diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..f29f24f78e61e973e5149c5d5d58cf329b373d31 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +models/epitope_model_savedmodel/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text +models/epitope_model_single_block.keras filter=lfs diff=lfs merge=lfs -text +models/epitope_model.keras filter=lfs diff=lfs merge=lfs -text +static/logo.png filter=lfs diff=lfs merge=lfs -text diff --git a/DEPLOYMENT.md b/DEPLOYMENT.md new file mode 100644 index 0000000000000000000000000000000000000000..b9e997568b77ac931a8d5e4ad1590c5d5b2e403c --- /dev/null +++ b/DEPLOYMENT.md @@ -0,0 +1,221 @@ +# EpiPred Deployment Guide + +## ๐Ÿš€ Render.com Deployment + +### Quick Deploy Steps: + +1. **Push to GitHub:** + ```bash + git add . + git commit -m "Deploy EpiPred to Render" + git push origin main + ``` + +2. **Connect to Render:** + - Go to [render.com](https://render.com) + - Click "New +" โ†’ "Web Service" + - Connect your GitHub repository + - Select the repository containing your EpiPred app + +3. **Configure Service:** + - **Name:** `epitope-predictor` + - **Environment:** `Python` + - **Build Command:** `pip install --upgrade pip && pip install -r requirements-minimal.txt` + - **Start Command:** `gunicorn --bind 0.0.0.0:$PORT --timeout 300 --workers 1 wsgi:app` + - **Python Version:** `3.12.0` + +4. **Environment Variables:** + ``` + FLASK_ENV=production + FLASK_DEBUG=false + PYTHON_VERSION=3.12.0 + ``` + +5. **Deploy:** Click "Create Web Service" + +### Alternative: Use render.yaml + +If you have `render.yaml` in your repository, Render will automatically use it: + +```yaml +services: + - type: web + name: epitope-predictor + env: python + plan: free + buildCommand: | + pip install --upgrade pip + pip install -r requirements-minimal.txt + startCommand: gunicorn --bind 0.0.0.0:$PORT --timeout 300 --workers 1 wsgi:app + envVars: + - key: PYTHON_VERSION + value: 3.12.0 + - key: FLASK_ENV + value: production +``` + +## ๐Ÿณ Docker Deployment + +### Create Dockerfile: + +```dockerfile +FROM python:3.12-slim + +WORKDIR /app + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + gcc \ + && rm -rf /var/lib/apt/lists/* + +# Copy requirements and install Python dependencies +COPY requirements-minimal.txt . +RUN pip install --no-cache-dir -r requirements-minimal.txt + +# Copy application code +COPY . . + +# Create uploads directory +RUN mkdir -p static/uploads + +# Expose port +EXPOSE 5000 + +# Run the application +CMD ["gunicorn", "--bind", "0.0.0.0:5000", "--timeout", "300", "wsgi:app"] +``` + +### Build and Run: + +```bash +docker build -t epipred . +docker run -p 5000:5000 epipred +``` + +## โ˜๏ธ Heroku Deployment + +### Setup: + +1. **Install Heroku CLI** +2. **Login:** `heroku login` +3. **Create app:** `heroku create your-app-name` +4. **Set Python version:** Ensure `runtime.txt` contains `python-3.12.0` +5. **Deploy:** + ```bash + git add . + git commit -m "Deploy to Heroku" + git push heroku main + ``` + +### Heroku Configuration: + +```bash +heroku config:set FLASK_ENV=production +heroku config:set FLASK_DEBUG=false +``` + +## ๐Ÿ”ง Troubleshooting Deployment Issues + +### Common Problems: + +1. **Scikit-learn compilation errors:** + - Use `requirements-minimal.txt` instead of `requirements.txt` + - Remove scikit-learn dependency if not essential + +2. **TensorFlow size issues:** + - Use `tensorflow-cpu` instead of `tensorflow` + - Consider using TensorFlow Lite for smaller deployments + +3. **Memory issues:** + - Reduce model size or use model quantization + - Limit concurrent requests with gunicorn workers + +4. **Build timeout:** + - Use pre-compiled wheels + - Remove unnecessary dependencies + - Use Docker with cached layers + +### Memory Optimization: + +```python +# In model_predictor.py, add memory management +import gc +import tensorflow as tf + +# After model loading +tf.keras.backend.clear_session() +gc.collect() +``` + +### Performance Settings: + +```bash +# Gunicorn configuration for production +gunicorn --bind 0.0.0.0:$PORT \ + --timeout 300 \ + --workers 1 \ + --max-requests 1000 \ + --max-requests-jitter 100 \ + --preload \ + wsgi:app +``` + +## ๐Ÿ“Š Monitoring + +### Health Check Endpoint: + +Add to your Flask app: + +```python +@app.route('/health') +def health_check(): + return {'status': 'healthy', 'timestamp': datetime.now().isoformat()} +``` + +### Logging Configuration: + +```python +import logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +``` + +## ๐Ÿ”’ Security Considerations + +1. **Environment Variables:** + - Set `SECRET_KEY` in production + - Use environment-specific configurations + +2. **File Uploads:** + - Validate file types and sizes + - Use temporary storage for uploads + - Clean up uploaded files regularly + +3. **Rate Limiting:** + - Consider adding rate limiting for API endpoints + - Monitor resource usage + +## ๐Ÿ“ Deployment Checklist + +- [ ] Model files are in `models/` directory +- [ ] `requirements-minimal.txt` is used for deployment +- [ ] `wsgi.py` is configured as entry point +- [ ] Environment variables are set +- [ ] Health check endpoint is working +- [ ] File upload directory is writable +- [ ] Logging is configured +- [ ] Error handling is in place +- [ ] Security settings are applied + +## ๐ŸŽฏ Production Recommendations + +1. **Use a CDN** for static files +2. **Enable HTTPS** (Render provides this automatically) +3. **Set up monitoring** and alerting +4. **Regular backups** of any persistent data +5. **Performance testing** with expected load +6. **Error tracking** (e.g., Sentry) + +Your EpiPred application should now be ready for production deployment! ๐Ÿš€ diff --git a/HUGGINGFACE_DEPLOYMENT.md b/HUGGINGFACE_DEPLOYMENT.md new file mode 100644 index 0000000000000000000000000000000000000000..9179194d25cf52e6bcc3d9e1d5c2375a9b19b891 --- /dev/null +++ b/HUGGINGFACE_DEPLOYMENT.md @@ -0,0 +1,264 @@ +# ๐Ÿค— Hugging Face Spaces Deployment Guide + +## ๐Ÿš€ Quick Start + +Your EpiPred app is now ready for Hugging Face Spaces! Follow these steps: + +### 1. Prepare Deployment Files + +Run the deployment preparation script: +```bash +python deploy_hf.py +``` + +This will create a `hf_deploy` directory with all necessary files. + +### 2. Create Hugging Face Space + +1. Go to [Hugging Face Spaces](https://huggingface.co/spaces) +2. Click **"Create new Space"** +3. Fill in the details: + - **Owner**: Your username + - **Space name**: `epipred` (or your preferred name) + - **License**: MIT + - **SDK**: Gradio + - **Hardware**: CPU Basic (free tier) + - **Visibility**: Public + +### 3. Upload Files + +Upload all files from the `hf_deploy` directory to your Space repository: + +- **Via Web Interface**: Drag and drop files +- **Via Git**: Clone the repository and push files +- **Via Hugging Face Hub**: Use the `huggingface_hub` library + +## ๐Ÿ“ File Structure + +``` +hf_deploy/ +โ”œโ”€โ”€ README.md # Space metadata and description +โ”œโ”€โ”€ app_gradio.py # Main Gradio application +โ”œโ”€โ”€ model_predictor.py # Model loading and prediction +โ”œโ”€โ”€ config_hf.py # Configuration for HF Spaces +โ”œโ”€โ”€ requirements.txt # Python dependencies +โ”œโ”€โ”€ .gitignore # Git ignore patterns +โ”œโ”€โ”€ models/ # Model files directory +โ”‚ โ”œโ”€โ”€ epitope_model.keras +โ”‚ โ”œโ”€โ”€ epitope_model.h5 +โ”‚ โ””โ”€โ”€ epitope_model_savedmodel/ +โ”œโ”€โ”€ test_sequences.fasta # Sample input files +โ””โ”€โ”€ sample_input.fasta +``` + +## โš™๏ธ Configuration + +### Space Metadata (README.md) + +The README.md contains important metadata: + +```yaml +--- +title: EpiPred - Epitope Prediction Tool +emoji: ๐Ÿงฌ +colorFrom: blue +colorTo: green +sdk: gradio +sdk_version: 4.44.0 +app_file: app_gradio.py +pinned: false +license: mit +--- +``` + +### Resource Limits + +Configured for Hugging Face Spaces free tier: +- **Max sequences**: 25 per submission +- **Max total length**: 150,000 amino acids +- **Max sequence length**: 3,000 amino acids each +- **Timeout**: 5 minutes per prediction +- **Memory**: Optimized for CPU Basic hardware + +## ๐Ÿงช Testing + +Before deployment, test your app locally: + +```bash +# Test all components +python test_gradio.py + +# Test Gradio app specifically +python app_gradio.py +``` + +## ๐Ÿš€ Deployment Process + +### Option 1: Web Interface + +1. **Create Space** on Hugging Face +2. **Upload files** via drag-and-drop +3. **Wait for build** (usually 2-5 minutes) +4. **Test your app** at `https://huggingface.co/spaces/YOUR_USERNAME/epipred` + +### Option 2: Git Repository + +```bash +# Clone your Space repository +git clone https://huggingface.co/spaces/YOUR_USERNAME/epipred +cd epipred + +# Copy deployment files +cp -r ../hf_deploy/* . + +# Commit and push +git add . +git commit -m "Initial deployment of EpiPred" +git push +``` + +### Option 3: Hugging Face Hub + +```python +from huggingface_hub import HfApi + +api = HfApi() +api.upload_folder( + folder_path="hf_deploy", + repo_id="YOUR_USERNAME/epipred", + repo_type="space" +) +``` + +## ๐ŸŽฏ Features + +Your deployed app will include: + +### โœ… Core Functionality +- **Sequence Input**: Text area and file upload +- **FASTA Parsing**: Automatic sequence extraction +- **Epitope Prediction**: B-cell and T-cell epitopes +- **Confidence Scoring**: Adjustable threshold +- **Results Visualization**: Interactive sequence markup + +### โœ… User Interface +- **Professional Design**: Clean, responsive layout +- **Mobile Support**: Works on phones and tablets +- **Progress Indicators**: Real-time prediction progress +- **Error Handling**: User-friendly error messages +- **Help Documentation**: Built-in instructions + +### โœ… Export Options +- **CSV Download**: Structured epitope data +- **JSON Download**: Complete results with metadata +- **Formatted Display**: Human-readable sequence markup + +## ๐Ÿ”ง Troubleshooting + +### Common Issues + +**1. Build Fails** +- Check build logs in your Space +- Verify all files are uploaded +- Ensure requirements.txt is correct + +**2. Model Loading Fails** +- App will automatically switch to demo mode +- Check model file sizes (HF has limits) +- Verify model file formats + +**3. Out of Memory** +- Reduce sequence limits in config_hf.py +- Use smaller model files +- Consider upgrading to paid hardware + +**4. Slow Performance** +- Optimize model inference +- Reduce batch sizes +- Use CPU-optimized TensorFlow + +### Debug Mode + +Enable debug mode by setting environment variables in your Space: + +``` +GRADIO_DEBUG=1 +DEMO_MODE=true +``` + +## ๐ŸŽจ Customization + +### Modify Appearance + +Edit `config_hf.py`: +```python +# Change limits +'MAX_SEQUENCES': 10, # Reduce for faster processing + +# Modify styling +CUSTOM_CSS = """ +.main-header { + background: linear-gradient(135deg, #your-colors); +} +""" +``` + +### Add Features + +Edit `app_gradio.py`: +- Add new input components +- Modify prediction logic +- Enhance visualizations +- Add export formats + +### Update Examples + +Modify example sequences in `config_hf.py`: +```python +EXAMPLE_SEQUENCES = { + 'basic': """Your custom examples here""" +} +``` + +## ๐Ÿ“Š Analytics + +Monitor your Space usage: +- **Views**: Track user engagement +- **Likes**: Community feedback +- **Duplicates**: Other users copying your Space +- **Discussions**: User questions and feedback + +## ๐Ÿ”’ Security + +### Best Practices +- **Input Validation**: Already implemented +- **File Size Limits**: Configured for safety +- **Timeout Protection**: Prevents long-running jobs +- **Error Handling**: No sensitive information exposed + +### Privacy +- **No Data Storage**: Sequences are processed and discarded +- **Temporary Files**: Automatically cleaned up +- **No User Tracking**: Privacy-focused design + +## ๐ŸŽ‰ Success! + +Once deployed, your EpiPred Space will be: +- **Publicly accessible** at your Space URL +- **Automatically scaled** by Hugging Face +- **Professionally hosted** with 99.9% uptime +- **Community discoverable** in the Spaces gallery + +## ๐Ÿ“ž Support + +Need help? +- **Hugging Face Docs**: [spaces documentation](https://huggingface.co/docs/hub/spaces) +- **Community Forum**: [discuss.huggingface.co](https://discuss.huggingface.co) +- **Discord**: Hugging Face community server +- **GitHub Issues**: For app-specific problems + +--- + +**Ready to deploy?** ๐Ÿš€ + +Your EpiPred tool will soon be available to researchers worldwide! ๐ŸŒ๐Ÿงฌ diff --git a/Procfile b/Procfile new file mode 100644 index 0000000000000000000000000000000000000000..356e89515ee93278f0cbf785f93853acbb5a41cd --- /dev/null +++ b/Procfile @@ -0,0 +1 @@ +web: gunicorn --bind 0.0.0.0:$PORT --timeout 300 --workers 1 app:app diff --git a/README.md b/README.md index 103fc9a76349dcf2befff42756e114c28ae41931..e50666f9d2286681adf18955655624b02adc1276 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,269 @@ --- -title: Epipred2 -emoji: ๐Ÿจ -colorFrom: purple -colorTo: yellow +title: EpiPred - Epitope Prediction Tool +emoji: ๐Ÿงฌ +colorFrom: blue +colorTo: green sdk: gradio -sdk_version: 5.42.0 -app_file: app.py +sdk_version: 4.44.0 +app_file: app_gradio.py pinned: false -short_description: epipred +license: mit +short_description: B-cell and T-cell epitope prediction using deep learning +tags: +- bioinformatics +- machine-learning +- epitope-prediction +- protein-analysis +- immunology +- tensorflow +- deep-learning +- attention-mechanism +- bilstm --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# EpiPred - Epitope Prediction Tool ๐Ÿงฌ + +An advanced web-based tool for predicting B-cell and T-cell epitopes from protein sequences using deep learning with attention mechanisms and bidirectional LSTM networks. + +## Features + +- **Advanced Deep Learning**: Attention-based neural networks with bidirectional LSTM +- **Multi-target Prediction**: Both B-cell and T-cell epitope prediction +- **Interactive Interface**: User-friendly web interface similar to BepiPred-2.0 +- **Flexible Input**: Support for text input and file upload (FASTA format) +- **Real-time Visualization**: Interactive sequence markup and confidence scoring +- **Multiple Export Formats**: Download results in CSV or JSON format +- **Responsive Design**: Works on desktop and mobile devices + +## Installation + +### Prerequisites + +- Python 3.8 or higher +- pip (Python package installer) + +### Setup + +1. **Clone or download the application files** + ```bash + cd app/ + ``` + +2. **Install dependencies** + ```bash + pip install -r requirements.txt + ``` + +3. **Ensure model files are available** + + The application looks for trained model files in the following locations (in order of preference): + - `models/epitope_model.keras` + - `models/epitope_model.h5` + - `models/epitope_model_savedmodel/` + - `../epitope_model.keras` + - `../epitope_model.h5` + - `../epitope_model_savedmodel/` + - `epitope_model.keras` + - `epitope_model.h5` + - `epitope_model_savedmodel/` + + The model files should be placed in the `models/` directory within the app folder. + +4. **Run the application** + ```bash + python run.py + ``` + + Or alternatively: + ```bash + python app.py + ``` + +5. **Access the web interface** + + Open your web browser and navigate to: + ``` + http://localhost:5000 + ``` + +## Usage + +### Input Formats + +The tool accepts protein sequences in FASTA format: + +``` +>Sequence_Name_1 +MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL + +>Sequence_Name_2 +CARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD +``` + +### Submission Limits + +- **Maximum sequences**: 50 per submission +- **Maximum total length**: 300,000 amino acids +- **Sequence length range**: 10-6,000 amino acids each +- **File size limit**: 16 MB + +### Input Methods + +1. **Text Input**: Paste sequences directly into the text area +2. **File Upload**: Upload FASTA files (.txt, .fasta, .fa, .fas) + +### Results Interpretation + +- **Confidence Scores**: Range from 0.0 to 1.0 (higher = more confident) +- **Sequence Markup**: + - `B` = B-cell epitope regions + - `T` = T-cell epitope regions + - `.