Spaces:
Sleeping
Sleeping
| title: AI Polymer Classification | |
| emoji: 🔬 | |
| colorFrom: indigo | |
| colorTo: yellow | |
| sdk: streamlit | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| ## AI-Driven Polymer Aging Prediction and Classification (v0.1) | |
| This web application classifies the degradation state of polymers using Raman spectroscopy and deep learning. | |
| It was developed as part of the AIRE 2025 internship project at the Imageomics Institute and demonstrates a prototype pipeline for evaluating multiple convolutional neural networks (CNNs) on spectral data. | |
| --- | |
| ## 🧪 Current Scope | |
| - 🔬 **Modality**: Raman spectroscopy (.txt) | |
| - 🧠 **Model**: Figure2CNN (baseline) | |
| - 📊 **Task**: Binary classification — Stable vs Weathered polymers | |
| - 🛠️ **Architecture**: PyTorch + Streamlit | |
| --- | |
| ## 🚧 Roadmap | |
| - [x] Inference from Raman `.txt` files | |
| - [x] Model selection (Figure2CNN, ResNet1D) | |
| - [ ] Add more trained CNNs for comparison | |
| - [ ] FTIR support (modular integration planned) | |
| - [ ] Image-based inference (future modality) | |
| --- | |
| ## 🧭 How to Use | |
| 1. Upload a Raman spectrum `.txt` file (or select a sample) | |
| 2. Choose a model from the sidebar | |
| 3. Run analysis | |
| 4. View prediction, logits, and technical information | |
| Supported input: | |
| - Plaintext `.txt` files with 1–2 columns | |
| - Space- or comma-separated | |
| - Comment lines (#) are ignored | |
| - Automatically resampled to 500 points | |
| --- | |
| ## Contributors | |
| 👨🏫 Dr. Sanmukh Kuppannagari (Mentor) | |
| 👨🏫 Dr. Metin Karailyan (Mentor) | |
| 👨💻 Jaser Hasan (Author/Developer) | |
| ## 🧠 Model Credit | |
| Baseline model inspired by: | |
| Neo, E.R.K., Low, J.S.C., Goodship, V., Debattista, K. (2023). | |
| *Deep learning for chemometric analysis of plastic spectral data from infrared and Raman databases.* | |
| _Resources, Conservation & Recycling_, **188**, 106718. | |
| [https://doi.org/10.1016/j.resconrec.2022.106718](https://doi.org/10.1016/j.resconrec.2022.106718) | |
| --- | |
| ## 🔗 Links | |
| - 💻 **Live App**: [Hugging Face Space](https://huggingface.co/spaces/dev-jas/polymer-aging-ml) | |
| - 📂 **GitHub Repo**: [ml-polymer-recycling](https://github.com/KLab-AI3/ml-polymer-recycling) | |
| ## 🎯 Strategic Expansion Objectives (Roadmap) | |
| **The roadmap defines three major expansion paths designed to broaden the system’s capabilities and impact:** | |
| 1. **Model Expansion: Multi-Model Dashboard** | |
| > The dashboard will evolve into a hub for multiple model architectures rather than being tied to a single baseline. Planned work includes: | |
| - **Retraining & Fine-Tuning**: Incorporating publicly available vision models and retraining them with the polymer dataset. | |
| - **Model Registry**: Automatically detecting available .pth weights and exposing them in the dashboard for easy selection. | |
| - **Side-by-Side Reporting**: Running comparative experiments and reporting each model’s accuracy and diagnostics in a standardized format. | |
| - **Reproducible Integration**: Maintaining modular scripts and pipelines so each model’s results can be replicated without conflict. | |
| This ensures flexibility for future research and transparency in performance comparisons. | |
| 2. **Image Input Modality** | |
| > The system will support classification on images as an additional modality, extending beyond spectra. Key features will include: | |
| - **Upload Support**: Users can upload single images or batches directly through the dashboard. | |
| - **Multi-Model Execution**: Selected models from the registry can be applied to all uploaded images simultaneously. | |
| - **Batch Results**: Output will be returned in a structured, accessible way, showing both individual predictions and aggregate statistics. | |
| - **Enhanced Feedback**: Outputs will include predicted class, model confidence, and potentially annotated image previews. | |
| This expands the system toward a multi-modal framework, supporting broader research workflows. | |
| 3. **FTIR Dataset Integration** | |
| > Although previously deferred, FTIR support will be added back in a modular, distinct fashion. Planned steps are: | |
| - **Dedicated Preprocessing**: Tailored scripts to handle FTIR-specific signal characteristics (multi-layer handling, baseline correction, normalization). | |
| - **Architecture Compatibility**: Ensuring existing and retrained models can process FTIR data without mixing it with Raman workflows. | |
| - **UI Integration**: Introducing FTIR as a separate option in the modality selector, keeping Raman, Image, and FTIR workflows clearly delineated. | |
| - **Phased Development**: Implementation details to be refined during meetings to ensure scientific rigor. | |
| This guarantees FTIR becomes a supported modality without undermining the validated Raman foundation. | |