(DOCS)[README]: Update and enhance description to include FTIR support; refine current scope and usage instructions

README.md

@@ -1,5 +1,5 @@
 ---
-title: AI Polymer Classification
+title: AI Polymer Classification (Raman & FTIR)
 emoji: 🔬
 colorFrom: indigo
 colorTo: yellow
@@ -8,19 +8,21 @@ 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.
+## AI-Driven Polymer Aging Prediction and Classification (v0.1)
 
-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.
+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.
 
 ---
 
 ## 🧪 Current Scope
 
-- 🔬 **Modality**: Raman spectroscopy (.txt)
-- 🧠 **Model**: Figure2CNN (baseline)
+- 🔬 **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
 
 ---
@@ -29,84 +31,76 @@ It was developed as part of the AIRE 2025 internship project at the Imageomics I
 
 - [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**
 - [ ] Add more trained CNNs for comparison
-- [ ] FTIR support (modular integration planned)
 - [ ] Image-based inference (future modality)
+- [ ] RESTful API for programmatic access
 
 ---
 
 ## 🧭 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
+The application provides three main analysis modes in a tabbed interface:
 
-Supported input:
+1.  **Standard Analysis**:
 
-- Plaintext `.txt` files with 1–2 columns
-- Space- or comma-separated
-- Comment lines (#) are ignored
-- Automatically resampled to 500 points
+    - 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.
 
----
-
-## Contributors
+2.  **Model Comparison**:
 
-  👨‍🏫 Dr. Sanmukh Kuppannagari (Mentor)  
-  👨‍🏫 Dr. Metin Karailyan (Mentor)  
-  👨‍💻 Jaser Hasan (Author/Developer)
+    - 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.
 
-## 🧠 Model Credit
+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.
 
-Baseline model inspired by:
+### Supported Input
 
-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)
+- 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.
 
 ---
 
-## 🔗 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:
+## Contributors
 
-   - **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.
+Dr. Sanmukh Kuppannagari (Mentor)
+Dr. Metin Karailyan (Mentor)
+Jaser Hasan (Author/Developer)
 
-   This ensures flexibility for future research and transparency in performance comparisons.
+## Model Credit
 
-2. **Image Input Modality**
+Baseline model inspired by:
 
-    > The system will support classification on images as an additional modality, extending beyond spectra. Key features will include:
+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)
 
-   - **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.
+## 🔗 Links
 
-3. **FTIR Dataset Integration**
+- **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)
 
-    > Although previously deferred, FTIR support will be added back in a modular, distinct fashion. Planned steps are:
+## 🚀 Technical Architecture
 
-    - **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.
+**The system is built on a modular, production-ready architecture designed for scalability and maintainability.**
 
-    This guarantees FTIR becomes a supported modality without undermining the validated Raman foundation.
+- **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.