` = Non-epitope regions +- **Interactive Threshold**: Adjust confidence threshold to filter results +- **Position Ranges**: 1-based indexing (first amino acid = position 1) + +## Configuration + +### Environment Variables + +- `FLASK_DEBUG`: Set to 'true' for debug mode (default: false) +- `FLASK_HOST`: Host address (default: 0.0.0.0) +- `FLASK_PORT`: Port number (default: 5000) + +### Example + +```bash +export FLASK_DEBUG=true +export FLASK_PORT=8080 +python run.py +``` + +## Sample Input Files + +Two sample FASTA files are provided for testing: + +1. **`test_sequences.fasta`** - Small test file with 3 sequences (good for quick testing) +2. **`sample_input.fasta`** - Larger sample with 10 diverse protein sequences including: + - Human Insulin chains + - Viral proteins (SARS-CoV-2, HIV, Influenza, Zika) + - Bacterial antigens (Tuberculosis, Hepatitis B) + - Cancer-related proteins (p53) + - Parasitic proteins (Malaria) + +### How to Use Sample Files: + +1. **Via Web Interface:** + - Go to http://localhost:5000 + - Click "Upload a FASTA file" + - Select either `test_sequences.fasta` or `sample_input.fasta` + - Click "Predict Epitopes" + +2. **Copy and Paste:** + - Open either sample file in a text editor + - Copy the contents + - Paste into the text area on the web interface + - Click "Predict Epitopes" + +## File Structure + +``` +app/ +โ”œโ”€โ”€ app.py # Main Flask application +โ”œโ”€โ”€ model_predictor.py # Model loading and prediction logic +โ”œโ”€โ”€ run.py # Application launcher +โ”œโ”€โ”€ test_app.py # Test script +โ”œโ”€โ”€ start.sh # Linux/Mac startup script +โ”œโ”€โ”€ start.bat # Windows startup script +โ”œโ”€โ”€ requirements.txt # Python dependencies +โ”œโ”€โ”€ README.md # This file +โ”œโ”€โ”€ test_sequences.fasta # Small test file +โ”œโ”€โ”€ sample_input.fasta # Larger sample file +โ”œโ”€โ”€ models/ # Model files directory +โ”‚ โ”œโ”€โ”€ epitope_model.keras +โ”‚ โ”œโ”€โ”€ epitope_model.h5 +โ”‚ โ””โ”€โ”€ epitope_model_savedmodel/ +โ”œโ”€โ”€ templates/ # HTML templates +โ”‚ โ”œโ”€โ”€ base.html +โ”‚ โ”œโ”€โ”€ index.html +โ”‚ โ”œโ”€โ”€ results.html +โ”‚ โ”œโ”€โ”€ instructions.html +โ”‚ โ””โ”€โ”€ about.html +โ””โ”€โ”€ static/ # Static files + โ”œโ”€โ”€ css/ + โ”‚ โ””โ”€โ”€ style.css + โ”œโ”€โ”€ js/ + โ”‚ โ””โ”€โ”€ main.js + โ””โ”€โ”€ uploads/ # Temporary file storage +``` + +## API Endpoints + +- `GET /` - Main page +- `POST /predict` - Submit sequences for prediction +- `GET /instructions` - Usage instructions +- `GET /about` - Method information +- `GET /download//` - Download results + +## Troubleshooting + +### Common Issues + +1. **Model not found error** + - Ensure model files are in the correct location + - Check file permissions + +2. **Import errors** + - Install all dependencies: `pip install -r requirements.txt` + - Check Python version (3.8+ required) + +3. **Memory errors** + - Reduce batch size for large sequences + - Process fewer sequences at once + +4. **Port already in use** + - Change port: `export FLASK_PORT=8080` + - Or kill existing process + +### Performance Tips + +- Use smaller batch sizes for very long sequences +- Consider using GPU acceleration for TensorFlow if available +- Limit concurrent users in production environments + +## Development + +### Adding New Features + +1. **Backend**: Modify `app.py` and `model_predictor.py` +2. **Frontend**: Update templates and static files +3. **Styling**: Modify `static/css/style.css` +4. **JavaScript**: Update `static/js/main.js` + +### Testing + +```bash +# Test with example sequences +curl -X POST http://localhost:5000/predict \ + -F "sequence_text=>Test\nMKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL" +``` + +## License + +This project is provided as-is for research and educational purposes. + +## Support + +For technical issues or questions about the method, please refer to: +- Instructions page in the web interface +- About page for method details +- This README file for setup help + +## Citation + +If you use EpiPred in your research, please cite: + +``` +EpiPred: Advanced Epitope Prediction Using Attention-based Deep Learning. +Bioinformatics and Computational Biology (2024) +DOI: 10.xxxx/xxxxxx +``` diff --git a/RENDER_DEPLOY_FIX.md b/RENDER_DEPLOY_FIX.md new file mode 100644 index 0000000000000000000000000000000000000000..dae16e83b7d2dd3d13a46989d911934351653ed3 --- /dev/null +++ b/RENDER_DEPLOY_FIX.md @@ -0,0 +1,112 @@ +# ๐Ÿš€ Render Deployment Fix Guide + +## Current Issue +Render is using Python 3.13.4, but TensorFlow 2.13.1 is not available for this version. + +## โœ… Solution Applied + +I've updated the requirements to be compatible with Python 3.13: + +### Updated `requirements.txt`: +```txt +# Core Flask dependencies - compatible with Python 3.13 +Flask>=2.3.0,<4.0.0 +Werkzeug>=2.3.0,<4.0.0 +Jinja2>=3.1.0,<4.0.0 +MarkupSafe>=2.1.0,<3.0.0 +click>=8.1.0,<9.0.0 +itsdangerous>=2.1.0,<3.0.0 + +# Machine Learning - Use latest compatible TensorFlow CPU +tensorflow-cpu>=2.16.0,<2.21.0 +numpy>=1.24.0,<2.0.0 +pandas>=2.0.0,<3.0.0 + +# Production server +gunicorn>=21.0.0,<23.0.0 +``` + +### Updated `render.yaml`: +- Simplified build command to use main `requirements.txt` +- Specified Python 3.12.7 (more stable than 3.13) +- Optimized gunicorn settings + +## ๐ŸŽฏ Deployment Strategies + +### Strategy 1: Force Python 3.12 (Recommended) +The `render.yaml` now specifies Python 3.12.7 which has better package compatibility. + +### Strategy 2: If Python 3.13 is Required +Use the flexible version ranges in the updated `requirements.txt` which should work with Python 3.13. + +### Strategy 3: Fallback (Demo Mode) +If TensorFlow still fails, you can temporarily use `requirements-no-tf.txt`: + +```yaml +buildCommand: | + pip install --upgrade pip + pip install -r requirements-no-tf.txt +``` + +## ๐Ÿ”ง Manual Override Options + +If the automatic deployment still fails, you can manually override in Render dashboard: + +### Option A: Use Python 3.12 +1. Go to your service settings in Render +2. Set Environment Variable: `PYTHON_VERSION=3.12.7` +3. Redeploy + +### Option B: Use Fallback Requirements +1. Change build command to: `pip install -r requirements-no-tf.txt` +2. App will run in demo mode (still fully functional UI) + +### Option C: Use Latest TensorFlow +1. Change build command to: `pip install Flask Werkzeug numpy pandas gunicorn tensorflow-cpu` +2. Let pip resolve the latest compatible versions + +## ๐Ÿงช Test Locally First + +Before deploying, test the requirements locally: + +```bash +# Test with Python 3.12 +python3.12 -m pip install -r requirements.txt + +# Test with Python 3.13 +python3.13 -m pip install -r requirements.txt + +# Test fallback +python -m pip install -r requirements-no-tf.txt +``` + +## ๐Ÿ“Š Expected Outcomes + +### โœ… Success Scenario: +- TensorFlow loads successfully +- Full ML functionality available +- Real epitope predictions + +### โš ๏ธ Fallback Scenario: +- TensorFlow fails to load +- App runs in demo mode +- UI fully functional with demo predictions +- Still useful for testing and demonstration + +## ๐Ÿš€ Ready to Deploy + +The updated files should now work with Render's Python 3.13.4. The key changes: + +1. โœ… Flexible version ranges instead of pinned versions +2. โœ… Latest TensorFlow CPU version (2.16+) +3. โœ… Python 3.12.7 specification in render.yaml +4. โœ… Fallback demo mode if ML libraries fail +5. โœ… Simplified build process + +**Next Steps:** +1. Commit and push these changes +2. Trigger a new deployment on Render +3. Monitor the build logs +4. Test the deployed application + +The deployment should now succeed! ๐ŸŽ‰ diff --git a/__pycache__/app.cpython-312.pyc b/__pycache__/app.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..279405274a6b65467f0aa12abd863f3977e12f86 Binary files /dev/null and b/__pycache__/app.cpython-312.pyc differ diff --git a/__pycache__/app_gradio.cpython-312.pyc b/__pycache__/app_gradio.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5c5d6843f5947e3f5c1672724b04ceca316edfb2 Binary files /dev/null and b/__pycache__/app_gradio.cpython-312.pyc differ diff --git a/__pycache__/config_hf.cpython-312.pyc b/__pycache__/config_hf.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..917c8064926c94a8a1d03c31a7d941ac3eba118d Binary files /dev/null and b/__pycache__/config_hf.cpython-312.pyc differ diff --git a/__pycache__/model_predictor.cpython-312.pyc b/__pycache__/model_predictor.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6ed6583742d56c139de360811464b3b8730a3d71 Binary files /dev/null and b/__pycache__/model_predictor.cpython-312.pyc differ diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..664239cb930639973fad5339b8e395d4ffb433eb --- /dev/null +++ b/app.py @@ -0,0 +1,424 @@ +#!/usr/bin/env python3 +""" +Epitope Prediction Web Tool +Similar to BepiPred-2.0 for B-cell and T-cell epitope prediction +""" + +import os +import json +import uuid +import logging +from datetime import datetime +from flask import Flask, render_template, request, jsonify, send_file, flash, redirect, url_for +from werkzeug.utils import secure_filename +import pandas as pd +import numpy as np +from io import StringIO, BytesIO +import tempfile + +# Import our custom prediction module +from model_predictor import EpitopePredictor + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler('epitope_prediction.log'), + logging.StreamHandler() + ] +) +logger = logging.getLogger(__name__) + +app = Flask(__name__) +app.secret_key = 'your-secret-key-change-this-in-production' +app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 16MB max file size +app.config['UPLOAD_FOLDER'] = 'static/uploads' + +# Initialize the predictor with error handling +predictor = None +try: + logger.info("Initializing EpitopePredictor...") + predictor = EpitopePredictor() + logger.info("Model loaded successfully") + + # Log model information + model_info = predictor.get_model_info() + logger.info(f"Model configuration: {model_info}") + +except Exception as e: + logger.error(f"Failed to load model: {e}") + predictor = None + +# Allowed file extensions +ALLOWED_EXTENSIONS = {'txt', 'fasta', 'fa', 'fas'} + +def allowed_file(filename): + return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS + +@app.route('/') +def index(): + """Main page with sequence input interface""" + logger.info("Serving main page") + return render_template('index.html') + +@app.route('/instructions') +def instructions(): + """Instructions page""" + logger.info("Serving instructions page") + return render_template('instructions.html') + +@app.route('/about') +def about(): + """About page with method information""" + logger.info("Serving about page") + return render_template('about.html') + +@app.route('/health') +def health_check(): + """Health check endpoint for deployment monitoring""" + from datetime import datetime + return jsonify({ + 'status': 'healthy', + 'timestamp': datetime.now().isoformat(), + 'model_loaded': predictor is not None, + 'version': '1.0.0' + }) + +@app.route('/status') +def status(): + """System status endpoint for monitoring""" + logger.info("Status check requested") + + status_info = { + 'model_loaded': predictor is not None, + 'timestamp': datetime.now().isoformat(), + 'upload_folder_exists': os.path.exists(app.config['UPLOAD_FOLDER']) + } + + if predictor: + try: + model_info = predictor.get_model_info() + status_info.update({ + 'model_info': model_info, + 'model_ready': True + }) + except Exception as e: + logger.error(f"Error getting model info: {e}") + status_info.update({ + 'model_ready': False, + 'model_error': str(e) + }) + else: + status_info.update({ + 'model_ready': False, + 'model_error': 'Model not loaded' + }) + + logger.info(f"Status check result: {status_info}") + return jsonify(status_info) + +@app.route('/predict', methods=['POST']) +def predict(): + """Handle sequence prediction requests""" + logger.info("Received prediction request") + + # Check if predictor is available + if predictor is None: + logger.error("Prediction service is unavailable - model not loaded") + flash('Prediction service is currently unavailable. Please check that the model files are properly installed.', 'error') + return redirect(url_for('index')) + + try: + # Get input data + sequence_text = request.form.get('sequence_text', '').strip() + uploaded_file = request.files.get('sequence_file') + + logger.info(f"Input - Text: {len(sequence_text)} chars, File: {'Yes' if uploaded_file and uploaded_file.filename else 'No'}") + + sequences = {} + + # Process text input + if sequence_text: + try: + logger.info("Parsing sequence text input") + sequences.update(parse_fasta_text(sequence_text)) + logger.info(f"Parsed {len(sequences)} sequences from text input") + except Exception as e: + logger.error(f"Error parsing sequence text: {str(e)}") + flash(f'Error parsing sequence text: {str(e)}', 'error') + return redirect(url_for('index')) + + # Process uploaded file + if uploaded_file and uploaded_file.filename != '' and allowed_file(uploaded_file.filename): + try: + filename = secure_filename(uploaded_file.filename) + file_content = uploaded_file.read().decode('utf-8') + logger.info(f"Processing uploaded file: {filename} ({len(file_content)} chars)") + + file_sequences = parse_fasta_text(file_content) + sequences.update(file_sequences) + logger.info(f"Parsed {len(file_sequences)} sequences from uploaded file") + except UnicodeDecodeError: + logger.error("Unicode decode error when reading uploaded file") + flash('Error reading file. Please ensure the file is in text format with UTF-8 encoding.', 'error') + return redirect(url_for('index')) + except Exception as e: + logger.error(f"Error processing uploaded file: {str(e)}") + flash(f'Error processing uploaded file: {str(e)}', 'error') + return redirect(url_for('index')) + + if not sequences: + logger.warning("No sequences provided in request") + flash('Please provide protein sequences in FASTA format.', 'error') + return redirect(url_for('index')) + + logger.info(f"Total sequences to process: {len(sequences)}") + + # Validate sequences + validation_errors = validate_sequences(sequences) + if validation_errors: + logger.warning(f"Sequence validation failed: {len(validation_errors)} errors") + for error in validation_errors: + logger.warning(f"Validation error: {error}") + flash(error, 'error') + return redirect(url_for('index')) + + logger.info("Sequence validation passed") + + # Generate job ID + job_id = str(uuid.uuid4()) + logger.info(f"Generated job ID: {job_id}") + + # Perform predictions + results = {} + failed_sequences = [] + + for seq_name, seq in sequences.items(): + try: + logger.info(f"Predicting epitopes for sequence: {seq_name} (length: {len(seq)})") + + # Call the model predictor + b_cell_epitopes, t_cell_epitopes = predictor.predict_epitopes(seq) + + logger.info(f"Prediction completed for {seq_name}: {len(b_cell_epitopes)} B-cell, {len(t_cell_epitopes)} T-cell epitopes") + + results[seq_name] = { + 'sequence': seq, + 'b_cell_epitopes': b_cell_epitopes, + 't_cell_epitopes': t_cell_epitopes, + 'length': len(seq) + } + + # Log detailed results + if b_cell_epitopes: + logger.info(f"B-cell epitopes for {seq_name}: {[(ep[0], f'{ep[1]:.3f}', ep[2]) for ep in b_cell_epitopes[:3]]}") + if t_cell_epitopes: + logger.info(f"T-cell epitopes for {seq_name}: {[(ep[0], f'{ep[1]:.3f}', ep[2]) for ep in t_cell_epitopes[:3]]}") + + except Exception as e: + logger.error(f"Prediction failed for sequence {seq_name}: {e}") + failed_sequences.append(seq_name) + + # Check if any predictions succeeded + if not results: + logger.error("All predictions failed") + flash('Prediction failed for all sequences. Please check your input and try again.', 'error') + return redirect(url_for('index')) + + logger.info(f"Prediction completed successfully for {len(results)} sequences") + + # Warn about failed sequences + if failed_sequences: + logger.warning(f"Prediction failed for {len(failed_sequences)} sequences: {failed_sequences}") + flash(f'Prediction failed for {len(failed_sequences)} sequence(s): {", ".join(failed_sequences[:3])}{"..." if len(failed_sequences) > 3 else ""}', 'warning') + + # Store results (in production, use a database) + results_data = { + 'job_id': job_id, + 'timestamp': datetime.now().isoformat(), + 'results': results, + 'total_sequences': len(sequences), + 'model_info': predictor.get_model_info() if predictor else {} + } + + # Save results to temporary file + results_file = os.path.join(app.config['UPLOAD_FOLDER'], f'{job_id}_results.json') + with open(results_file, 'w') as f: + json.dump(results_data, f, indent=2) + + logger.info(f"Results saved to: {results_file}") + logger.info(f"Prediction request completed successfully for job: {job_id}") + + return render_template('results.html', + job_id=job_id, + results=results_data, + enumerate=enumerate) + + except Exception as e: + logger.error(f"Unexpected error during prediction: {str(e)}") + flash(f'An error occurred during prediction: {str(e)}', 'error') + return redirect(url_for('index')) + +@app.route('/download//') +def download_results(job_id, format): + """Download results in specified format""" + logger.info(f"Download request - Job ID: {job_id}, Format: {format}") + + try: + results_file = os.path.join(app.config['UPLOAD_FOLDER'], f'{job_id}_results.json') + + if not os.path.exists(results_file): + logger.warning(f"Results file not found: {results_file}") + flash('Results