Remove outdated environment configuration files and update README with detailed usage instructions for the Protein Location Predictor application. Add error handling for model loading in prediction utility, and include a sample protein sequence in FASTA format. Update the project structure and installation steps to reflect the latest changes.

Data/P50307.fasta

@@ -0,0 +1,8 @@
+>sp|P50307|METK_STAAU S-adenosylmethionine synthase OS=Staphylococcus aureus OX=1280 GN=metK PE=3 SV=1
+MLNNKRLFTSESVTEGHPDKIADQVSDAILDAILKDDPNARVACETTVTTGMALIAGEIS
+TTTYVDIPKVVRETIKEIGYTRAKYGYDYETMAILTAIDEQSPDIAQGVDKALEYRDKDS
+EEEIEATGAGDQGLMFGYATNETETYMPLAIYLSHQLAKRLSDVRKDGTLNYLRPDGKVQ
+VTVEYDENDNPVRIDTIVVSTQHAEDVTLEQIQEDIKAHVIYPTVPENLINEQTKFYINP
+TGRFVIGGPQGDAGLTGRKIIVDTYGGIARHGGGCFSGKDPTKVDRSAAYAARYVAKNIV
+AAGLADQCEVQLAYAIGVAEPVSIAIDTFGTGKVSEGQLVEAVRKHFDLRPAGIIKMLDL
+KQPIYKQTAAYGHFGRTDVLFPWEKLDKVEELKDAVK

Envs/requirements.txt

@@ -1,121 +0,0 @@
-asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1733250440834/work
-attrs @ file:///croot/attrs_1734533101012/work
-biopython @ file:///home/builder/ci_310/biopython_1640788437968/work
-biotite==0.41.2
-Brotli==1.1.0
-certifi==2025.1.31
-charset-normalizer==3.4.1
-cloudpathlib==0.20.0
-comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1733502965406/work
-contourpy==1.3.1
-cycler==0.12.1
-debugpy @ file:///croot/debugpy_1736267418885/work
-decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1740384970518/work
-dna_features_viewer==3.1.4
-einops==0.8.1
-entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1733327148154/work
-esm==3.1.4
-exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1733208806608/work
-executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1733569351617/work
-fastjsonschema @ file:///croot/python-fastjsonschema_1731939362158/work
-filelock==3.17.0
-fonttools==4.56.0
-fsspec==2025.2.0
-graphviz==0.20.3
-huggingface-hub==0.29.1
-idna==3.10
-importlib_metadata @ file:///croot/importlib_metadata-suite_1732633488278/work
-ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1719845459717/work
-ipython @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_ipython_1740856895/work
-ipywidgets @ file:///home/conda/feedstock_root/build_artifacts/ipywidgets_1733493556527/work
-jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1733300866624/work
-Jinja2==3.1.5
-joblib==1.4.2
-jsonschema @ file:///croot/jsonschema_1728486696720/work
-jsonschema-specifications @ file:///croot/jsonschema-specifications_1699032386549/work
-jupyter-client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1654730843242/work
-jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1727163409502/work
-jupyterlab_widgets @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_widgets_1733428046021/work
-kaleido @ file:///home/conda/feedstock_root/build_artifacts/python-kaleido_1615204619408/work
-kiwisolver==1.4.8
-MarkupSafe==3.0.2
-matplotlib==3.10.1
-matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1733416936468/work
-mkl-service==2.4.0
-mkl_fft @ file:///io/mkl313/mkl_fft_1730824109137/work
-mkl_random @ file:///io/mkl313/mkl_random_1730823916628/work
-mpmath==1.3.0
-msgpack==1.1.0
-msgpack-numpy==0.4.8
-narwhals @ file:///croot/narwhals_1742845957875/work
-nbformat @ file:///croot/nbformat_1728049424075/work
-nest_asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1733325553580/work
-networkx==3.4.2
-numpy @ file:///croot/numpy_and_numpy_base_1708638617955/work/dist/numpy-1.26.4-cp310-cp310-linux_x86_64.whl#sha256=d8cd837ed43e87f77e6efaa08e8de927ca030a1c9c5d04624432d6fb9a74a5ee
-nvidia-cublas-cu12==12.4.5.8
-nvidia-cuda-cupti-cu12==12.4.127
-nvidia-cuda-nvrtc-cu12==12.4.127
-nvidia-cuda-runtime-cu12==12.4.127
-nvidia-cudnn-cu12==9.1.0.70
-nvidia-cufft-cu12==11.2.1.3
-nvidia-curand-cu12==10.3.5.147
-nvidia-cusolver-cu12==11.6.1.9
-nvidia-cusparse-cu12==12.3.1.170
-nvidia-cusparselt-cu12==0.6.2
-nvidia-nccl-cu12==2.21.5
-nvidia-nvjitlink-cu12==12.4.127
-nvidia-nvtx-cu12==12.4.127
-packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1733203243479/work
-pandas==2.2.3
-parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1733271261340/work
-pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1733301927746/work
-pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1733327343728/work
-pillow==11.1.0
-platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1733232627818/work
-plotly @ file:///home/conda/feedstock_root/build_artifacts/plotly_1742240435426/work
-prompt_toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1737453357274/work
-psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1653089181607/work
-ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1733302279685/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl#sha256=92c32ff62b5fd8cf325bec5ab90d7be3d2a8ca8c8a3813ff487a8d2002630d1f
-pure_eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1733569405015/work
-py3Dmol==2.4.2
-pyfaidx @ file:///opt/conda/conda-bld/pyfaidx_1728570107633/work
-Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1736243443484/work
-pyparsing==3.2.1
-PyQt6==6.7.1
-PyQt6_sip @ file:///croot/pyqt-split_1744804475988/work/pyqt_sip
-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1733215673016/work
-pytz==2025.1
-PyVCF3 @ file:///opt/conda/conda-bld/pyvcf3_1650931562118/work
-PyYAML==6.0.2
-pyzmq @ file:///croot/pyzmq_1734687138743/work
-referencing @ file:///croot/referencing_1699012038513/work
-regex==2024.11.6
-requests==2.32.3
-rpds-py @ file:///croot/rpds-py_1736541261634/work
-safetensors==0.5.3
-scikit-learn==1.6.1
-scipy==1.15.2
-sip @ file:///croot/sip_1738856193618/work
-six @ file:///home/conda/feedstock_root/build_artifacts/six_1733380938961/work
-stack_data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1733569443808/work
-sympy==1.13.1
-tenacity==9.0.0
-threadpoolctl==3.5.0
-tokenizers==0.20.3
-tomli @ file:///opt/conda/conda-bld/tomli_1657175507142/work
-torch==2.6.0
-torchtext==0.18.0
-torchvision==0.21.0
-tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1648827254365/work
-tqdm==4.67.1
-traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1733367359838/work
-transformers==4.46.3
-triton==3.2.0
-typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1733188668063/work
-tzdata==2025.1
-uniprot-id-mapper==1.1.4
-urllib3==2.3.0
-wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1733231326287/work
-widgetsnbextension @ file:///home/conda/feedstock_root/build_artifacts/widgetsnbextension_1733128559935/work
-zipp @ file:///croot/zipp_1732630741423/work
-zstd==1.5.6.5

