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

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Usage

Run the Gradio app locally:

python app.py

This launches the UI defined in app.py/ui.py and loads the pretrained artifacts from models/:

• models/gradient_boosting_pipeline.pkl
• models/label_encoder.pkl

In the UI, provide:

• Nucleotide Sequence (e.g., ATG...)
• Description

The app returns the predicted gene type (e.g., PROTEIN_CODING, ncRNA, etc.).

Note: The pickled pipeline expects the helper get_kmers() from utils.py. Keep the file layout unchanged when running the app.

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Configuration / Options

• Model paths: The UI loads from models/. To swap models, replace the .pkl files with compatible artifacts and keep the filenames or update the paths in ui.py (pipeline and label_encoder loaders).
• Gradio server options: To customize host/port, edit demo.launch() in app.py, e.g. demo.launch(server_name="0.0.0.0", server_port=7860).

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Contributing

• Issues & ideas: Open an issue describing the change and rationale.
• PRs: Keep changes focused, add clear descriptions, and update docs if behavior changes.
• Style: Prefer small, readable functions and explicit dependencies.

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License

This project is licensed under the MIT License.

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Acknowledgements / Credits

• Built with Gradio for the UI and scikit-learn for the model pipeline. 🧬🚀