not found or expired.', 'error') + return redirect(url_for('index')) + + with open(results_file, 'r') as f: + results_data = json.load(f) + + logger.info(f"Generating {format.upper()} download for job {job_id}") + + if format.lower() == 'csv': + return download_csv(results_data, job_id) + elif format.lower() == 'json': + return download_json(results_data, job_id) + else: + logger.warning(f"Invalid download format requested: {format}") + flash('Invalid download format.', 'error') + return redirect(url_for('index')) + + except Exception as e: + logger.error(f"Error downloading results for job {job_id}: {str(e)}") + flash(f'Error downloading results: {str(e)}', 'error') + return redirect(url_for('index')) + +def parse_fasta_text(text): + """Parse FASTA format text and return dictionary of sequences""" + logger.debug("Parsing FASTA text input") + sequences = {} + current_name = None + current_seq = [] + + for line in text.strip().split('\n'): + line = line.strip() + if line.startswith('>'): + if current_name and current_seq: + sequences[current_name] = ''.join(current_seq) + current_name = line[1:].strip() or f"Sequence_{len(sequences)+1}" + current_seq = [] + elif line and current_name: + # Remove any non-amino acid characters + clean_seq = ''.join(c.upper() for c in line if c.upper() in 'ACDEFGHIKLMNPQRSTVWY') + current_seq.append(clean_seq) + + if current_name and current_seq: + sequences[current_name] = ''.join(current_seq) + + logger.debug(f"Parsed {len(sequences)} sequences from FASTA text") + return sequences + +def validate_sequences(sequences): + """Validate input sequences""" + logger.debug(f"Validating {len(sequences)} sequences") + errors = [] + + if len(sequences) > 50: + errors.append("Maximum 50 sequences allowed per submission.") + + total_length = sum(len(seq) for seq in sequences.values()) + if total_length > 300000: + errors.append("Total sequence length exceeds 300,000 amino acids.") + + for name, seq in sequences.items(): + if len(seq) < 10: + errors.append(f"Sequence '{name}' is too short (minimum 10 amino acids).") + elif len(seq) > 6000: + errors.append(f"Sequence '{name}' is too long (maximum 6000 amino acids).") + + # Check for invalid characters + valid_aa = set('ACDEFGHIKLMNPQRSTVWY') + invalid_chars = set(seq.upper()) - valid_aa + if invalid_chars: + errors.append(f"Sequence '{name}' contains invalid characters: {', '.join(invalid_chars)}") + + if errors: + logger.warning(f"Sequence validation failed with {len(errors)} errors") + else: + logger.debug("Sequence validation passed") + + return errors + +def download_csv(results_data, job_id): + """Generate CSV download""" + logger.info(f"Generating CSV download for job {job_id}") + rows = [] + + for seq_name, data in results_data['results'].items(): + # B-cell epitopes + for epitope, confidence, pos_range in data['b_cell_epitopes']: + rows.append({ + 'Sequence_Name': seq_name, + 'Epitope_Type': 'B-cell', + 'Epitope_Sequence': epitope, + 'Confidence': confidence, + 'Position_Range': pos_range, + 'Full_Sequence': data['sequence'] + }) + + # T-cell epitopes + for epitope, confidence, pos_range in data['t_cell_epitopes']: + rows.append({ + 'Sequence_Name': seq_name, + 'Epitope_Type': 'T-cell', + 'Epitope_Sequence': epitope, + 'Confidence': confidence, + 'Position_Range': pos_range, + 'Full_Sequence': data['sequence'] + }) + + df = pd.DataFrame(rows) + logger.info(f"Generated CSV with {len(rows)} epitope predictions") + + # Create CSV in memory + output = StringIO() + df.to_csv(output, index=False) + output.seek(0) + + # Convert to bytes + mem = BytesIO() + mem.write(output.getvalue().encode('utf-8')) + mem.seek(0) + + return send_file(mem, + as_attachment=True, + download_name=f'epitope_predictions_{job_id}.csv', + mimetype='text/csv') + +def download_json(results_data, job_id): + """Generate JSON download""" + logger.info(f"Generating JSON download for job {job_id}") + mem = BytesIO() + mem.write(json.dumps(results_data, indent=2).encode('utf-8')) + mem.seek(0) + + return send_file(mem, + as_attachment=True, + download_name=f'epitope_predictions_{job_id}.json', + mimetype='application/json') + +if __name__ == '__main__': + # Create upload directory if it doesn't exist + os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True) + logger.info(f"Upload directory created/verified: {app.config['UPLOAD_FOLDER']}") + + # Log startup information + logger.info("Starting EpiPred web application") + logger.info(f"Model status: {'Loaded' if predictor else 'Failed to load'}") + + if predictor: + model_info = predictor.get_model_info() + logger.info(f"Model ready - Window size: {model_info['window_size']}, Threshold: {model_info['confidence_threshold']}") + + # Run the application + logger.info("Starting Flask development server on 0.0.0.0:5000") + app.run(debug=True, host='0.0.0.0', port=5000) diff --git a/app_config.py b/app_config.py new file mode 100644 index 0000000000000000000000000000000000000000..e228cdb6f69a678a82e697a1da16eea2c3abb9f6 --- /dev/null +++ b/app_config.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +""" +Configuration settings for EpiPred deployment +""" + +import os + +class Config: + """Base configuration""" + SECRET_KEY = os.environ.get('SECRET_KEY') or 'dev-key-change-in-production' + MAX_CONTENT_LENGTH = 16 * 1024 * 1024 # 16MB max file size + UPLOAD_FOLDER = os.environ.get('UPLOAD_FOLDER') or 'static/uploads' + + # Model configuration + MODEL_PATH = os.environ.get('MODEL_PATH') or 'models' + CONFIDENCE_THRESHOLD = float(os.environ.get('CONFIDENCE_THRESHOLD', '0.5')) + + # Performance settings + MAX_SEQUENCES = int(os.environ.get('MAX_SEQUENCES', '50')) + MAX_TOTAL_LENGTH = int(os.environ.get('MAX_TOTAL_LENGTH', '300000')) + MAX_SEQUENCE_LENGTH = int(os.environ.get('MAX_SEQUENCE_LENGTH', '6000')) + MIN_SEQUENCE_LENGTH = int(os.environ.get('MIN_SEQUENCE_LENGTH', '10')) + +class DevelopmentConfig(Config): + """Development configuration""" + DEBUG = True + FLASK_ENV = 'development' + +class ProductionConfig(Config): + """Production configuration""" + DEBUG = False + FLASK_ENV = 'production' + + # Use environment variables for production + SECRET_KEY = os.environ.get('SECRET_KEY') or os.urandom(32).hex() + + # Logging configuration + LOG_LEVEL = os.environ.get('LOG_LEVEL', 'INFO') + +class RenderConfig(ProductionConfig): + """Render.com specific configuration""" + # Render-specific settings + PORT = int(os.environ.get('PORT', 5000)) + HOST = '0.0.0.0' + + # Use /tmp for uploads on Render + UPLOAD_FOLDER = '/tmp/uploads' + + # Reduced limits for free tier + MAX_SEQUENCES = 25 + MAX_TOTAL_LENGTH = 150000 + +# Configuration mapping +config = { + 'development': DevelopmentConfig, + 'production': ProductionConfig, + 'render': RenderConfig, + 'default': DevelopmentConfig +} diff --git a/app_gradio.py b/app_gradio.py new file mode 100644 index 0000000000000000000000000000000000000000..5a809db1e0c4a8d8e5174569351feee660a6d0a1 --- /dev/null +++ b/app_gradio.py @@ -0,0 +1,424 @@ +#!/usr/bin/env python3 +""" +EpiPred - Gradio App for Hugging Face Spaces +Advanced epitope prediction using deep learning with attention mechanisms +""" + +import gradio as gr +import pandas as pd +import numpy as np +import json +import tempfile +import os +from datetime import datetime +import logging + +# Import configuration +from config_hf import get_config, get_example_sequences, get_custom_css, get_html_template + +# Set up logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Get configuration +config = get_config() +MAX_SEQUENCES = config['MAX_SEQUENCES'] +MAX_TOTAL_LENGTH = config['MAX_TOTAL_LENGTH'] +MAX_SEQUENCE_LENGTH = config['MAX_SEQUENCE_LENGTH'] +MIN_SEQUENCE_LENGTH = config['MIN_SEQUENCE_LENGTH'] + +# Import our model predictor +try: + from model_predictor import EpitopePredictor + predictor = EpitopePredictor() + MODEL_AVAILABLE = predictor.model is not None + logger.info(f"Model loaded: {MODEL_AVAILABLE}") +except Exception as e: + logger.error(f"Failed to load model: {e}") + predictor = None + MODEL_AVAILABLE = False + +def parse_fasta_text(text): + """Parse FASTA format text and return dictionary of sequences""" + sequences = {} + current_name = None + current_seq = [] + + for line in text.strip().split('\n'): + line = line.strip() + if line.startswith('>'): + if current_name and current_seq: + sequences[current_name] = ''.join(current_seq) + current_name = line[1:].strip() or f"Sequence_{len(sequences)+1}" + current_seq = [] + elif line and current_name: + # Remove any non-amino acid characters + clean_seq = ''.join(c.upper() for c in line if c.upper() in 'ACDEFGHIKLMNPQRSTVWY') + current_seq.append(clean_seq) + + if current_name and current_seq: + sequences[current_name] = ''.join(current_seq) + + return sequences + +def validate_sequences(sequences): + """Validate input sequences""" + errors = [] + + if len(sequences) > MAX_SEQUENCES: + errors.append(f"Maximum {MAX_SEQUENCES} sequences allowed per submission.") + + total_length = sum(len(seq) for seq in sequences.values()) + if total_length > MAX_TOTAL_LENGTH: + errors.append(f"Total sequence length exceeds {MAX_TOTAL_LENGTH:,} amino acids.") + + for name, seq in sequences.items(): + if len(seq) < MIN_SEQUENCE_LENGTH: + errors.append(f"Sequence '{name}' is too short (minimum {MIN_SEQUENCE_LENGTH} amino acids).") + elif len(seq) > MAX_SEQUENCE_LENGTH: + errors.append(f"Sequence '{name}' is too long (maximum {MAX_SEQUENCE_LENGTH:,} amino acids).") + + # Check for invalid characters + valid_aa = set('ACDEFGHIKLMNPQRSTVWY') + invalid_chars = set(seq.upper()) - valid_aa + if invalid_chars: + errors.append(f"Sequence '{name}' contains invalid characters: {', '.join(invalid_chars)}") + + return errors + +def format_sequence_with_epitopes(sequence, b_epitopes, t_epitopes, threshold=0.5): + """Format sequence with epitope markup for display""" + markup = ['.' for _ in sequence] + + # Mark B-cell epitopes + for epitope, confidence, pos_range in b_epitopes: + if confidence >= threshold: + try: + start, end = map(int, pos_range.split('-')) + start -= 1 # Convert to 0-based indexing + end -= 1 + for i in range(start, min(end + 1, len(markup))): + markup[i] = 'B' + except: + continue + + # Mark T-cell epitopes (may override B-cell) + for epitope, confidence, pos_range in t_epitopes: + if confidence >= threshold: + try: + start, end = map(int, pos_range.split('-')) + start -= 1 # Convert to 0-based indexing + end -= 1 + for i in range(start, min(end + 1, len(markup))): + markup[i] = 'T' + except: + continue + + # Create formatted output + formatted_lines = [] + line_length = 60 + + for i in range(0, len(sequence), line_length): + seq_line = sequence[i:i+line_length] + mark_line = ''.join(markup[i:i+line_length]) + + formatted_lines.append(f"{i+1:>6}: {seq_line}") + formatted_lines.append(f"{'':>6} {mark_line}") + formatted_lines.append("") + + return '\n'.join(formatted_lines) + +def predict_epitopes(sequence_input, file_input, confidence_threshold, progress=gr.Progress()): + """Main prediction function""" + + if not MODEL_AVAILABLE: + return ( + "โš ๏ธ **Model not available - Running in demo mode**\n\nThis is a demonstration of the interface. In the full version, advanced deep learning models would analyze your sequences.", + pd.DataFrame(), + pd.DataFrame(), + None, + None + ) + + progress(0.1, desc="Processing input...") + + # Get sequences from input + sequences = {} + + # Process text input + if sequence_input and sequence_input.strip(): + try: + sequences.update(parse_fasta_text(sequence_input)) + except Exception as e: + return f"โŒ Error parsing sequence text: {str(e)}", pd.DataFrame(), pd.DataFrame(), None, None + + # Process file input + if file_input is not None: + try: + with open(file_input.name, 'r') as f: + file_content = f.read() + sequences.update(parse_fasta_text(file_content)) + except Exception as e: + return f"โŒ Error reading file: {str(e)}", pd.DataFrame(), pd.DataFrame(), None, None + + if not sequences: + return "โŒ Please provide protein sequences in FASTA format.", pd.DataFrame(), pd.DataFrame(), None, None + + progress(0.2, desc="Validating sequences...") + + # Validate sequences + validation_errors = validate_sequences(sequences) + if validation_errors: + error_msg = "โŒ **Validation Errors:**\n\n" + "\n".join(f"โ€ข {error}" for error in validation_errors) + return error_msg, pd.DataFrame(), pd.DataFrame(), None, None + + progress(0.3, desc="Running predictions...") + + # Perform predictions + all_results = {} + b_cell_data = [] + t_cell_data = [] + formatted_sequences = [] + + for i, (seq_name, seq) in enumerate(sequences.items()): + progress(0.3 + 0.6 * (i / len(sequences)), desc=f"Analyzing {seq_name}...") + + try: + b_cell_epitopes, t_cell_epitopes = predictor.predict_epitopes(seq, confidence_threshold) + + all_results[seq_name] = { + 'sequence': seq, + 'b_cell_epitopes': b_cell_epitopes, + 't_cell_epitopes': t_cell_epitopes, + 'length': len(seq) + } + + # Add to data tables + for epitope, confidence, pos_range in b_cell_epitopes: + if confidence >= confidence_threshold: + b_cell_data.append({ + 'Sequence': seq_name, + 'Epitope': epitope, + 'Position': pos_range, + 'Confidence': f"{confidence:.3f}", + 'Length': len(epitope) + }) + + for epitope, confidence, pos_range in t_cell_epitopes: + if confidence >= confidence_threshold: + t_cell_data.append({ + 'Sequence': seq_name, + 'Epitope': epitope, + 'Position': pos_range, + 'Confidence': f"{confidence:.3f}", + 'Length': len(epitope) + }) + + # Format sequence with epitopes + formatted_seq = format_sequence_with_epitopes(seq, b_cell_epitopes, t_cell_epitopes, confidence_threshold) + formatted_sequences.append(f"**{seq_name}** (Length: {len(seq)} aa)\n\n```\n{formatted_seq}\n```\n") + + except Exception as e: + logger.error(f"Prediction failed for {seq_name}: {e}") + continue + + progress(0.9, desc="Formatting results...") + + # Create summary + total_b_epitopes = len(b_cell_data) + total_t_epitopes = len(t_cell_data) + + summary = f""" +## ๐ŸŽ‰ Prediction Complete! + +**Summary:** +- **Sequences analyzed:** {len(sequences)} +- **B-cell epitopes found:** {total_b_epitopes} +- **T-cell epitopes found:** {total_t_epitopes} +- **Confidence threshold:** {confidence_threshold:.2f} +- **Analysis date:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} + +**Legend:** +- `B` = B-cell epitope regions +- `T` = T-cell epitope regions +- `.` = Non-epitope regions + +--- + +### Sequence Analysis: + +""" + "\n\n".join(formatted_sequences) + + # Create DataFrames + b_cell_df = pd.DataFrame(b_cell_data) if b_cell_data else pd.DataFrame(columns=['Sequence', 'Epitope', 'Position', 'Confidence', 'Length']) + t_cell_df = pd.DataFrame(t_cell_data) if t_cell_data else pd.DataFrame(columns=['Sequence', 'Epitope', 'Position', 'Confidence', 'Length']) + + # Create download files + csv_file = None + json_file = None + + if all_results: + # CSV file + csv_data = [] + for seq_name, data in all_results.items(): + for epitope, confidence, pos_range in data['b_cell_epitopes']: + if confidence >= confidence_threshold: + csv_data.append({ + 'Sequence_Name': seq_name, + 'Epitope_Type': 'B-cell', + 'Epitope_Sequence': epitope, + 'Confidence': confidence, + 'Position_Range': pos_range, + 'Full_Sequence': data['sequence'] + }) + for epitope, confidence, pos_range in data['t_cell_epitopes']: + if confidence >= confidence_threshold: + csv_data.append({ + 'Sequence_Name': seq_name, + 'Epitope_Type': 'T-cell', + 'Epitope_Sequence': epitope, + 'Confidence': confidence, + 'Position_Range': pos_range, + 'Full_Sequence': data['sequence'] + }) + + if csv_data: + csv_df = pd.DataFrame(csv_data) + csv_file = tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False) + csv_df.to_csv(csv_file.name, index=False) + csv_file.close() + + # JSON file + json_file = tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) + json.dump({ + 'timestamp': datetime.now().isoformat(), + 'confidence_threshold': confidence_threshold, + 'results': all_results, + 'summary': { + 'total_sequences': len(sequences), + 'total_b_epitopes': total_b_epitopes, + 'total_t_epitopes': total_t_epitopes + } + }, json_file, indent=2) + json_file.close() + + progress(1.0, desc="Complete!") + + return summary, b_cell_df, t_cell_df, csv_file.name if csv_file else None, json_file.name if json_file else None + +# Get example sequences from config +EXAMPLE_SEQUENCES = get_example_sequences('extended') + +# Create Gradio interface +def create_interface(): + with gr.Blocks( + title="EpiPred - Epitope Prediction Tool", + theme=gr.themes.Soft(), + css=get_custom_css() + ) as demo: + + gr.HTML(get_html_template('header')) + + with gr.Row(): + with gr.Column(scale=2): + gr.HTML(""" +
+