README.md

@@ -1,16 +1,229 @@
+# Protein Location Predictor
+
+A comprehensive GUI application for predicting protein subcellular localization using state-of-the-art machine learning models including PROST-T5 and ESM-C embeddings.
+
+## Features
+
+- **Multiple Model Support**: Choose from three different prediction models:
+  - PROST-T5: Transformer-based protein language model
+  - ESM-C 300M: Evolutionary Scale Modeling (300M parameters)
+  - ESM-C 600M: Evolutionary Scale Modeling (600M parameters)
+
+- **User-Friendly GUI**: Simple Tkinter-based interface with progress tracking
+- **Sequential Processing**: Process multiple protein sequences from FASTA files
+- **Flexible Output**: Save predictions with confidence scores in text format
+- **Error Handling**: Comprehensive error handling and user feedback
+
+## Requirements
+
+### Dependencies
+
+The project uses conda for environment management. All dependencies are specified in `environment.yml` and include:
+
+- PyTorch with CUDA support
+- Transformers library
+- ESM models
+- Scikit-learn
+- BioPython
+- NumPy, Joblib
+- Tkinter (GUI components)
+
+### Hardware Requirements
+
+- **Minimum**: 8GB RAM, CPU-only execution
+- **Recommended**: 16GB+ RAM, NVIDIA GPU with 8GB+ VRAM
+- **Storage**: ~5GB for model weights and cache
+
+## Installation
+
+**Prerequisites**: Conda must be installed on your system. [Download Conda](https://docs.conda.io/en/latest/miniconda.html)
+
+1. **Clone the repository from Hugging Face**:
+
+```bash
+# Make sure git-lfs is installed (https://git-lfs.com)
+git lfs install
+
+# Clone with all files
+git clone https://huggingface.co/jpuglia/ProteinLocationPredictor
+```
+
+**Optional - Clone without large files** (just pointers):
+```bash
+# If you want to clone without large files - just their pointers
+GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/jpuglia/ProteinLocationPredictor
+```
+
+2. **Navigate to the project directory**:
+```bash
+cd ProteinLocationPredictor
+```
+
+3. **Create conda environment**:
+```bash
+conda env create -n protein-predictor -f environment.yml
+conda activate protein-predictor
+```
+
+4. **Pre-trained models**:
+   - Model files are included in the repository via Git LFS
+   - If you cloned without large files, you'll need to download them separately
+
+## Usage
+
+### Running the GUI Application
+
+```bash
+conda activate protein-predictor
+python gui.py
+```
+
+### Step-by-Step Instructions
+
+1. **Launch the application**
+   - Run the GUI script
+   - The main window will appear with prediction options
+
+2. **Load a FASTA file**
+   - Click "File" → "Load FASTA"
+   - Select your protein sequences file (`.fasta`, `.fa`, or `.fas`)
+
+3. **Choose a prediction model**
+   - **PROST-T5**
+   - **ESM-C 300M**
+   - **ESM-C 600M**
+
+4. **Run prediction**
+   - Click the corresponding prediction button
+   - Monitor progress in the progress bar window
+   - Select output directory when prompted
+
+5. **Save results**
+   - Choose location and filename for prediction results
+   - Results are saved in CSV format with confidence scores for each subcellular location
+
+### Input Format
+
+FASTA files should contain protein sequences in standard format:
+
+```
+>protein_1
+MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG
+>protein_2
+MKTIIALSYIFCLVFAHATAKASEQTDNLQWDLAAIDNSGGHNAVDIKQNLQFQCQNNLHGCF
+```
+
+### Output Format
+
+Results are saved as CSV files with predictions for 6 subcellular locations, ranked by probability:
+
+```csv
