README.md

metadata

title: AI Polymer Classification (Raman & FTIR)
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 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
• 📊 Task: Binary classification — Stable vs Weathered polymers
• 🚀 Features: Multi-model comparison, performance tracking dashboard
• 🛠️ Architecture: PyTorch + Streamlit

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🚧 Roadmap

• Inference from Raman .txt files
• Model selection (Figure2CNN, ResNet1D)
• FTIR support (modular integration complete)
• Multi-model comparison dashboard
• Performance tracking dashboard
• Add more trained CNNs for comparison
• 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

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🔗 Links

• Live App: Hugging Face Space
• GitHub Repo: ml-polymer-recycling

🚀 Technical Architecture

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

• Frontend: A Streamlit-based web application (app.py) provides an interactive, multi-tab user interface.
• Backend: PyTorch handles all deep learning operations, including model loading and inference.
• Model Management: A registry pattern (models/registry.py) allows for dynamic model loading and easy integration of new architectures.
• Data Processing: A robust, modality-aware preprocessing pipeline (utils/preprocessing.py) ensures data integrity and standardization for both Raman and FTIR data.
• Multi-Format Parsing: The utils/multifile.py module handles parsing of .txt, .csv, and .json files.
• Results Management: The utils/results_manager.py module manages session and persistent results, with support for multi-model comparison and data export.
• Performance Tracking: The utils/performance_tracker.py module logs performance metrics to a SQLite database and provides a dashboard for visualization.
• Deployment: The application is containerized using Docker (Dockerfile) for reproducible, cross-platform execution.