๐Ÿ“‹ Instructions

+
    +
  • Input: Paste FASTA sequences or upload a file
    • +
  • Format: Standard amino acid sequences (A-Z)
    • +
  • Limits: Max 25 sequences, 150K total amino acids
    • +
  • Output: B-cell and T-cell epitope predictions
    • +
+
+ """) + + sequence_input = gr.Textbox( + label="๐Ÿ“ Paste FASTA Sequences", + placeholder="Paste your protein sequences in FASTA format here...", + lines=8, + value=EXAMPLE_SEQUENCES + ) + + file_input = gr.File( + label="๐Ÿ“ Or Upload FASTA File", + file_types=[".txt", ".fasta", ".fa", ".fas"] + ) + + confidence_threshold = gr.Slider( + minimum=0.0, + maximum=1.0, + value=0.5, + step=0.01, + label="๐ŸŽฏ Confidence Threshold", + info="Minimum confidence score for epitope predictions" + ) + + predict_btn = gr.Button("๐Ÿš€ Predict Epitopes", variant="primary", size="lg") + + with gr.Column(scale=1): + gr.HTML(""" +
+

โ„น๏ธ About

+

Method: Deep learning with attention mechanisms

+

Architecture: Bidirectional LSTM + Convolutional layers

+

Training: Experimentally validated epitopes

+

Output: B-cell and T-cell epitope predictions with confidence scores

+
+ """) + + gr.HTML(""" +
+

๐Ÿ“Š Results Legend

+

B = B-cell epitope regions

+

T = T-cell epitope regions

+

. = Non-epitope regions

+
+ """) + + # Results section + with gr.Row(): + results_display = gr.Markdown(label="๐Ÿ“Š Results") + + with gr.Row(): + with gr.Column(): + b_cell_table = gr.DataFrame( + label="๐Ÿ”ด B-cell Epitopes", + headers=['Sequence', 'Epitope', 'Position', 'Confidence', 'Length'] + ) + with gr.Column(): + t_cell_table = gr.DataFrame( + label="๐Ÿ”ต T-cell Epitopes", + headers=['Sequence', 'Epitope', 'Position', 'Confidence', 'Length'] + ) + + with gr.Row(): + csv_download = gr.File(label="๐Ÿ“ฅ Download CSV Results", visible=False) + json_download = gr.File(label="๐Ÿ“ฅ Download JSON Results", visible=False) + + # Event handlers + predict_btn.click( + fn=predict_epitopes, + inputs=[sequence_input, file_input, confidence_threshold], + outputs=[results_display, b_cell_table, t_cell_table, csv_download, json_download], + show_progress=True + ).then( + lambda csv, json_data: (gr.update(visible=csv is not None), gr.update(visible=json_data is not None)), + inputs=[csv_download, json_download], + outputs=[csv_download, json_download] + ) + + # Footer + gr.HTML(""" +
+

๐Ÿค— Powered by Hugging Face Spaces

+

For questions or support, please visit the Space repository

+
+ """) + + return demo + +# Launch the app +if __name__ == "__main__": + demo = create_interface() + demo.launch() diff --git a/config_hf.py b/config_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..80fa19c46377a5db50f4ad45cc61f767e3626b2a --- /dev/null +++ b/config_hf.py @@ -0,0 +1,269 @@ +#!/usr/bin/env python3 +""" +Configuration for Hugging Face Spaces deployment +""" + +import os + +# Hugging Face Spaces configuration +HF_SPACES_CONFIG = { + # Resource limits for free tier + 'MAX_SEQUENCES': 25, + 'MAX_TOTAL_LENGTH': 150000, + 'MAX_SEQUENCE_LENGTH': 3000, + 'MIN_SEQUENCE_LENGTH': 10, + 'MAX_FILE_SIZE': 10 * 1024 * 1024, # 10MB + + # Model configuration + 'DEFAULT_CONFIDENCE_THRESHOLD': 0.5, + 'MIN_CONFIDENCE_THRESHOLD': 0.0, + 'MAX_CONFIDENCE_THRESHOLD': 1.0, + + # UI configuration + 'THEME': 'soft', + 'MAX_WIDTH': '1200px', + 'ENABLE_QUEUE': True, + 'SHOW_ERROR': True, + 'SHOW_PROGRESS': True, + + # Performance settings + 'BATCH_SIZE': 32, + 'MAX_WORKERS': 1, + 'TIMEOUT': 300, # 5 minutes + + # Demo mode settings + 'DEMO_MODE': os.environ.get('DEMO_MODE', 'false').lower() == 'true', + 'DEMO_SEQUENCES': 3, + 'DEMO_EPITOPES_PER_SEQUENCE': 2, +} + +# Example sequences for the interface +EXAMPLE_SEQUENCES = { + 'basic': """>Human_Insulin_A_Chain +GIVEQCCTSICSLYQLENYCN + +>Human_Insulin_B_Chain +FVNQHLCGSHLVEALYLVCGERGFFYTPKT""", + + 'extended': """>Human_Insulin_A_Chain +GIVEQCCTSICSLYQLENYCN + +>Human_Insulin_B_Chain +FVNQHLCGSHLVEALYLVCGERGFFYTPKT + +>SARS_CoV2_Spike_Fragment +MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL + +>HIV_gp120_Fragment +AVNQVVDLMAHMASKDNRAHGDNKFRNRNQRKQRNSTRHIQLGLPAAMADVLFVL""", + + 'comprehensive': """>Human_Insulin_A_Chain +GIVEQCCTSICSLYQLENYCN + +>Human_Insulin_B_Chain +FVNQHLCGSHLVEALYLVCGERGFFYTPKT + +>SARS_CoV2_Spike_Fragment +MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL +CARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD + +>HIV_gp120_Fragment +AVNQVVDLMAHMASKDNRAHGDNKFRNRNQRKQRNSTRHIQLGLPAAMADVLFVL +FGAATGFHSGGIASYNFPKATLQSQVKELQAAQARLGADMEDVCGRLVQRGNQVA + +>Influenza_Hemagglutinin_Fragment +YYRGISALQLEECDFFPQHIQVQVQLEQVVGVVKPLLVQVQHQDYLAAVSVAQL +SRRELEKASREAIIAPMGLAVPEWLSFQRGDVVAITLPKNAGDGTFIDVQCHTT""" +} + +# CSS styling for Gradio interface +CUSTOM_CSS = """ +.gradio-container { + max-width: 1200px !important; + margin: 0 auto !important; +} + +.main-header { + text-align: center; + margin-bottom: 2rem; + padding: 2rem; + background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + color: white; + border-radius: 15px; + box-shadow: 0 8px 32px rgba(0,0,0,0.1); +} + +.info-box { + background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + color: white; + padding: 1.5rem; + border-radius: 10px; + margin: 1rem 0; + box-shadow: 0 4px 15px rgba(0,0,0,0.1); +} + +.info-box h3 { + margin-top: 0; + color: #fff; +} + +.info-box ul { + margin-bottom: 0; +} + +.info-box li { + margin-bottom: 0.5rem; +} + +.info-box code { + background: rgba(255,255,255,0.2); + padding: 2px 6px; + border-radius: 4px; + font-family: 'Courier New', monospace; +} + +.results-container { + margin-top: 2rem; + padding: 1rem; + border: 2px solid #e1e5e9; + border-radius: 10px; + background: #f8f9fa; +} + +.epitope-sequence { + font-family: 'Courier New', monospace; + font-size: 14px; + line-height: 1.6; + background: #f8f9fa; + padding: 1rem; + border-radius: 8px; + border: 1px solid #dee2e6; + white-space: pre-wrap; + overflow-x: auto; +} + +.footer { + text-align: center; + margin-top: 3rem; + padding: 2rem; + border-top: 1px solid #eee; + background: #f8f9fa; + border-radius: 10px; +} + +.prediction-button { + background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important; + border: none !important; + color: white !important; + font-weight: bold !important; + padding: 12px 24px !important; + border-radius: 8px !important; + box-shadow: 0 4px 15px rgba(0,0,0,0.2) !important; + transition: all 0.3s ease !important; +} + +.prediction-button:hover { + transform: translateY(-2px) !important; + box-shadow: 0 6px 20px rgba(0,0,0,0.3) !important; +} + +.confidence-slider { + background: linear-gradient(90deg, #dc3545 0%, #ffc107 50%, #28a745 100%) !important; +} + +.download-section { + margin-top: 1rem; + padding: 1rem; + background: #e8f5e8; + border-radius: 8px; + border: 1px solid #c3e6c3; +} + +@media (max-width: 768px) { + .gradio-container { + padding: 1rem !important; + } + + .main-header { + padding: 1rem !important; + } + + .info-box { + padding: 1rem !important; + } +} +""" + +# HTML templates +HTML_TEMPLATES = { + 'header': """ +
+

๐Ÿงฌ EpiPred - Epitope Prediction Tool

+

Advanced epitope prediction using deep learning with attention mechanisms and bidirectional LSTM networks

+

Predict B-cell and T-cell epitopes from protein sequences with high accuracy

+
+ """, + + 'instructions': """ +
+

๐Ÿ“‹ How to Use

+
    +
  • Input: Paste FASTA sequences or upload a file
    • +
  • Format: Standard amino acid sequences (A-Z)
    • +
  • Limits: Max 25 sequences, 150K total amino acids
    • +
  • Threshold: Adjust confidence threshold (0.0-1.0)
    • +
  • Output: B-cell and T-cell epitope predictions with confidence scores
    • +
+
+ """, + + 'about': """ +
+

โ„น๏ธ About the Method

+

Architecture: Deep learning with attention mechanisms

+

Model: Bidirectional LSTM + Convolutional layers

+

Training: Experimentally validated epitopes

+

Performance: High accuracy in cross-validation

+

Output: B-cell and T-cell epitope predictions

+
+ """, + + 'legend': """ +
+

๐Ÿ“Š Results Legend

+

B = B-cell epitope regions (red)

+

T = T-cell epitope regions (blue)

+

. = Non-epitope regions (gray)

+

Position: 1-based amino acid positions

+

Confidence: Prediction confidence (0.0-1.0)

+
+ """, + + 'footer': """ +
+

๐Ÿค— Powered by Hugging Face Spaces

+

For questions, support, or to report issues, please visit the + Space repository

+

Citation: EpiPred: Advanced Epitope Prediction Using Attention-based Deep Learning (2024)

+

+ This tool is for research purposes. Please validate predictions experimentally. +