+Sequence_ID,Prediction 1,Prediction 2,Prediction 3,Prediction 4,Prediction 5,Prediction 6
+sp|P0A7V8|RS4_ECOLI,Cytoplasmic (0.9860),CytoplasmicMembrane (0.0081),Periplasmic (0.0029),Extracellular (0.0019),OuterMembrane (0.0007),Cellwall (0.0003)
+```
+
+**Predicted Locations:**
+- **Cytoplasmic**: Interior of the cell
+- **CytoplasmicMembrane**: Inner membrane
+- **Periplasmic**: Space between inner and outer membranes
+- **Extracellular**: Outside the cell
+- **OuterMembrane**: Outer membrane
+- **Cellwall**: Cell wall structure
+
+## Model Details
+
+### PROST-T5
+- **Base Model**: Rostlab/ProstT5
+- **Embedding Dimension**: 1024
+- **Classifier**: Support Vector Machine (SVM)
+- **Memory Usage**: ~4GB GPU/8GB RAM
+
+### ESM-C Models
+- **Base Models**: ESM-C 300M/600M
+- **Embedding Dimension**: Variable (300M: 960, 600M: 1280)
+- **Classifier**: Support Vector Machine (SVM)
+- **Memory Usage**: 300M: ~2GB GPU, 600M: ~4GB GPU
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Out of Memory Errors**
+   - Reduce batch size or use CPU-only mode
+   - Close other applications to free memory
+   - Try smaller model (ESM-C 300M instead of 600M)
+
+2. **Model Loading Errors**
+   - Ensure model files are in the correct `Models/` directory
+   - Check file permissions and integrity
+   - Clear Hugging Face cache: `rm -rf ~/.cache/huggingface/`
+
+3. **CUDA Errors**
+   - Update GPU drivers
+   - Ensure CUDA-compatible PyTorch installation
+   - Fall back to CPU mode if GPU issues persist
+
+### Performance Tips
+
+- **GPU Usage**: Models automatically detect and use GPU when available
+- **Memory Management**: CUDA cache is cleared after each prediction
+- **Sequential Processing**: Sequences are processed one at a time with progress tracking
+
+## Project Structure
+
+```
+ProteinLocationPredictor/
+├── gui.py                 # Main GUI application
+├── src/
+│   └── my_utils.py       # Core prediction functions
+├── Models/               # Pre-trained model files (via Git LFS)
+│   ├── Prost T5_svm.joblib
+│   ├── Prost T5_le_svm.joblib
+│   ├── ESMC-300m_svm.joblib
+│   ├── ESMC-600m_svm.joblib
+│   └── ...
+├── environment.yml        # Conda environment specification
+└── README.md            # This file
+```
+
+## Contributing
+
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit your changes (`git commit -m 'Add amazing feature'`)
+4. Push to the branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Citation
+
+If you use this tool in your research, please cite:
+
+```bibtex
+@software{protein_location_predictor,
+  title={Protein Location Predictor},
+  author={Juan Diego Puglia},
+  year={2025},
+  url={https://huggingface.co/jpuglia/ProteinLocationPredictor}
+}
+```
+
+## Acknowledgments
+
+- [Rostlab](https://rostlab.org/) for the PROST-T5 model
+- [Meta AI](https://ai.meta.com/) for the ESM models
+- [Hugging Face](https://huggingface.co/) for model hosting and transformers library
+- [BioPython](https://biopython.org/) for sequence handling utilities
+
+## Contact
+
+For questions, issues, or collaborations, please:
+- Visit the [Hugging Face repository](https://huggingface.co/jpuglia/ProteinLocationPredictor)
+- Open a discussion on the Hugging Face platform
+
 ---
-license: mit
-language:
-- en
-metrics:
-- accuracy
-- precision
-- f1
-- recall
-base_model:
-- Rostlab/ProstT5
-- EvolutionaryScale/esmc-300m-2024-12
-- EvolutionaryScale/esmc-600m-2024-12
-tags:
-- biology
----
+
+**Note**: This tool is for research purposes. Please validate predictions with experimental methods for critical applications.

