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(DOCS): finalize research-grade README with agnostic OCI instructions and scientific baseline
c446b65 | title: "PolymerOS: Predictive Framework for Polymer Aging" | |
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| license: apache-2.0 | |
| # PolymerOS: A Computational Framework for Degradation-Aware Plastic Classification | |
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| [**Live Interactive Dashboard**](https://huggingface.co/spaces/dev-jas/polymer-aging-with-ml) | [**Official Repository**](https://github.com/KLab-AI3/ml-polymer-recycling) | |
| --- | |
| ## Overview | |
| **PolymerOS** is the official computational framework for the predictive aging of plastics as described in the manuscript *"Predictive Framework to Indicate the Age of Plastics for Proper Recycling."* | |
| Conventional mechanical recycling often overlooks the degradation history of materials, leading to inconsistent product quality. This framework utilizes **deep learning applied to Raman and FTIR spectroscopy** to identify early-stage chemical and physical aging signatures. It provides a standardized, secure, and reproducible environment for the scientific classification of aged versus unaged polymers. | |
| --- | |
| ## Core Research Artifacts | |
| ### 1. Model Zoo | |
| Verified architectures and weights for the following models are provided: | |
| - **Figure2CNN**: High-performance binary classifier (Aged vs. Unaged) optimized for spectral data. | |
| - **ResNet1D**: Benchmarked 1D-convolutional architecture for spectral feature extraction. | |
| - **Preprocessing Pipeline**: A standardized 4-step sequence including asymmetric least-squares baseline correction, Savitzky–Golay smoothing, min-max normalization, and resampling to 4000 spectral points. | |
| ### 2. Standalone Scientific Appliance | |
| To facilitate reproducibility and practical use by researchers, the entire pipeline—including the interactive dashboard, inference engine, and preprocessing logic—is delivered as a **portable OCI container**. | |
| --- | |
| ## Technical Architecture | |
| ```text | |
| ml-polymer-recycling/ | |
| ├── backend/ | |
| │ ├── main.py # API entrypoint and static asset server | |
| │ ├── models/ | |
| │ │ └── weights/ # Model binaries (.pth managed via Git LFS) | |
| │ ├── utils/ | |
| │ │ ├── model_manager.py # Hardened PyTorch 2.6 safe-loading logic | |
| │ │ └── preprocessing.py # Standardized 4-step spectral preprocessing | |
| │ └── service.py # Core inference orchestration | |
| ├── frontend/ | |
| │ ├── src/ # React/TypeScript source code | |
| │ │ └── apiClient.ts # Location-agnostic API Client | |
| │ └── dist/ # Compiled production assets | |
| ├── Dockerfile # Multi-stage hardened OCI build configuration | |
| ├── requirements.txt # Python environment specifications | |
| └── .gitattributes # Git LFS tracking for model weights | |
| ``` | |
| --- | |
| ## Reproducibility & Local Operation | |
| ### 1. Prerequisites | |
| - **Git LFS** (Required to download model weight binaries). | |
| - **Docker** or **Podman**. | |
| ### 2. Setup | |
| ```bash | |
| # Clone the official repository | |
| git clone https://github.com/KLab-AI3/ml-polymer-recycling.git | |
| cd ml-polymer-recycling | |
| # Initialize LFS and pull model weights | |
| git lfs install | |
| git lfs pull | |
| ``` | |
| ### 3. Running the Dashboard | |
| To ensure bit-perfect scientific parity with the benchmarks reported in the manuscript, we recommend running the framework as a standalone appliance. The container is internally hardened to run in a restricted, read-only state. | |
| ```bash | |
| # Build the appliance | |
| docker build -t polymer-os . | |
| # Launch the dashboard | |
| docker run -p 7860:7860 polymer-os | |
| ``` | |
| The interactive interface will be available at: **http://localhost:7860** | |
| --- | |
| ## Security & Agnosticism | |
| - **PyTorch 2.6 Enforcement**: Models are loaded using the hardened `weights_only=True` standard to ensure safe execution in public environments. | |
| - **Environment Aware**: The "Agnostic" API client automatically detects host and port settings, ensuring seamless transitions between local workstations and cloud providers like Hugging Face Spaces. | |
| --- | |
| ## Contributors | |
| - **Dhoopshikha Lakshmi Devi Basgeet** — Lead Author | |
| - **Jaser Hasan** — Lead Developer / Technical Audit | |
| - **Konpal Raheja** | |
| - **Divita Mathur** | |
| - **Dr. Sanmukh Kuppannagari** — Corresponding Author | |
| - **Dr. Metin Karayilan** — Corresponding Author | |
| --- | |
| ## License | |
| Licensed under the Apache License, Version 2.0 (the "License"); see [LICENSE](LICENSE) for details. | |