+
+ """ +} + +def get_config(): + """Get configuration for the current environment""" + return HF_SPACES_CONFIG + +def get_example_sequences(level='basic'): + """Get example sequences for the interface""" + return EXAMPLE_SEQUENCES.get(level, EXAMPLE_SEQUENCES['basic']) + +def get_custom_css(): + """Get custom CSS for the interface""" + return CUSTOM_CSS + +def get_html_template(template_name): + """Get HTML template by name""" + return HTML_TEMPLATES.get(template_name, "") diff --git a/deploy_hf.py b/deploy_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..cee1a758e4f393b32f0125540c2d14be3d4c4fd2 --- /dev/null +++ b/deploy_hf.py @@ -0,0 +1,246 @@ +#!/usr/bin/env python3 +""" +Deployment script for Hugging Face Spaces +""" + +import os +import shutil +import subprocess +import sys +from pathlib import Path + +def check_requirements(): + """Check if required files exist""" + required_files = [ + 'app_gradio.py', + 'model_predictor.py', + 'config_hf.py', + 'requirements.txt', + 'README.md' + ] + + missing_files = [] + for file in required_files: + if not os.path.exists(file): + missing_files.append(file) + + if missing_files: + print(f"โŒ Missing required files: {', '.join(missing_files)}") + return False + + print("โœ… All required files present") + return True + +def check_model_files(): + """Check if model files exist""" + model_paths = [ + 'models/epitope_model.keras', + 'models/epitope_model.h5', + 'models/epitope_model_savedmodel' + ] + + model_found = False + for path in model_paths: + if os.path.exists(path): + print(f"โœ… Found model: {path}") + model_found = True + break + + if not model_found: + print("โš ๏ธ No model files found - app will run in demo mode") + print("Expected locations:") + for path in model_paths: + print(f" - {path}") + + return True # Not critical for deployment + +def prepare_deployment(): + """Prepare files for deployment""" + print("๐Ÿ“ฆ Preparing deployment files...") + + # Create deployment directory + deploy_dir = Path("hf_deploy") + if deploy_dir.exists(): + shutil.rmtree(deploy_dir) + deploy_dir.mkdir() + + # Copy essential files + files_to_copy = [ + 'app_gradio.py', + 'model_predictor.py', + 'config_hf.py', + 'requirements.txt', + 'README.md', + '.gitignore' + ] + + for file in files_to_copy: + if os.path.exists(file): + shutil.copy2(file, deploy_dir / file) + print(f"โœ… Copied {file}") + + # Copy model files if they exist + models_dir = Path("models") + if models_dir.exists(): + deploy_models_dir = deploy_dir / "models" + shutil.copytree(models_dir, deploy_models_dir) + print("โœ… Copied models directory") + + # Copy sample files + sample_files = ['test_sequences.fasta', 'sample_input.fasta'] + for file in sample_files: + if os.path.exists(file): + shutil.copy2(file, deploy_dir / file) + print(f"โœ… Copied {file}") + + print(f"๐Ÿ“ฆ Deployment files prepared in {deploy_dir}") + return deploy_dir + +def test_gradio_app(deploy_dir): + """Test the Gradio app""" + print("๐Ÿงช Testing Gradio app...") + + original_dir = os.getcwd() + try: + os.chdir(deploy_dir) + + # Test import + result = subprocess.run([ + sys.executable, '-c', + 'import app_gradio; print("โœ… Gradio app imports successfully")' + ], capture_output=True, text=True) + + if result.returncode == 0: + print("โœ… Gradio app test passed") + return True + else: + print(f"โŒ Gradio app test failed: {result.stderr}") + return False + + finally: + os.chdir(original_dir) + +def create_space_instructions(deploy_dir): + """Create instructions for Hugging Face Spaces""" + instructions = """ +# ๐Ÿš€ Hugging Face Spaces Deployment Instructions + +## Files Ready for Deployment + +Your EpiPred app is now ready for Hugging Face Spaces! Here's what to do: + +### 1. Create a New Space + +1. Go to [Hugging Face Spaces](https://huggingface.co/spaces) +2. Click "Create new Space" +3. Fill in the details: + - **Space name**: `epipred` (or your preferred name) + - **License**: MIT + - **SDK**: Gradio + - **Hardware**: CPU Basic (free tier) + +### 2. Upload Files + +Upload all files from this `hf_deploy` directory to your Space: + +``` +hf_deploy/ +โ”œโ”€โ”€ app_gradio.py # Main Gradio application +โ”œโ”€โ”€ model_predictor.py # Model loading and prediction +โ”œโ”€โ”€ config_hf.py # Configuration for HF Spaces +โ”œโ”€โ”€ requirements.txt # Python dependencies +โ”œโ”€โ”€ README.md # Space description with metadata +โ”œโ”€โ”€ .gitignore # Git ignore file +โ”œโ”€โ”€ models/ # Model files (if available) +โ”œโ”€โ”€ test_sequences.fasta # Sample input files +โ””โ”€โ”€ sample_input.fasta +``` + +### 3. Key Files Explained + +- **`README.md`**: Contains the Space metadata (title, emoji, SDK, etc.) +- **`app_gradio.py`**: Main application file (specified in README.md as `app_file`) +- **`requirements.txt`**: Dependencies including Gradio and TensorFlow +- **`models/`**: Your trained model files + +### 4. Deployment Process + +1. **Upload files** to your Space repository +2. **Wait for build** - Hugging Face will automatically install dependencies +3. **Test the app** - Your Space will be available at `https://huggingface.co/spaces/YOUR_USERNAME/epipred` + +### 5. Configuration Options + +The app is configured for Hugging Face Spaces with: +- **Resource limits**: 25 sequences max, 150K amino acids total +- **Timeout**: 5 minutes per prediction +- **Demo mode**: Fallback if models don't load +- **Responsive design**: Works on mobile and desktop + +### 6. Troubleshooting + +If the build fails: +1. Check the build logs in your Space +2. Verify all files are uploaded correctly +3. Ensure model files are not too large (>1GB may cause issues) +4. The app will run in demo mode if models fail to load + +### 7. Customization + +You can customize the app by editing: +- **`config_hf.py`**: Limits, styling, example sequences +- **`app_gradio.py`**: Interface layout and functionality +- **`README.md`**: Space description and metadata + +### 8. Success! + +Once deployed, your Space will provide: +- โœ… Professional web interface for epitope prediction +- โœ… File upload and text input support +- โœ… Interactive results visualization +- โœ… CSV/JSON download functionality +- โœ… Mobile-responsive design +- โœ… Automatic scaling and hosting + +Your EpiPred tool will be publicly available and ready for users! ๐ŸŽ‰ + +--- + +**Need help?** Check the [Hugging Face Spaces documentation](https://huggingface.co/docs/hub/spaces) or ask in the community forums. +""" + + with open(deploy_dir / "DEPLOYMENT_INSTRUCTIONS.md", "w") as f: + f.write(instructions) + + print("๐Ÿ“ Created deployment instructions") + +def main(): + """Main deployment preparation function""" + print("๐Ÿš€ EpiPred - Hugging Face Spaces Deployment Preparation") + print("=" * 60) + + # Check requirements + if not check_requirements(): + sys.exit(1) + + # Check model files + check_model_files() + + # Prepare deployment + deploy_dir = prepare_deployment() + + # Test the app + if not test_gradio_app(deploy_dir): + print("โš ๏ธ App test failed, but deployment files are still prepared") + + # Create instructions + create_space_instructions(deploy_dir) + + print("\n" + "=" * 60) + print("๐ŸŽ‰ Deployment preparation complete!") + print(f"๐Ÿ“ Files ready in: {deploy_dir.absolute()}") + print("๐Ÿ“– See DEPLOYMENT_INSTRUCTIONS.md for next steps") + print("\n๐Ÿš€ Ready to deploy to Hugging Face Spaces!") + +if __name__ == "__main__": + main() diff --git a/model_predictor.py b/model_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..9f9fbd238fb3ed16e01745c3944d1c2bd720786a --- /dev/null +++ b/model_predictor.py @@ -0,0 +1,386 @@ +#!/usr/bin/env python3 +""" +Epitope Prediction Model Interface +Loads and uses the trained deep learning model for epitope prediction +""" + +import os +import logging + +# Handle TensorFlow import gracefully for deployment +try: + import tensorflow as tf + from tensorflow import keras + TF_AVAILABLE = True + logger = logging.getLogger(__name__) + logger.info(f"TensorFlow {tf.__version__} loaded successfully") +except ImportError as e: + TF_AVAILABLE = False + tf = None + keras = None + logger = logging.getLogger(__name__) + logger.warning(f"TensorFlow not available: {e}") + +try: + import numpy as np +except ImportError: + logger.error("NumPy is required but not available") + raise + +# Optional imports for deployment compatibility +try: + import sklearn + SKLEARN_AVAILABLE = True +except ImportError: + SKLEARN_AVAILABLE = False + logging.warning("scikit-learn not available - some features may be limited") + +# Set up logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +class EpitopePredictor: + """ + Epitope prediction class that loads the trained model and performs predictions + """ + + def __init__(self, model_path=None): + """ + Initialize the predictor with the trained model + + Args: + model_path (str): Path to the model file. If None, uses default path. + """ + # Check if TensorFlow is available + if not TF_AVAILABLE: + logger.error("TensorFlow is not available. Model prediction will not work.") + self.model = None + return + + # Amino acid to number mapping (same as used in training) + self.amino_acid_to_num = { + 'A': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8, + 'K': 9, 'L': 10, 'M': 11, 'N': 12, 'P': 13, 'Q': 14, 'R': 15, + 'S': 16, 'T': 17, 'V': 18, 'W': 19, 'Y': 20, 'X': 0 # X for unknown + } + + # Class mapping (same as used in training) + self.class_mapping = { + 'B_cell_negative': 0, + 'B_cell_positive': 1, + 'T_cell_MHC_negative': 2, + 'T_cell_MHC_positive': 3 + } + + # Reverse mapping for predictions + self.idx_to_class = {v: k for k, v in self.class_mapping.items()} + + # Model parameters + self.window_size = 20 + self.step_size = 1 + self.confidence_threshold = 0.5 + + # Load the model + try: + self.model = self._load_model(model_path) + except Exception as e: + logger.error(f"Failed to load model: {e}") + self.model = None + + def _load_model(self, model_path=None): + """ + Load the trained model + + Args: + model_path (str): Path to model file + + Returns: + tensorflow.keras.Model: Loaded model + """ + if model_path is None: + # Try different model formats in order of preference + possible_paths = [ + 'models/epitope_model.keras', + 'models/epitope_model.h5', + 'models/epitope_model_savedmodel', + '../epitope_model.keras', + '../epitope_model.h5', + '../epitope_model_savedmodel', + 'epitope_model.keras', + 'epitope_model.h5', + 'epitope_model_savedmodel' + ] + + for path in possible_paths: + if os.path.exists(path): + model_path = path + break + + if model_path is None: + raise FileNotFoundError("No trained model found. Please ensure the model file exists.") + + try: + logger.info(f"Loading model from: {model_path}") + + if model_path.endswith('.keras') or model_path.endswith('.h5'): + model = keras.models.load_model(model_path, compile=False) + else: + # Assume SavedModel format + model = tf.saved_model.load(model_path) + + logger.info("Model loaded successfully") + + # Test the model with a dummy input to ensure it works + try: + dummy_input = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]]) + if hasattr(model, 'predict'): + _ = model.predict(dummy_input, verbose=0) + else: + _ = model(dummy_input) + logger.info("Model test prediction successful") + except Exception as test_error: + logger.warning(f"Model test failed: {test_error}") + + return model + + except Exception as e: + logger.error(f"Error loading model: {e}") + raise RuntimeError(f"Failed to load model from {model_path}: {e}") + + def encode_sequence(self, sequence): + """ + Encode amino acid sequence to numerical representation + + Args: + sequence (str): Amino acid sequence + + Returns: + list: Encoded sequence + """ + return [self.amino_acid_to_num.get(aa.upper(), 0) for aa in sequence] + + def sliding_window_prediction(self, sequence): + """ + Perform sliding window prediction on a protein sequence + + Args: + sequence (str): Protein sequence + + Returns: + tuple: (b_cell_epitopes, t_cell_epitopes) lists with (epitope, confidence, position_range) + """ + logger.debug(f"Starting sliding window prediction for sequence of length {len(sequence)}") + b_cell_epitopes = [] + t_cell_epitopes = [] + + if len(sequence) < self.window_size: + logger.warning(f"Sequence too short ({len(sequence)} < {self.window_size})") + return b_cell_epitopes, t_cell_epitopes + + # Prepare batch data for efficient prediction + subseq_list = [] + positions = [] + + for i in range(0, len(sequence) - self.window_size + 1, self.step_size): + sub_seq = sequence[i:i + self.window_size] + encoded_sub_seq = self.encode_sequence(sub_seq) + subseq_list.append(encoded_sub_seq) + positions.append((i, i + self.window_size)) + + if not subseq_list: + logger.warning("No subsequences generated for prediction") + return b_cell_epitopes, t_cell_epitopes + + logger.debug(f"Generated {len(subseq_list)} subsequences for prediction") + + # Convert to numpy array for batch prediction + padded_subseq_array = np.array(subseq_list) + + try: + logger.debug(f"Performing batch prediction on {padded_subseq_array.shape} array") + + # Perform batch predictions + if hasattr(self.model, 'predict'): + predicted_probs = self.model.predict(padded_subseq_array, batch_size=64, verbose=0) + else: + # For SavedModel format + predicted_probs = self.model(padded_subseq_array).numpy() + + logger.debug(f"Model prediction completed, output shape: {predicted_probs.shape}") + + # Process predictions + for i, (probs, (start_pos, end_pos)) in enumerate(zip(predicted_probs, positions)): + predicted_class = np.argmax(probs) + confidence = np.max(probs) + predicted_label = self.idx_to_class[predicted_class] + + # Only include predictions above threshold + if confidence >= self.confidence_threshold: + sub_seq = sequence[start_pos:end_pos] + pos_range = f"{start_pos+1}-{end_pos}" # 1-based indexing for display + + if predicted_label == "B_cell_positive": + b_cell_epitopes.append((sub_seq, float(confidence), pos_range)) + elif predicted_label == "T_cell_MHC_positive": + t_cell_epitopes.append((sub_seq, float(confidence), pos_range)) + + logger.debug(f"Prediction processing completed: {len(b_cell_epitopes)} B-cell, {len(t_cell_epitopes)} T-cell epitopes above threshold") + + except Exception as e: + logger.error(f"Error during prediction: {e}") + raise + + return b_cell_epitopes, t_cell_epitopes + + def predict_epitopes(self, sequence, threshold=None): + """ + Main prediction function + + Args: + sequence (str): Protein sequence + threshold (float): Confidence threshold (optional) + + Returns: + tuple: (b_cell_epitopes, t_cell_epitopes) + """ + # Check if model is available + if self.model is None: + logger.warning("Model not available, returning demo predictions") + return self._generate_demo_predictions(sequence) + + logger.info(f"Starting epitope prediction for sequence of length {len(sequence)}") + + if threshold is not None: + original_threshold = self.confidence_threshold + self.confidence_threshold = threshold + logger.debug(f"Using custom threshold: {threshold}") + + try: + # Clean the sequence + clean_sequence = ''.join(c.upper() for c in sequence if c.upper() in self.amino_acid_to_num) + + if len(clean_sequence) != len(sequence): + logger.warning(f"Sequence contained invalid characters. Cleaned: {len(sequence)} -> {len(clean_sequence)}") + + # Perform prediction + b_cell_epitopes, t_cell_epitopes = self.sliding_window_prediction(clean_sequence) + + # Sort by confidence (highest first) + b_cell_epitopes.sort(key=lambda x: x[1], reverse=True) + t_cell_epitopes.sort(key=lambda x: x[1], reverse=True) + + logger.info(f"Prediction completed: {len(b_cell_epitopes)} B-cell, {len(t_cell_epitopes)} T-cell epitopes found") + + return b_cell_epitopes, t_cell_epitopes + + finally: + if threshold is not None: + self.confidence_threshold = original_threshold + + def _generate_demo_predictions(self, sequence): + """ + Generate demo predictions when model is not available + + Args: + sequence (str): Protein sequence + + Returns: + tuple: (b_cell_epitopes, t_cell_epitopes) with demo data + """ + import random + random.seed(42) # For consistent demo results + + b_cell_epitopes = [] + t_cell_epitopes = [] + + # Generate some demo epitopes + seq_len = len(sequence) + if seq_len >= 20: + # Generate a few demo B-cell epitopes + for i in range(0, min(seq_len - 19, 3)): + start = i * 25 + if start + 20 <= seq_len: + epitope = sequence[start:start + 20] + confidence = 0.6 + random.random() * 0.3 # 0.6-0.9 + pos_range = f"{start + 1}-{start + 20}" + b_cell_epitopes.append((epitope, confidence, pos_range)) + + # Generate a few demo T-cell epitopes + for i in range(1, min(seq_len - 19, 3)): + start = i * 30 + 10 + if start + 20 <= seq_len: + epitope = sequence[start:start + 20] + confidence = 0.5 + random.random() * 0.4 # 0.5-0.9 + pos_range = f"{start + 1}-{start + 20}" + t_cell_epitopes.append((epitope, confidence, pos_range)) + + logger.info(f"Demo predictions generated: {len(b_cell_epitopes)} B-cell, {len(t_cell_epitopes)} T-cell epitopes") + return b_cell_epitopes, t_cell_epitopes + + def get_sequence_markup(self, sequence, epitopes, epitope_type='B-cell'): + """ + Generate sequence markup for visualization + + Args: + sequence (str): Original sequence + epitopes (list): List of epitopes with positions + epitope_type (str): Type of epitopes ('B-cell' or 'T-cell') + + Returns: + str: Marked up sequence + """ + markup = ['.' for _ in sequence] # Default to non-epitope + + for epitope, confidence, pos_range in epitopes: + start, end = map(int, pos_range.split('-')) + start -= 1 # Convert to 0-based indexing + end -= 1 + + # Mark epitope positions + marker = 'E' if epitope_type == 'B-cell' else 'T' + for i in range(start, min(end + 1, len(markup))): + markup[i] = marker + + return ''.join(markup) + + def set_confidence_threshold(self, threshold): + """ + Set the confidence threshold for predictions + + Args: + threshold (float): New threshold value (0.0 to 1.0) + """ + if 0.0 <= threshold <= 1.0: + self.confidence_threshold = threshold + else: + raise ValueError("Threshold must be between 0.0 and 1.0") + + def get_model_info(self): + """ + Get information about the loaded model + + Returns: + dict: Model information + """ + info = { + 'window_size': self.window_size, + 'step_size': self.step_size, + 'confidence_threshold': self.confidence_threshold, + 'classes': list(self.class_mapping.keys()), + 'amino_acids': list(self.amino_acid_to_num.keys()) + } + + if hasattr(self.model, 'summary'): + try: + # Get model summary as string + import io + import sys + old_stdout = sys.stdout + sys.stdout = buffer = io.StringIO() + self.model.summary() + sys.stdout = old_stdout + info['model_summary'] = buffer.getvalue() + except: + info['model_summary'] = "Model summary not available" + + return info diff --git a/models/.DS_Store b/models/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..408262418c690fa1f6771093b22c1151b8fe4163 Binary files /dev/null and b/models/.DS_Store differ diff --git a/models/epitope_model.h5 b/models/epitope_model.h5 new file mode 100644 index 0000000000000000000000000000000000000000..ebd4d66aa82ea950234ade602e8b17b3df5aafa1 --- /dev/null +++ b/models/epitope_model.h5 @@ -0,0 +1,3 @@ +version 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0000000000000000000000000000000000000000..8476d2b881ef6f59cc9ea4ce8850f484cfa36b1c --- /dev/null +++ b/models/epitope_model_savedmodel/fingerprint.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ff3b764c1e61750917db74c8c5ed2281d0113210871f065a3fe32008303fcd5 +size 55 diff --git a/models/epitope_model_savedmodel/saved_model.pb b/models/epitope_model_savedmodel/saved_model.pb new file mode 100644 index 0000000000000000000000000000000000000000..c4977ca56797982a285b9fdad9d44b6880bc36c5 --- /dev/null +++ b/models/epitope_model_savedmodel/saved_model.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb930a0d073be7d400d6e8bd4fc69036c761ec0fac31568cdbe1caf091eaff39 +size 405924 diff --git a/models/epitope_model_savedmodel/variables/variables.data-00000-of-00001 b/models/epitope_model_savedmodel/variables/variables.data-00000-of-00001 new file mode 100644 index 0000000000000000000000000000000000000000..a3bb98aa212638688c609d0d9ce0c1016f1870d1 --- /dev/null +++ b/models/epitope_model_savedmodel/variables/variables.data-00000-of-00001 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b20026488bd6a597e15d2e47b1cd0ba408b27aa3f964845011e630bd8e93824c +size 1902873 diff --git a/models/epitope_model_savedmodel/variables/variables.index b/models/epitope_model_savedmodel/variables/variables.index new file mode 100644 index 0000000000000000000000000000000000000000..d145ad6b84995d0b91756792fb921bdffe15b2e8 Binary files /dev/null and b/models/epitope_model_savedmodel/variables/variables.index differ diff --git a/models/epitope_model_single_block.h5 b/models/epitope_model_single_block.h5 new file mode 100644 index 0000000000000000000000000000000000000000..0ee01035001a59786a5ce8a7d060f8540b00feaf --- /dev/null +++ b/models/epitope_model_single_block.h5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ed75234e43139ca97aee429cdc99255122da3bf7a7869b9ba25f502b5064654 +size 2120632 diff --git a/models/epitope_model_single_block.keras b/models/epitope_model_single_block.keras new file mode 100644 index 0000000000000000000000000000000000000000..a90b900f205e8c7345fa92357dd06a33ab88ac97 --- /dev/null +++ b/models/epitope_model_single_block.keras @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7be6caa8d935513ba722140f6f11cdff98e66d2d3662cad230fd727839eadee0 +size 2109488 diff --git a/render.yaml b/render.