RepoStructure.txt

@@ -1,29 +0,0 @@
-ProteinSubcellularLocPredictor/
-│
-├── Data/                      # Raw and processed datasets
-│   ├── raw/                  # Raw, unaltered data
-│   ├── processed/            # Cleaned or feature-engineered data
-│   └── README.md             # Explain data sources and formats
-│
-├── Notebooks/                # Jupyter notebooks for EDA, training, etc.
-│   ├── 01_eda.ipynb
-│   ├── 02_preprocessing.ipynb
-│   ├── 03_training.ipynb
-│   └── 04_evaluation.ipynb
-│
-├── Deployment/               # Code for using the trained model
-│   ├── predictor.py          # Main script to load model and predict
-│   ├── api.py                # Optional: REST API using Flask/FastAPI
-│   └── cli.py                # Optional: Command-line interface
-│
-├── src/                      # Python modules shared between notebooks & deployment
-│   ├── __init__.py
-│   ├── preprocessing.py      # Feature engineering, tokenization, etc.
-│   ├── model.py              # Model creation/training/loading
-│   ├── utils.py              # Helper functions
-│   └── config.py             # Paths, constants, and config values
-│
-├── .gitignore                # Ignore datasets, checkpoints, virtual envs, etc.
-├── requirements.txt          # Python package dependencies
-├── README.md                 # Project overview, setup, usage
-└── LICENSE                   # Your preferred open-source license (e.g., MIT)

notebooks/01_EDA_Psort.ipynb

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:888f3665e5b2bf5e597acbe20bb839018b5ece80c55c3bf0bfd911904399031e
-size 10331239
+oid sha256:fae1ec4b018bfc4f3c580f34f7fa8e1e75a78848d2f3064778a2112fd8962fa4
+size 10331242

protein_predictor_readme.md:Zone.Identifier

@@ -0,0 +1,3 @@
+[ZoneTransfer]
+ZoneId=3
+HostUrl=https://claude.ai/

src/my_utils.py

@@ -927,9 +927,32 @@ def predict_with_prost(fasta_path: str):
     progress = ttk.Progressbar(progress_win, length=300, mode='determinate', maximum=total)
     progress.pack(padx=10, pady=10)
 
-    # Load model/tokenizer once
-    tokenizer = T5Tokenizer.from_pretrained("Rostlab/ProstT5", do_lower_case=False, legacy=True)
-    model = T5EncoderModel.from_pretrained("Rostlab/ProstT5")
+    # Load model/tokenizer with exception handling
+    try:
+        progress_label.config(text="Loading ProstT5 model...")
+        progress_win.update_idletasks()
+        
+        tokenizer = T5Tokenizer.from_pretrained("Rostlab/ProstT5", do_lower_case=False, legacy=True)
+        model = T5EncoderModel.from_pretrained("Rostlab/ProstT5")
+        
+        progress_label.config(text="Model loaded successfully! Embedding sequences...")
+        progress_win.update_idletasks()
+        
+    except RuntimeError as e:
+        progress_win.destroy()
+        if "Cannot allocate memory" in str(e):
+            messagebox.showerror(
+                "Memory Error", 
+                "Insufficient memory to load ProstT5 model.\n\n"
+                "Please try:\n"
+                "1. Close other applications\n"
+                "2. Restart your computer\n"
+                "3. Clear the model cache:\n"
+                "   rm -rf ~/.cache/huggingface/hub/models--Rostlab--ProstT5/"
+            )
+        else:
+            messagebox.showerror("Runtime Error", f"Error loading model: {str(e)}")
+        return
 
     embeddings = {}