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Swapin Vidya
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feat: initial architectural release of BioGraph-Edge-Quantizer
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‎.gitignore‎

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# Python / core_quantizer
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__pycache__/
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*.py[cod]
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*$py.class
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venv/
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.pytest_cache/
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core_quantizer/models/*.pt
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!core_quantizer/models/.gitkeep
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# Laravel / api_gateway
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api_gateway/vendor/
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api_gateway/node_modules/
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api_gateway/public/storage
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api_gateway/public/hot
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api_gateway/storage/*.key
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api_gateway/.env
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api_gateway/.phpunit.result.cache
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api_gateway/database/*.sqlite
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api_gateway/database/*.sqlite-journal
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# General
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.DS_Store
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.idea/
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.vscode/
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*.swp

‎CITATION.cff‎

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cff-version: 1.2.0
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message: "If you use this software, please cite it as below."
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authors:
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- family-names: Vidya
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given-names: Swapin
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orcid: "https://orcid.org/0009-0009-5758-3845"
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title: "BioGraph-Edge-Quantizer: Resource-Aware GNN Quantization for Clinical Edge Intelligence"
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version: 1.0.0
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date-released: 2026-05-02
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url: "https://github.com/[your-username]/BioGraph-Edge-Quantizer"
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keywords:
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- "Graph Neural Networks"
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- "Model Quantization"
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- "Edge Computing"
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- "FHIR"
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- "Bioinformatics"

‎CONTRIBUTING.md‎

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# Contributing to BioGraph-Edge-Quantizer
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Thank you for your interest in contributing to this project. We welcome contributions that improve model quantization efficiency, clinical data interoperability, or edge deployment strategies.
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## Architectural Standards
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This project maintains a strict separation between the **ML Core** and the **API Gateway**.
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* **Core (Python):** Must follow the `src/` layout and include `pytest` suites for any new optimization logic.
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* **Gateway (Laravel):** Must adhere to PSR-12 coding standards and maintain FHIR-compliant data structures.
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## How to Contribute
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### 1. Reporting Bugs
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* Ensure the bug is reproducible in the current `venv` or `composer` environment.
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* Open an issue with a clear description and steps to reproduce.
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### 2. Feature Requests
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* Features should align with the goal of **deterministic, edge-native intelligence**.
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* Please open an issue to discuss major architectural changes before starting work.
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### 3. Pull Requests
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1. Fork the repository and create your branch from `main`.
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2. **ML Changes:** Ensure `python -m pytest` passes with 100% success.
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3. **API Changes:** Ensure `php artisan test` (if applicable) and GraphQL resolvers are validated.
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4. Update the `README.md` if your change introduces new dependencies or configuration requirements.
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5. Maintain a clean commit history.
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## ⚖️ Code of Conduct
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Contributors are expected to maintain professional and technical excellence. We prioritize clarity, efficiency, and data sovereignty in all technical discussions.
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## License
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By contributing, you agree that your contributions will be licensed under the project's MIT License.

‎README.md‎

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# BioGraph-Edge-Quantizer
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**Lead Architect:** Swapin Vidya
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**ORCID:** [0009-0009-5758-3845](https://orcid.org/0009-0009-5758-3845)
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**Email:** swapin@peachbot.in
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**Professional Context:** Senior Systems Architect and Backend Developer; research developed during academic sabbatical.
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A deterministic framework for optimizing **Graph Neural Networks (GNNs)** for biological network analysis on edge hardware.
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## System Architecture
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* **`core_quantizer/`**: Python environment for GNN optimization using **Edge-GNN** principles.
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* **`api_gateway/`**: PHP/Laravel 12 implementation for serving inference results via a **FHIR-compliant** GraphQL interface.
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## Setup & Initialization
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### 1. ML Core (Python)
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```bash
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cd core_quantizer
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python -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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python -m src.quantizer # Generates optimized INT8 model
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```
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### 2. API Gateway (Laravel)
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```bash
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cd api_gateway
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composer install
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touch database/database.sqlite
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php artisan migrate # Initializes system tables (cache/sessions)
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php artisan serve
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```
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## API Testing Example
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Once the server is running at `http://localhost:8000`, you can verify the FHIR integration by sending a **POST** request to `http://localhost:8000/graphql` with the following payload:
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**Query:**
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```graphql
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query {
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diagnosticReport(patientId: "SW-1985") {
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id
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status
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category
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subject {
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reference
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}
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aiInferenceScore
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edgeModelVersion
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}
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}
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```
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**Expected Response:**
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```json
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{
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"data": {
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"diagnosticReport": {
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"id": "fhir-report-...",
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"status": "final",
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"category": "Oncology-PPI-Analysis",
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"subject": { "reference": "Patient/SW-1985" },
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"aiInferenceScore": 0.874,
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"edgeModelVersion": "INT8-Quantized-v1.0"
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}
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}
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}
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```
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## Implementation Rationale
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* **Resource Efficiency:** Implements INT8 quantization to bridge the gap between heavy GNN research and edge-native execution.
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* **Interoperability:** Maps raw AI scores to **FHIR** `DiagnosticReport` resources for seamless clinical integration.
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* **IP Alignment:** Developed in coordination with modular on-device clinical intelligence research (Patent No. 202541127477).
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---
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### Documentation
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* **`CITATION.cff`**: Academic and professional attribution.
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* **`CONTRIBUTING.md`**: Architectural and coding standards for open-source contributors.
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---