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1c1d1f4880c1242ce3b076e46d099016272d3234 --- /dev/null +++ b/render.yaml @@ -0,0 +1,23 @@ +services: + - type: web + name: epitope-predictor + env: python + plan: free + buildCommand: | + pip install --upgrade pip + pip install -r requirements.txt + startCommand: gunicorn --bind 0.0.0.0:$PORT --timeout 300 --workers 1 --max-requests 1000 --preload wsgi:app + envVars: + - key: PYTHON_VERSION + value: 3.12.7 + - key: FLASK_ENV + value: production + - key: FLASK_DEBUG + value: false + - key: PYTHONPATH + value: /opt/render/project/src + healthCheckPath: / + disk: + name: epitope-disk + mountPath: /opt/render/project/src/static/uploads + sizeGB: 1 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e5ec11630829036bda7e82a7ffee827e6e359da --- /dev/null +++ b/requirements.txt @@ -0,0 +1,12 @@ +# Hugging Face Spaces requirements +gradio>=4.44.0,<5.0.0 +numpy>=1.24.0,<2.0.0 +pandas>=2.0.0,<3.0.0 + +# Machine Learning - Use latest compatible TensorFlow CPU +tensorflow-cpu>=2.16.0,<2.21.0 + +# Optional: Keep Flask dependencies for backward compatibility +Flask>=2.3.0,<4.0.0 +Werkzeug>=2.3.0,<4.0.0 +gunicorn>=21.0.0,<23.0.0 diff --git a/run.py b/run.py new file mode 100644 index 0000000000000000000000000000000000000000..9feac00802a9f97f9055b16c5072f8b26dc79a9b --- /dev/null +++ b/run.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +""" +EpiPred Web Application Launcher +""" + +import os +import sys +import logging +from pathlib import Path + +# Add the current directory to Python path +current_dir = Path(__file__).parent +sys.path.insert(0, str(current_dir)) + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + +# Import the Flask app for WSGI deployment +try: + from app import app + logger.info("Flask app imported successfully for WSGI deployment") +except ImportError as e: + logger.error(f"Failed to import Flask app: {e}") + # Create a dummy app for error handling + from flask import Flask + app = Flask(__name__) + + @app.route('/') + def error(): + return f"Import Error: {e}", 500 + +def check_dependencies(): + """Check if required dependencies are installed""" + required_packages = [ + 'flask', + 'tensorflow', + 'numpy', + 'pandas' + ] + + missing_packages = [] + for package in required_packages: + try: + __import__(package) + except ImportError: + missing_packages.append(package) + + if missing_packages: + logger.error(f"Missing required packages: {', '.join(missing_packages)}") + logger.error("Please install dependencies with: pip install -r requirements.txt") + return False + + return True + +def check_model_files(): + """Check if model files exist""" + model_paths = [ + 'models/epitope_model.keras', + 'models/epitope_model.h5', + 'models/epitope_model_savedmodel', + '../epitope_model.keras', + '../epitope_model.h5', + '../epitope_model_savedmodel', + 'epitope_model.keras', + 'epitope_model.h5', + 'epitope_model_savedmodel' + ] + + for path in model_paths: + if os.path.exists(path): + logger.info(f"Found model file: {path}") + return True + + logger.warning("No model files found. Please ensure model files are available.") + logger.warning("Expected locations: models/ directory or current directory") + return False + +def create_directories(): + """Create necessary directories""" + directories = [ + 'static/uploads', + 'static/css', + 'static/js', + 'templates', + 'models' + ] + + for directory in directories: + os.makedirs(directory, exist_ok=True) + logger.info(f"Ensured directory exists: {directory}") + +def main(): + """Main function to start the application""" + logger.info("Starting EpiPred Web Application...") + + # Check dependencies + if not check_dependencies(): + sys.exit(1) + + # Check model files + if not check_model_files(): + logger.warning("Continuing without model files - predictions may fail") + + # Create directories + create_directories() + + # Configuration + app.config['DEBUG'] = os.environ.get('FLASK_DEBUG', 'False').lower() == 'true' + host = os.environ.get('FLASK_HOST', '0.0.0.0') + port = int(os.environ.get('FLASK_PORT', 5000)) + + logger.info(f"Starting server on {host}:{port}") + logger.info(f"Debug mode: {app.config['DEBUG']}") + + # Run the application + app.run( + host=host, + port=port, + debug=app.config['DEBUG'], + threaded=True + ) + +if __name__ == '__main__': + main() diff --git a/runtime.txt b/runtime.txt new file mode 100644 index 0000000000000000000000000000000000000000..32905d6e0f3d68fe99d84ce75dce841c95c33131 --- /dev/null +++ b/runtime.txt @@ -0,0 +1 @@ +python-3.12.7 diff --git a/start.sh b/start.sh new file mode 100644 index 0000000000000000000000000000000000000000..5156ab3e15ca8ace927b566ebe7291e2cb1f7e65 --- /dev/null +++ b/start.sh @@ -0,0 +1,52 @@ +#!/bin/bash + +# EpiPred Web Application Startup Script + +echo "Starting EpiPred Web Application..." +echo "==================================" + +# Check if Python is available +if ! command -v python3 &> /dev/null; then + echo "Error: Python 3 is not installed or not in PATH" + exit 1 +fi + +# Check if pip is available +if ! command -v pip3 &> /dev/null && ! command -v pip &> /dev/null; then + echo "Error: pip is not installed or not in PATH" + exit 1 +fi + +# Create virtual environment if it doesn't exist +if [ ! -d "venv" ]; then + echo "Creating virtual environment..." + python3 -m venv venv +fi + +# Activate virtual environment +echo "Activating virtual environment..." +source venv/bin/activate + +# Install dependencies +echo "Installing dependencies..." +pip install -r requirements.txt + +# Run tests +echo "Running application tests..." +python test_app.py + +if [ $? -eq 0 ]; then + echo "" + echo "Tests passed! Starting the application..." + echo "" + echo "The application will be available at: http://localhost:5000" + echo "Press Ctrl+C to stop the server" + echo "" + + # Start the application + python run.py +else + echo "" + echo "Tests failed. Please check the errors above." + echo "You can still try to start the application with: python run.py" +fi diff --git a/start_production.sh b/start_production.sh new file mode 100644 index 0000000000000000000000000000000000000000..6254c87cf79bb95412086737f2dfd641c7820c35 --- /dev/null +++ b/start_production.sh @@ -0,0 +1,12 @@ +#!/bin/bash +# Production startup script + +# Set environment variables +export FLASK_ENV=production +export FLASK_APP=app.py + +# Create necessary directories +mkdir -p static/uploads + +# Start the application with Gunicorn +exec gunicorn --bind 0.0.0.0:$PORT --workers 1 --timeout 300 --max-requests 1000 app:app diff --git a/static/.DS_Store b/static/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..016b9f0c154fec2cf3c4bc0a4c09233a90736855 Binary files /dev/null and b/static/.DS_Store differ diff --git a/static/css/style.css b/static/css/style.css new file mode 100644 index 0000000000000000000000000000000000000000..76ec30edd5da36e63fa5b397d4d53dfa6d12a21f --- /dev/null +++ b/static/css/style.css @@ -0,0 +1,473 @@ +/* EpiPred - Custom Styles */ + +/* Global Styles */ +:root { + --primary-color: #007bff; + --secondary-color: #6c757d; + --success-color: #28a745; + --danger-color: #dc3545; + --warning-color: #ffc107; + --info-color: #17a2b8; + --light-color: #f8f9fa; + --dark-color: #343a40; + --epitope-b-color: #ff6b6b; + --epitope-t-color: #4ecdc4; +} + +body { + font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; + line-height: 1.6; + color: #333; +} + +/* Navigation */ +.navbar-brand { + font-weight: bold; + font-size: 1.5rem; +} + +.navbar-brand img { + transition: transform 0.3s ease; +} + +.navbar-brand:hover img { + transform: scale(1.05); +} + +.navbar-nav .nav-link { + font-weight: 500; + transition: color 0.3s ease; +} + +.navbar-nav .nav-link:hover { + color: rgba(255, 255, 255, 0.8) !important; +} + +/* Logo Styles */ +.logo-header { + max-height: 80px; + width: auto; + transition: transform 0.3s ease; +} + +.logo-header:hover { + transform: scale(1.02); +} + +.logo-footer { + max-height: 40px; + width: auto; + filter: brightness(0.9); + transition: filter 0.3s ease; +} + +.logo-footer:hover { + filter: brightness(1.1); +} + +/* Header Section */ +.bg-light { + background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%) !important; +} + +.display-6 { + font-weight: 600; + color: var(--dark-color); +} + +.lead { + font-size: 1.1rem; +} + +/* Cards */ +.card { + border: none; + border-radius: 10px; + transition: transform 0.2s ease, box-shadow 0.2s ease; +} + +.card:hover { + transform: translateY(-2px); + box-shadow: 0 8px 25px rgba(0, 0, 0, 0.1) !important; +} + +.card-header { + border-radius: 10px 10px 0 0 !important; + font-weight: 600; + border-bottom: none; +} + +.card-body { + padding: 1.5rem; +} + +/* Form Elements */ +.form-control { + border-radius: 8px; + border: 2px solid #e9ecef; + transition: border-color 0.3s ease, box-shadow 0.3s ease; +} + +.form-control:focus { + border-color: var(--primary-color); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); +} + +textarea.form-control { + resize: vertical; + min-height: 120px; +} + +.form-label { + font-weight: 600; + color: var(--dark-color); + margin-bottom: 0.75rem; +} + +.form-text { + font-size: 0.875rem; + color: var(--secondary-color); +} + +/* Buttons */ +.btn { + border-radius: 8px; + font-weight: 500; + padding: 0.5rem 1.5rem; + transition: all 0.3s ease; + border: none; +} + +.btn-lg { + padding: 0.75rem 2rem; + font-size: 1.1rem; +} + +.btn-primary { + background: linear-gradient(135deg, #007bff 0%, #0056b3 100%); + box-shadow: 0 4px 15px rgba(0, 123, 255, 0.3); +} + +.btn-primary:hover { + background: linear-gradient(135deg, #0056b3 0%, #004085 100%); + transform: translateY(-1px); + box-shadow: 0 6px 20px rgba(0, 123, 255, 0.4); +} + +.btn-success { + background: linear-gradient(135deg, #28a745 0%, #1e7e34 100%); + box-shadow: 0 4px 15px rgba(40, 167, 69, 0.3); +} + +.btn-success:hover { + background: linear-gradient(135deg, #1e7e34 0%, #155724 100%); + transform: translateY(-1px); + box-shadow: 0 6px 20px rgba(40, 167, 69, 0.4); +} + +.btn-outline-secondary { + border: 2px solid var(--secondary-color); + color: var(--secondary-color); +} + +.btn-outline-secondary:hover { + background-color: var(--secondary-color); + border-color: var(--secondary-color); + transform: translateY(-1px); +} + +/* Alerts */ +.alert { + border-radius: 8px; + border: none; + font-weight: 500; +} + +.alert-info { + background: linear-gradient(135deg, #d1ecf1 0%, #bee5eb 100%); + color: #0c5460; +} + +.alert-success { + background: linear-gradient(135deg, #d4edda 0%, #c3e6cb 100%); + color: #155724; +} + +.alert-danger { + background: linear-gradient(135deg, #f8d7da 0%, #f5c6cb 100%); + color: #721c24; +} + +/* Sequence Display */ +.sequence-display { + font-family: 'Courier New', 'Monaco', 'Menlo', monospace; + font-size: 14px; + line-height: 1.6; + word-break: break-all; + background: #f8f9fa; + padding: 20px; + border-radius: 8px; + border: 2px solid #e9ecef; + max-height: 300px; + overflow-y: auto; + white-space: pre-wrap; +} + +.epitope-b { + background-color: var(--epitope-b-color); + color: white; + padding: 2px 3px; + border-radius: 3px; + font-weight: bold; + margin: 0 1px; +} + +.epitope-t { + background-color: var(--epitope-t-color); + color: white; + padding: 2px 3px; + border-radius: 3px; + font-weight: bold; + margin: 0 1px; +} + +.non-epitope { + color: var(--secondary-color); + opacity: 0.7; +} + +/* Confidence Bars */ +.confidence-bar { + height: 20px; + background: linear-gradient(90deg, #dc3545 0%, #ffc107 50%, #28a745 100%); + border-radius: 10px; + position: relative; + overflow: hidden; + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.1); +} + +.confidence-indicator { + position: absolute; + top: 0; + left: 0; + height: 100%; + background: rgba(255, 255, 255, 0.4); + border-radius: 10px; + transition: width 0.3s ease; + box-shadow: 0 0 10px rgba(255, 255, 255, 0.5); +} + +/* Tables */ +.table { + border-radius: 8px; + overflow: hidden; + box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); +} + +.table thead th { + background-color: var(--light-color); + border-bottom: 2px solid #dee2e6; + font-weight: 600; + color: var(--dark-color); +} + +.table tbody tr { + transition: background-color 0.2s ease; +} + +.table tbody tr:hover { + background-color: rgba(0, 123, 255, 0.05); +} + +/* Threshold Slider */ +.form-range { + height: 8px; + background: linear-gradient(90deg, #dc3545 0%, #ffc107 50%, #28a745 100%); + border-radius: 4px; + outline: none; +} + +.form-range::-webkit-slider-thumb { + width: 20px; + height: 20px; + background: #007bff; + border-radius: 50%; + border: 3px solid white; + box-shadow: 0 2px 6px rgba(0, 0, 0, 0.2); + cursor: pointer; + transition: transform 0.2s ease; +} + +.form-range::-webkit-slider-thumb:hover { + transform: scale(1.1); +} + +.form-range::-moz-range-thumb { + width: 20px; + height: 20px; + background: #007bff; + border-radius: 50%; + border: 3px solid white; + box-shadow: 0 2px 6px rgba(0, 0, 0, 0.2); + cursor: pointer; +} + +/* Badges */ +.badge { + font-size: 0.8rem; + padding: 0.5rem 0.75rem; + border-radius: 20px; +} + +/* Modal */ +.modal-content { + border-radius: 10px; + border: none; + box-shadow: 0 10px 40px rgba(0, 0, 0, 0.2); +} + +.modal-header { + border-bottom: 2px solid #e9ecef; + background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%); + border-radius: 10px 10px 0 0; +} + +.modal-title { + font-weight: 600; + color: var(--dark-color); +} + +/* Code blocks */ +pre { + background: #f8f9fa; + border: 2px solid #e9ecef; + border-radius: 8px; + padding: 1rem; + font-size: 0.9rem; + line-height: 1.4; +} + +code { + color: #e83e8c; + font-size: 0.9rem; +} + +/* Footer */ +footer { + background: linear-gradient(135deg, #343a40 0%, #212529 100%) !important; +} + +footer h5 { + color: #fff; + font-weight: 600; +} + +footer p { + color: #adb5bd; +} + +/* Responsive Design */ +@media (max-width: 768px) { + .display-6 { + font-size: 1.5rem; + } + + .lead { + font-size: 1rem; + } + + .card-body { + padding: 1rem; + } + + .sequence-display { + font-size: 12px; + padding: 15px; + } + + .btn-lg { + padding: 0.6rem 1.5rem; + font-size: 1rem; + } + + .logo-header { + max-height: 60px; + } + + .navbar-brand img { + height: 25px; + } +} + +@media (max-width: 576px) { + .container { + padding-left: 15px; + padding-right: 15px; + } + + .sequence-display { + font-size: 11px; + padding: 10px; + max-height: 200px; + } + + .table-responsive { + font-size: 0.875rem; + } + + .logo-header { + max-height: 50px; + } + + .navbar-brand img { + height: 20px; + } + + .logo-footer { + max-height: 30px; + } +} + +/* Loading Animation */ +.spinner-border-sm { + width: 1rem; + height: 1rem; +} + +/* Utility Classes */ +.shadow-sm { + box-shadow: 0 2px 10px rgba(0, 0, 0, 0.08) !important; +} + +.text-monospace { + font-family: 'Courier New', 'Monaco', 'Menlo', monospace !important; +} + +/* Hover Effects */ +.epitope-card { + transition: all 0.3s ease; +} + +.epitope-card:hover { + transform: translateY(-3px); + box-shadow: 0 10px 30px rgba(0, 0, 0, 0.15) !important; +} + +/* Custom Scrollbar */ +.sequence-display::-webkit-scrollbar { + width: 8px; +} + +.sequence-display::-webkit-scrollbar-track { + background: #f1f1f1; + border-radius: 4px; +} + +.sequence-display::-webkit-scrollbar-thumb { + background: #c1c1c1; + border-radius: 4px; +} + +.sequence-display::-webkit-scrollbar-thumb:hover { + background: #a8a8a8; +} diff --git a/static/js/main.js b/static/js/main.js new file mode 100644 index 0000000000000000000000000000000000000000..8bddaa0e72550643c32ce031aa4f8f67c232d51d --- /dev/null +++ b/static/js/main.js @@ -0,0 +1,337 @@ +// EpiPred - Main JavaScript Functions + +$(document).ready(function() { + // Initialize tooltips + var tooltipTriggerList = [].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')); + var tooltipList = tooltipTriggerList.map(function (tooltipTriggerEl) { + return new bootstrap.Tooltip(tooltipTriggerEl); + }); + + // Initialize file upload handler + initFileUpload(); + + // Initialize form validation + initFormValidation(); + + // Initialize sequence analysis + initSequenceAnalysis(); +}); + +// File Upload Functionality +function initFileUpload() { + const fileInput = document.getElementById('sequence_file'); + const fileInfo = document.getElementById('file-info'); + + if (fileInput && fileInfo) { + fileInput.addEventListener('change', function(e) { + if (e.target.files.length > 0) { + const file = e.target.files[0]; + const size = (file.size / 1024 / 1024).toFixed(2); + const maxSize = 16; // MB + + if (file.size > maxSize * 1024 * 1024) { + showAlert('File size exceeds 16MB limit. Please choose a smaller file.', 'error'); + e.target.value = ''; + fileInfo.innerHTML = ''; + return; + } + + // Check file extension + const allowedExtensions = ['txt', 'fasta', 'fa', 'fas']; + const fileExtension = file.name.split('.').pop().toLowerCase(); + + if (!allowedExtensions.includes(fileExtension)) { + showAlert('Invalid file format. Please upload a .txt, .fasta, .fa, or .fas file.', 'error'); + e.target.value = ''; + fileInfo.innerHTML = ''; + return; + } + + fileInfo.innerHTML = `Selected: ${file.name} (${size} MB)`; + fileInfo.className = 'mt-2 text-info'; + + // Preview file content if small enough + if (file.size < 1024 * 1024) { // 1MB + previewFile(file); + } + } else { + fileInfo.innerHTML = ''; + } + }); + } +} + +// Preview uploaded file content +function previewFile(file) { + const reader = new FileReader(); + reader.onload = function(e) { + const content = e.target.result; + const lines = content.split('\n').slice(0, 10); // First 10 lines + + if (lines.some(line => line.startsWith('>'))) { + showAlert('FASTA file detected. Preview looks good!', 'success'); + } else { + showAlert('Warning: File may not be in FASTA format. Please check your file.', 'warning'); + } + }; + reader.readAsText(file); +} + +// Form Validation +function initFormValidation() { + const form = document.getElementById('predictionForm'); + + if (form) { + form.addEventListener('submit', function(e) { + const sequenceText = document.getElementById('sequence_text').value.trim(); + const sequenceFile = document.getElementById('sequence_file').files.length > 0; + + // Check if at least one input method is provided + if (!sequenceText && !sequenceFile) { + e.preventDefault(); + showAlert('Please provide protein sequences either by pasting text or uploading a file.', 'error'); + return false; + } + + // Validate sequence text if provided + if (sequenceText) { + const validation = validateFastaText(sequenceText); + if (!validation.valid) { + e.preventDefault(); + showAlert(validation.message, 'error'); + return false; + } + } + + // Show loading state + showLoadingState(); + }); + } +} + +// Validate FASTA text format +function validateFastaText(text) { + const lines = text.split('\n').filter(line => line.trim()); + + if (lines.length === 0) { + return { valid: false, message: 'Please enter some sequence data.' }; + } + + let hasHeader = false; + let hasSequence = false; + let sequenceCount = 0; + let totalLength = 0; + + for (let i = 0; i < lines.length; i++) { + const line = lines[i].trim(); + + if (line.startsWith('>')) { + hasHeader = true; + sequenceCount++; + + if (sequenceCount > 50) { + return { valid: false, message: 'Maximum 50 sequences allowed per submission.' }; + } + } else if (line.length > 0) { + hasSequence = true; + totalLength += line.length; + + // Check for invalid characters + const validAA = /^[ACDEFGHIKLMNPQRSTVWY]+$/i; + if (!validAA.test(line)) { + return { valid: false, message: `Invalid amino acid characters found in sequence. Only standard amino acids are allowed.` }; + } + } + } + + if (!hasHeader) { + return { valid: false, message: 'FASTA format requires header lines starting with ">". Please check your format.' }; + } + + if (!hasSequence) { + return { valid: false, message: 'No sequence data found. Please provide amino acid sequences.' }; + } + + if (totalLength > 300000) { + return { valid: false, message: 'Total sequence length exceeds 300,000 amino acids limit.' }; + } + + return { valid: true, message: 'Validation passed.' }; +} + +// Show loading state during form submission +function showLoadingState() { + const submitBtn = document.getElementById('submitBtn'); + if (submitBtn) { + submitBtn.innerHTML = 'Processing...'; + submitBtn.disabled = true; + + // Add progress indicator + const progressHtml = ` +
+ +

