README.md

---

title: AI Polymer Classification (Raman & FTIR)
emoji: 🔬
colorFrom: indigo
colorTo: yellow
sdk: streamlit
app_file: Upload_and_Run.py
pinned: false
license: apache-2.0
---


# AI-Driven Polymer Aging Prediction and Classification (v0.1)

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
![Streamlit](https://img.shields.io/badge/Streamlit-1.25%2B-brightgreen)
![Docker](https://img.shields.io/badge/docker-ready-blue)
![HF Space](https://img.shields.io/badge/HF%20Space-Live-blueviolet)

This web application classifies the degradation state of polymers using **Raman and FTIR spectroscopy** and deep learning.
It is a prototype pipeline for evaluating multiple convolutional neural networks (CNNs) on spectral data.

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## 🧪 Current Scope

- 🔬 **Modalities**: Raman & FTIR spectroscopy
- 💾 **Input Formats**: `.txt`, `.csv`, `.json` (with auto-detection)
- 🧠 **Models**: Figure2CNN (baseline), ResNet1D, ResNet18Vision, Custom CNNs (Enhanced, Efficient, Hybrid)
- 📊 **Task**: Binary classification — Stable vs Weathered polymers
- 🚀 **Features**:
  - Single-spectrum + Batch Spectrum Analysis
  - Multi-model comparison
  - Performance tracking dashboard
- 🛠️ **Architecture**: PyTorch + Streamlit

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## 🚧 Roadmap

- [x] Inference from Raman `.txt` files
- [x] Model selection (Figure2CNN, ResNet1D)
- [x] **FTIR support** (modular integration complete)
- [x] **Multi-model comparison dashboard**
- [x] **Performance tracking dashboard**
- [x] Add more trained CNNs for comparison
- [x] Image-based inference (future modality)
- [ ] RESTful API for programmatic access

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## 🧭 How to Use

The application provides three main analysis modes in a tabbed interface:

1. **Standard Analysis**:

   - Upload a single spectrum file (`.txt`, `.csv`, `.json`) or a batch of files.
   - Choose a model from the sidebar.
   - Run analysis and view the prediction, confidence, and technical details.

2. **Model Comparison**:

   - Upload a single spectrum file.
   - The app runs inference with all available models.
   - View a side-by-side comparison of the models' predictions and performance.

3. **Performance Tracking**:
   - Explore a dashboard with visualizations of historical performance data.
   - Compare model performance across different metrics.
   - Export performance data in CSV or JSON format.

### Supported Input

- Plaintext `.txt`, `.csv`, or `.json` files.
- Data can be space-, comma-, or tab-separated.
- Comment lines (`#`, `%`) are ignored.
- The app automatically detects the file format and resamples the data to a standard length.

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## 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)

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## 🔗 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)

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## 🚀 Technical Architecture

**The system is built on a modular, production-ready architecture designed for scalability and maintainability.**

- **Frontend**: Streamlit-based web application (`Upload_and_Run.py`) with interactive, multi-tab UI.
- **Backend**: PyTorch for deep learning operations including model loading and inference.
- **Model Management**: Registry pattern (`models/registry.py`) for dynamic model loading and easy integration of new architectures.
- **Data Processing**: Modality-aware preprocessing pipeline (`utils/preprocessing.py`) for data integrity and standardization (Raman & FTIR).
- **Multi-Format Parsing**: `utils/multifile.py` for parsing `.txt`, `.csv`, and `.json` files.
- **Results Management**: `utils/results_manager.py` for managing session and persistent results, multi-model comparison, and data export.
- **Performance Tracking**: `utils/performance_tracker.py` logs metrics to SQLite and powers the dashboard.
- **Deployment**: Containerized via Docker (`Dockerfile`) for reproducible, cross-platform execution.

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## Notable Additions in `space-deploy` Branch

- Enhanced FTIR support, with modular integration for spectral data.
- Multi-model comparison dashboard for evaluating multiple CNNs in parallel.
- Performance tracking dashboard with export options (CSV, JSON).
- Batch spectrum analysis for high-throughput evaluation.
- Updated color scheme (teal → yellow) for improved UI clarity.
- Improved README with Hugging Face Space config and clearer usage instructions.
- Bug fixes for filename casing and SDK deployment settings.
- Forward-looking plans for RESTful API and image-based inference.

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