‎api_gateway/.editorconfig‎

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root = true
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[*]
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charset = utf-8
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end_of_line = lf
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indent_size = 4
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indent_style = space
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insert_final_newline = true
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trim_trailing_whitespace = true
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[*.md]
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trim_trailing_whitespace = false
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[*.{yml,yaml}]
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indent_size = 2
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[compose.yaml]
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indent_size = 4

‎api_gateway/.env.example‎

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APP_NAME=Laravel
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APP_ENV=local
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APP_KEY=
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APP_DEBUG=true
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APP_URL=http://localhost
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APP_LOCALE=en
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APP_FALLBACK_LOCALE=en
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APP_FAKER_LOCALE=en_US
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APP_MAINTENANCE_DRIVER=file
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# APP_MAINTENANCE_STORE=database
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# PHP_CLI_SERVER_WORKERS=4
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BCRYPT_ROUNDS=12
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LOG_CHANNEL=stack
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LOG_STACK=single
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LOG_DEPRECATIONS_CHANNEL=null
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LOG_LEVEL=debug
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DB_CONNECTION=sqlite
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# DB_HOST=127.0.0.1
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# DB_PORT=3306
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# DB_DATABASE=laravel
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# DB_USERNAME=root
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# DB_PASSWORD=
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SESSION_DRIVER=database
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SESSION_LIFETIME=120
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SESSION_ENCRYPT=false
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SESSION_PATH=/
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SESSION_DOMAIN=null
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BROADCAST_CONNECTION=log
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FILESYSTEM_DISK=local
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QUEUE_CONNECTION=database
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CACHE_STORE=database
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# CACHE_PREFIX=
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MEMCACHED_HOST=127.0.0.1
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REDIS_CLIENT=phpredis
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REDIS_HOST=127.0.0.1
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REDIS_PASSWORD=null
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REDIS_PORT=6379
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MAIL_MAILER=log
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MAIL_SCHEME=null
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MAIL_HOST=127.0.0.1
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MAIL_PORT=2525
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MAIL_USERNAME=null
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MAIL_PASSWORD=null
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MAIL_FROM_ADDRESS="hello@example.com"
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MAIL_FROM_NAME="${APP_NAME}"
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AWS_ACCESS_KEY_ID=
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AWS_SECRET_ACCESS_KEY=
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AWS_DEFAULT_REGION=us-east-1
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AWS_BUCKET=
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AWS_USE_PATH_STYLE_ENDPOINT=false
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VITE_APP_NAME="${APP_NAME}"

‎api_gateway/.gitattributes‎

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* text=auto eol=lf
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*.blade.php diff=html
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*.css diff=css
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*.html diff=html
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*.md diff=markdown
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*.php diff=php
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/.github export-ignore
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CHANGELOG.md export-ignore
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.styleci.yml export-ignore

‎api_gateway/.gitignore‎

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*.log
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.DS_Store
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.env
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.env.backup
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.env.production
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.phpactor.json
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.phpunit.result.cache
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/.fleet
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/.idea
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/.nova
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/.phpunit.cache
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/.vscode
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/.zed
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/auth.json
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/node_modules
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/public/build
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/public/hot
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/public/storage
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/storage/*.key
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/storage/pail
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/vendor
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Homestead.json
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Homestead.yaml
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Thumbs.db

‎api_gateway/app/GraphQL/Queries/DiagnosticReportQuery.php‎

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<?php
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namespace App\GraphQL\Queries;
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class DiagnosticReportQuery
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{
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/**
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* @param null $_
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* @param array<string, mixed> $args
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*/
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public function resolve($_, array $args)
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{
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// 1. In a production mesh, this intercepts the GraphQL request
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// and checks the Redis cache or database for the Edge AI worker's output.
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// 2. We mock the successful edge inference return payload:
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$simulatedAIResult = 0.874;
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return [
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'id' => uniqid('fhir-report-'),
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'status' => 'final',
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'category' => 'Oncology-PPI-Analysis',
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'subject' => [
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'id' => $args['patientId'],
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'reference' => 'Patient/' . $args['patientId']
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],
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'aiInferenceScore' => $simulatedAIResult,
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'edgeModelVersion' => 'INT8-Quantized-v1.0'
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];
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}
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}

‎api_gateway/app/Http/Controllers/Controller.php‎

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<?php
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namespace App\Http\Controllers;
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abstract class Controller
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{
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//
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}

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