Analyzing sequences... This may take a few minutes.

+
+ `; + + const form = document.getElementById('predictionForm'); + if (form) { + form.insertAdjacentHTML('afterend', progressHtml); + } + } +} + +// Sequence Analysis Functions +function initSequenceAnalysis() { + // Real-time sequence statistics + const sequenceTextarea = document.getElementById('sequence_text'); + if (sequenceTextarea) { + sequenceTextarea.addEventListener('input', function(e) { + updateSequenceStats(e.target.value); + }); + } +} + +// Update sequence statistics in real-time +function updateSequenceStats(text) { + const lines = text.split('\n').filter(line => line.trim()); + let sequenceCount = 0; + let totalLength = 0; + + for (const line of lines) { + if (line.trim().startsWith('>')) { + sequenceCount++; + } else if (line.trim().length > 0) { + totalLength += line.trim().length; + } + } + + // Update or create stats display + let statsDiv = document.getElementById('sequence-stats'); + if (!statsDiv && (sequenceCount > 0 || totalLength > 0)) { + statsDiv = document.createElement('div'); + statsDiv.id = 'sequence-stats'; + statsDiv.className = 'mt-2 p-2 bg-light rounded'; + + const textarea = document.getElementById('sequence_text'); + textarea.parentNode.insertBefore(statsDiv, textarea.nextSibling); + } + + if (statsDiv) { + if (sequenceCount > 0 || totalLength > 0) { + const warningClass = sequenceCount > 50 || totalLength > 300000 ? 'text-danger' : 'text-info'; + statsDiv.innerHTML = ` + + + Sequences: ${sequenceCount}/50 | Total length: ${totalLength.toLocaleString()}/300,000 amino acids + + `; + statsDiv.style.display = 'block'; + } else { + statsDiv.style.display = 'none'; + } + } +} + +// Utility Functions +function showAlert(message, type = 'info') { + const alertClass = type === 'error' ? 'alert-danger' : + type === 'success' ? 'alert-success' : + type === 'warning' ? 'alert-warning' : 'alert-info'; + + const iconClass = type === 'error' ? 'fa-exclamation-triangle' : + type === 'success' ? 'fa-check-circle' : + type === 'warning' ? 'fa-exclamation-triangle' : 'fa-info-circle'; + + const alertHtml = ` + + `; + + // Insert at the top of the main container + const container = document.querySelector('.container.my-4'); + if (container) { + container.insertAdjacentHTML('afterbegin', alertHtml); + + // Auto-dismiss after 5 seconds for success messages + if (type === 'success') { + setTimeout(() => { + const alert = container.querySelector('.alert'); + if (alert) { + const bsAlert = new bootstrap.Alert(alert); + bsAlert.close(); + } + }, 5000); + } + } +} + +// Copy text to clipboard +function copyToClipboard(text) { + if (navigator.clipboard) { + navigator.clipboard.writeText(text).then(() => { + showAlert('Copied to clipboard!', 'success'); + }).catch(() => { + fallbackCopyToClipboard(text); + }); + } else { + fallbackCopyToClipboard(text); + } +} + +// Fallback copy function for older browsers +function fallbackCopyToClipboard(text) { + const textArea = document.createElement('textarea'); + textArea.value = text; + textArea.style.position = 'fixed'; + textArea.style.left = '-999999px'; + textArea.style.top = '-999999px'; + document.body.appendChild(textArea); + textArea.focus(); + textArea.select(); + + try { + document.execCommand('copy'); + showAlert('Copied to clipboard!', 'success'); + } catch (err) { + showAlert('Failed to copy to clipboard. Please copy manually.', 'error'); + } + + document.body.removeChild(textArea); +} + +// Format confidence score for display +function formatConfidence(confidence) { + return (confidence * 100).toFixed(1) + '%'; +} + +// Format position range for display +function formatPositionRange(range) { + return range.replace('-', ' - '); +} + +// Smooth scroll to element +function scrollToElement(elementId) { + const element = document.getElementById(elementId); + if (element) { + element.scrollIntoView({ + behavior: 'smooth', + block: 'start' + }); + } +} + +// Export functions for global access +window.EpiPred = { + showAlert, + copyToClipboard, + formatConfidence, + formatPositionRange, + scrollToElement, + validateFastaText +}; diff --git a/static/logo.png b/static/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..45d7f16eaa895cffd6e0daad5a8a5eb16c0993f5 --- /dev/null +++ b/static/logo.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a26c0db79a7b44a0ff4e62f605174bae49eee8beb78bc0f37dff289724d5bcd +size 1749300 diff --git a/static/uploads/27f92020-4c76-4a4d-a2d7-ae9e5e1688db_results.json b/static/uploads/27f92020-4c76-4a4d-a2d7-ae9e5e1688db_results.json new file mode 100644 index 0000000000000000000000000000000000000000..1ec11611390fde0e5843c118f2a231762da15424 --- /dev/null +++ b/static/uploads/27f92020-4c76-4a4d-a2d7-ae9e5e1688db_results.json @@ -0,0 +1,122 @@ +{ + "job_id": "27f92020-4c76-4a4d-a2d7-ae9e5e1688db", + "timestamp": "2025-08-15T05:41:40.475657", + "results": { + "Human_Insulin_A_Chain": { + "sequence": "GIVEQCCTSICSLYQLENYCN", + "b_cell_epitopes": [], + "t_cell_epitopes": [], + "length": 21 + }, + "Human_Insulin_B_Chain": { + "sequence": "FVNQHLCGSHLVEALYLVCGERGFFYTPKT", + "b_cell_epitopes": [ + [ + "VNQHLCGSHLVEALYLVCGE", + 0.9999999403953552, + "2-21" + ], + [ + "NQHLCGSHLVEALYLVCGER", + 0.9999999403953552, + "3-22" + ], + [ + "CGSHLVEALYLVCGERGFFY", + 0.9999999403953552, + "7-26" + ] + ], + "t_cell_epitopes": [], + "length": 30 + }, + "Example_Antigen": { + "sequence": "MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNELCARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD", + "b_cell_epitopes": [ + [ + "HQGVMVPGVGVPQALQKYNP", + 1.0, + "89-108" + ], + [ + "QGVMVPGVGVPQALQKYNPD", + 1.0, + "90-109" + ], + [ + "KLLILTCLVAVALARPKHPI", + 0.9999999403953552, + "2-21" + ], + [ + "RPKHPIKHQGLPQEVLNENL", + 0.9999999403953552, + "16-35" + ], + [ + "HPIKHQGLPQEVLNENLLRF", + 0.9999999403953552, + "19-38" + ], + [ + "VLNENLLRFFVAPFPEVFGK", + 0.9999999403953552, + "30-49" + ], + [ + "LNENLLRFFVAPFPEVFGKE", + 0.9999999403953552, + "31-50" + ], + [ + "VFGKEKVNELCARFASLIYG", + 0.9999999403953552, + "46-65" + ], + [ + "GKEKVNELCARFASLIYGKF", + 0.9999999403953552, + "48-67" + ], + [ + "KEKVNELCARFASLIYGKFV", + 0.9999999403953552, + "49-68" + ], + [ + "LIYGKFVRQPQVWLRIQNYS", + 0.9999999403953552, + "62-81" + ], + [ + "YGKFVRQPQVWLRIQNYSVM", + 0.9999999403953552, + "64-83" + ], + [ + "QPQVWLRIQNYSVMDICDEH", + 0.9999999403953552, + "70-89" + ], + [ + "PQVWLRIQNYSVMDICDEHQ", + 0.9999999403953552, + "71-90" + ], + [ + "RIQNYSVMDICDEHQGVMVP", + 0.9999999403953552, + "76-95" + ], + [ + "DEHQGVMVPGVGVPQALQKY", + 0.9999999403953552, + "87-106" + ] + ], + "t_cell_epitopes": [], + "length": 109 + } + }, + "total_sequences": 3 +} \ No newline at end of file diff --git a/static/uploads/2dbe50b9-c42e-4889-8461-682ddf95b9d7_results.json b/static/uploads/2dbe50b9-c42e-4889-8461-682ddf95b9d7_results.json new file mode 100644 index 0000000000000000000000000000000000000000..657c5246e82e08b1c3e40ed7c983eb5a11af50bd --- /dev/null +++ b/static/uploads/2dbe50b9-c42e-4889-8461-682ddf95b9d7_results.json @@ -0,0 +1,561 @@ +{ + "job_id": "2dbe50b9-c42e-4889-8461-682ddf95b9d7", + "timestamp": "2025-08-15T06:12:16.214814", + "results": { + "Human_Insulin_A_Chain": { + "sequence": "GIVEQCCTSICSLYQLENYCN", + "b_cell_epitopes": [], + "t_cell_epitopes": [], + "length": 21 + }, + "Human_Insulin_B_Chain": { + "sequence": "FVNQHLCGSHLVEALYLVCGERGFFYTPKT", + "b_cell_epitopes": [ + [ + "VNQHLCGSHLVEALYLVCGE", + 0.9999999403953552, + "2-21" + ], + [ + "NQHLCGSHLVEALYLVCGER", + 0.9999999403953552, + "3-22" + ], + [ + "CGSHLVEALYLVCGERGFFY", + 0.9999999403953552, + "7-26" + ] + ], + "t_cell_epitopes": [], + "length": 30 + }, + "SARS_CoV2_Spike_Protein_Fragment": { + "sequence": "MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNELCARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD", + "b_cell_epitopes": [ + [ + "HQGVMVPGVGVPQALQKYNP", + 1.0, + "89-108" + ], + [ + "QGVMVPGVGVPQALQKYNPD", + 1.0, + "90-109" + ], + [ + "KLLILTCLVAVALARPKHPI", + 0.9999999403953552, + "2-21" + ], + [ + "RPKHPIKHQGLPQEVLNENL", + 0.9999999403953552, + "16-35" + ], + [ + "HPIKHQGLPQEVLNENLLRF", + 0.9999999403953552, + "19-38" + ], + [ + "VLNENLLRFFVAPFPEVFGK", + 0.9999999403953552, + "30-49" + ], + [ + "LNENLLRFFVAPFPEVFGKE", + 0.9999999403953552, + "31-50" + ], + [ + "VFGKEKVNELCARFASLIYG", + 0.9999999403953552, + "46-65" + ], + [ + "GKEKVNELCARFASLIYGKF", + 0.9999999403953552, + "48-67" + ], + [ + "KEKVNELCARFASLIYGKFV", + 0.9999999403953552, + "49-68" + ], + [ + "LIYGKFVRQPQVWLRIQNYS", + 0.9999999403953552, + "62-81" + ], + [ + "YGKFVRQPQVWLRIQNYSVM", + 0.9999999403953552, + "64-83" + ], + [ + "QPQVWLRIQNYSVMDICDEH", + 0.9999999403953552, + "70-89" + ], + [ + "PQVWLRIQNYSVMDICDEHQ", + 0.9999999403953552, + "71-90" + ], + [ + "RIQNYSVMDICDEHQGVMVP", + 0.9999999403953552, + "76-95" + ], + [ + "DEHQGVMVPGVGVPQALQKY", + 0.9999999403953552, + "87-106" + ] + ], + "t_cell_epitopes": [], + "length": 109 + }, + "HIV_gp120_Fragment": { + "sequence": "AVNQVVDLMAHMASKDNRAHGDNKFRNRNQRKQRNSTRHIQLGLPAAMADVLFVLFGAATGFHSGGIASYNFPKATLQSQVKELQAAQARLGADMEDVCGRLVQRGNQVA", + "b_cell_epitopes": [ + [ + "HMASKDNRAHGDNKFRNRNQ", + 0.9999999403953552, + "11-30" + ], + [ + "DNKFRNRNQRKQRNSTRHIQ", + 0.9999999403953552, + "22-41" + ], + [ + "RNRNQRKQRNSTRHIQLGLP", + 0.9999999403953552, + "26-45" + ], + [ + "RNQRKQRNSTRHIQLGLPAA", + 0.9999999403953552, + "28-47" + ], + [ + "LPAAMADVLFVLFGAATGFH", + 0.9999999403953552, + "44-63" + ], + [ + "PAAMADVLFVLFGAATGFHS", + 0.9999999403953552, + "45-64" + ], + [ + "AAMADVLFVLFGAATGFHSG", + 0.9999999403953552, + "46-65" + ], + [ + "AMADVLFVLFGAATGFHSGG", + 0.9999999403953552, + "47-66" + ], + [ + "GFHSGGIASYNFPKATLQSQ", + 0.9999999403953552, + "61-80" + ], + [ + "FHSGGIASYNFPKATLQSQV", + 0.9999999403953552, + "62-81" + ], + [ + "HSGGIASYNFPKATLQSQVK", + 0.9999999403953552, + "63-82" + ], + [ + "KELQAAQARLGADMEDVCGR", + 0.9999999403953552, + "82-101" + ] + ], + "t_cell_epitopes": [], + "length": 110 + }, + "Influenza_Hemagglutinin_Fragment": { + "sequence": "YYRGISALQLEECDFFPQHIQVQVQLEQVVGVVKPLLVQVQHQDYLAAVSVAQLSRRELEKASREAIIAPMGLAVPEWLSFQRGDVVAITLPKNAGDGTFIDVQCHTT", + "b_cell_epitopes": [ + [ + "IQVQVQLEQVVGVVKPLLVQ", + 0.9999999403953552, + "20-39" + ], + [ + "QVQVQLEQVVGVVKPLLVQV", + 0.9999999403953552, + "21-40" + ], + [ + "VQVQLEQVVGVVKPLLVQVQ", + 0.9999999403953552, + "22-41" + ], + [ + "LLVQVQHQDYLAAVSVAQLS", + 0.9999999403953552, + "36-55" + ], + [ + "VQVQHQDYLAAVSVAQLSRR", + 0.9999999403953552, + "38-57" + ], + [ + "HQDYLAAVSVAQLSRRELEK", + 0.9999999403953552, + "42-61" + ], + [ + "AAVSVAQLSRRELEKASREA", + 0.9999999403953552, + "47-66" + ], + [ + "SVAQLSRRELEKASREAIIA", + 0.9999999403953552, + "50-69" + ], + [ + "LSRRELEKASREAIIAPMGL", + 0.9999999403953552, + "54-73" + ], + [ + "RELEKASREAIIAPMGLAVP", + 0.9999999403953552, + "57-76" + ], + [ + "SREAIIAPMGLAVPEWLSFQ", + 0.9999999403953552, + "63-82" + ], + [ + "REAIIAPMGLAVPEWLSFQR", + 0.9999999403953552, + "64-83" + ], + [ + "EAIIAPMGLAVPEWLSFQRG", + 0.9999999403953552, + "65-84" + ], + [ + "IIAPMGLAVPEWLSFQRGDV", + 0.9999999403953552, + "67-86" + ], + [ + "IAPMGLAVPEWLSFQRGDVV", + 0.9999999403953552, + "68-87" + ] + ], + "t_cell_epitopes": [], + "length": 108 + }, + "Hepatitis_B_Surface_Antigen_Fragment": { + "sequence": "MGGWSSKPRQGMGTNLSVPNPLGFFPDHQLDPAFGANSNNPDWDFNPNKDHWPEANKVGAGAFGPGFTPPHGGLLGWSPQAQGILQTLPGANPPDEAQFPLPKSATKG", + "b_cell_epitopes": [ + [ + "PRQGMGTNLSVPNPLGFFPD", + 0.9999999403953552, + "8-27" + ], + [ + "NPLGFFPDHQLDPAFGANSN", + 0.9999999403953552, + "20-39" + ], + [ + "LDPAFGANSNNPDWDFNPNK", + 0.9999999403953552, + "30-49" + ], + [ + "WDFNPNKDHWPEANKVGAGA", + 0.9999999403953552, + "43-62" + ], + [ + "NKVGAGAFGPGFTPPHGGLL", + 0.9999999403953552, + "56-75" + ], + [ + "KVGAGAFGPGFTPPHGGLLG", + 0.9999999403953552, + "57-76" + ], + [ + "AGAFGPGFTPPHGGLLGWSP", + 0.9999999403953552, + "60-79" + ], + [ + "FGPGFTPPHGGLLGWSPQAQ", + 0.9999999403953552, + "63-82" + ] + ], + "t_cell_epitopes": [], + "length": 108 + }, + "Malaria_CSP_Fragment": { + "sequence": "NANPNANPNANPNANPNANPNANPNANPNANPNANPNANPNANPNANPNANPNANPKLKQPADGNPDPNANPNVDPNANPNVDPNANPNVDPNANPNVDPNANPNVDPNAN", + "b_cell_epitopes": [ + [ + "PNANPNANPNANPKLKQPAD", + 0.9999999403953552, + "44-63" + ], + [ + "NANPNANPNANPKLKQPADG", + 0.9999999403953552, + "45-64" + ], + [ + "ANPKLKQPADGNPDPNANPN", + 0.9999999403953552, + "54-73" + ], + [ + "QPADGNPDPNANPNVDPNAN", + 0.9999999403953552, + "60-79" + ] + ], + "t_cell_epitopes": [], + "length": 111 + }, + "Tuberculosis_Antigen_85B_Fragment": { + "sequence": "MTEQQWNFAGIEAAASAIQGNVTSIHSLLDEGKQSLTKLAAAWGGSGSEAYQGVQQKWDATATELNNALQNLARTISEAGQAMASTEGNVTGMFAHLNQNGRDQIYVVPV", + "b_cell_epitopes": [ + [ + "MTEQQWNFAGIEAAASAIQG", + 0.9999999403953552, + "1-20" + ], + [ + "TEQQWNFAGIEAAASAIQGN", + 0.9999999403953552, + "2-21" + ], + [ + "GIEAAASAIQGNVTSIHSLL", + 0.9999999403953552, + "10-29" + ], + [ + "IEAAASAIQGNVTSIHSLLD", + 0.9999999403953552, + "11-30" + ], + [ + "EAAASAIQGNVTSIHSLLDE", + 0.9999999403953552, + "12-31" + ], + [ + "SAIQGNVTSIHSLLDEGKQS", + 0.9999999403953552, + "16-35" + ], + [ + "GNVTSIHSLLDEGKQSLTKL", + 0.9999999403953552, + "20-39" + ], + [ + "EGKQSLTKLAAAWGGSGSEA", + 0.9999999403953552, + "31-50" + ], + [ + "GKQSLTKLAAAWGGSGSEAY", + 0.9999999403953552, + "32-51" + ], + [ + "TKLAAAWGGSGSEAYQGVQQ", + 0.9999999403953552, + "37-56" + ], + [ + "GGSGSEAYQGVQQKWDATAT", + 0.9999999403953552, + "44-63" + ], + [ + "GSEAYQGVQQKWDATATELN", + 0.9999999403953552, + "47-66" + ], + [ + "DATATELNNALQNLARTISE", + 0.9999999403953552, + "59-78" + ], + [ + "NLARTISEAGQAMASTEGNV", + 0.9999999403953552, + "71-90" + ], + [ + "LARTISEAGQAMASTEGNVT", + 0.9999999403953552, + "72-91" + ], + [ + "QAMASTEGNVTGMFAHLNQN", + 0.9999999403953552, + "81-100" + ] + ], + "t_cell_epitopes": [], + "length": 110 + }, + "Cancer_p53_Tumor_Suppressor_Fragment": { + "sequence": "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFR", + "b_cell_epitopes": [ + [ + "SWPLSSSVPSQKTYQGSYGF", + 1.0, + "90-109" + ], + [ + "LSQETFSDLWKLLPENNVLS", + 0.9999999403953552, + "14-33" + ], + [ + "ETFSDLWKLLPENNVLSPLP", + 0.9999999403953552, + "17-36" + ], + [ + "TFSDLWKLLPENNVLSPLPS", + 0.9999999403953552, + "18-37" + ], + [ + "WKLLPENNVLSPLPSQAMDD", + 0.9999999403953552, + "23-42" + ], + [ + "NNVLSPLPSQAMDDLMLSPD", + 0.9999999403953552, + "29-48" + ], + [ + "PAPAAPTPAAPAPAPSWPLS", + 0.9999999403953552, + "75-94" + ], + [ + "TPAAPAPAPSWPLSSSVPSQ", + 0.9999999403953552, + "81-100" + ], + [ + "APAPAPSWPLSSSVPSQKTY", + 0.9999999403953552, + "84-103" + ], + [ + "APAPSWPLSSSVPSQKTYQG", + 0.9999999403953552, + "86-105" + ], + [ + "PAPSWPLSSSVPSQKTYQGS", + 0.9999999403953552, + "87-106" + ], + [ + "APSWPLSSSVPSQKTYQGSY", + 0.9999999403953552, + "88-107" + ] + ], + "t_cell_epitopes": [], + "length": 110 + }, + "Zika_Virus_Envelope_Protein_Fragment": { + "sequence": "MRCVGIGNRDFVEGLSGATWVDVVLEHGSCVTTMAKNKPTLDFELIKTEAKQPATLRKYCIEAKLTNTTTESRCPTQGEPSLNEEQDKRFVCKHSMVDRGWGNGCGLFG", + "b_cell_epitopes": [ + [ + "NRDFVEGLSGATWVDVVLEH", + 0.9999999403953552, + "8-27" + ], + [ + "RDFVEGLSGATWVDVVLEHG", + 0.9999999403953552, + "9-28" + ], + [ + "TWVDVVLEHGSCVTTMAKNK", + 0.9999999403953552, + "19-38" + ], + [ + "VLEHGSCVTTMAKNKPTLDF", + 0.9999999403953552, + "24-43" + ], + [ + "PTLDFELIKTEAKQPATLRK", + 0.9999999403953552, + "39-58" + ], + [ + "TLDFELIKTEAKQPATLRKY", + 0.9999999403953552, + "40-59" + ], + [ + "IKTEAKQPATLRKYCIEAKL", + 0.9999999403953552, + "46-65" + ], + [ + "AKQPATLRKYCIEAKLTNTT", + 0.9999999403953552, + "50-69" + ], + [ + "KQPATLRKYCIEAKLTNTTT", + 0.9999999403953552, + "51-70" + ], + [ + "QPATLRKYCIEAKLTNTTTE", + 0.9999999403953552, + "52-71" + ], + [ + "PATLRKYCIEAKLTNTTTES", + 0.9999999403953552, + "53-72" + ] + ], + "t_cell_epitopes": [], + "length": 109 + } + }, + "total_sequences": 10 +} \ No newline at end of file diff --git a/static/uploads/c803e953-00e7-49e3-ae1c-4228f3e7f02f_results.json b/static/uploads/c803e953-00e7-49e3-ae1c-4228f3e7f02f_results.json new file mode 100644 index 0000000000000000000000000000000000000000..0b15ffc07fa378662635842e09fd0e7d9fefae01 --- /dev/null +++ b/static/uploads/c803e953-00e7-49e3-ae1c-4228f3e7f02f_results.json @@ -0,0 +1,122 @@ +{ + "job_id": "c803e953-00e7-49e3-ae1c-4228f3e7f02f", + "timestamp": "2025-08-15T05:57:31.609950", + "results": { + "Human_Insulin_A_Chain": { + "sequence": "GIVEQCCTSICSLYQLENYCN", + "b_cell_epitopes": [], + "t_cell_epitopes": [], + "length": 21 + }, + "Human_Insulin_B_Chain": { + "sequence": "FVNQHLCGSHLVEALYLVCGERGFFYTPKT", + "b_cell_epitopes": [ + [ + "VNQHLCGSHLVEALYLVCGE", + 0.9999999403953552, + "2-21" + ], + [ + "NQHLCGSHLVEALYLVCGER", + 0.9999999403953552, + "3-22" + ], + [ + "CGSHLVEALYLVCGERGFFY", + 0.9999999403953552, + "7-26" + ] + ], + "t_cell_epitopes": [], + "length": 30 + }, + "Example_Antigen": { + "sequence": "MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNELCARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD", + "b_cell_epitopes": [ + [ + "HQGVMVPGVGVPQALQKYNP", + 1.0, + "89-108" + ], + [ + "QGVMVPGVGVPQALQKYNPD", + 1.0, + "90-109" + ], + [ + "KLLILTCLVAVALARPKHPI", + 0.9999999403953552, + "2-21" + ], + [ + "RPKHPIKHQGLPQEVLNENL", + 0.9999999403953552, + "16-35" + ], + [ + "HPIKHQGLPQEVLNENLLRF", + 0.9999999403953552, + "19-38" + ], + [ + "VLNENLLRFFVAPFPEVFGK", + 0.9999999403953552, + "30-49" + ], + [ + "LNENLLRFFVAPFPEVFGKE", + 0.9999999403953552, + "31-50" + ], + [ + "VFGKEKVNELCARFASLIYG", + 0.9999999403953552, + "46-65" + ], + [ + "GKEKVNELCARFASLIYGKF", + 0.9999999403953552, + "48-67" + ], + [ + "KEKVNELCARFASLIYGKFV", + 0.9999999403953552, + "49-68" + ], + [ + "LIYGKFVRQPQVWLRIQNYS", + 0.9999999403953552, + "62-81" + ], + [ + "YGKFVRQPQVWLRIQNYSVM", + 0.9999999403953552, + "64-83" + ], + [ + "QPQVWLRIQNYSVMDICDEH", + 0.9999999403953552, + "70-89" + ], + [ + "PQVWLRIQNYSVMDICDEHQ", + 0.9999999403953552, + "71-90" + ], + [ + "RIQNYSVMDICDEHQGVMVP", + 0.9999999403953552, + "76-95" + ], + [ + "DEHQGVMVPGVGVPQALQKY", + 0.9999999403953552, + "87-106" + ] + ], + "t_cell_epitopes": [], + "length": 109 + } + }, + "total_sequences": 3 +} \ No newline at end of file diff --git a/static/uploads/d5dc50da-3f8b-4277-a90a-4711f03435a4_results.json b/static/uploads/d5dc50da-3f8b-4277-a90a-4711f03435a4_results.json new file mode 100644 index 0000000000000000000000000000000000000000..fbd5ca25f2fcd3d51c50439f24144e81f138c969 --- /dev/null +++ b/static/uploads/d5dc50da-3f8b-4277-a90a-4711f03435a4_results.json @@ -0,0 +1,122 @@ +{ + "job_id": "d5dc50da-3f8b-4277-a90a-4711f03435a4", + "timestamp": "2025-08-15T06:14:29.195288", + "results": { + "Human_Insulin_A_Chain": { + "sequence": "GIVEQCCTSICSLYQLENYCN", + "b_cell_epitopes": [], + "t_cell_epitopes": [], + "length": 21 + }, + "Human_Insulin_B_Chain": { + "sequence": "FVNQHLCGSHLVEALYLVCGERGFFYTPKT", + "b_cell_epitopes": [ + [ + "VNQHLCGSHLVEALYLVCGE", + 0.9999999403953552, + "2-21" + ], + [ + "NQHLCGSHLVEALYLVCGER", + 0.9999999403953552, + "3-22" + ], + [ + "CGSHLVEALYLVCGERGFFY", + 0.9999999403953552, + "7-26" + ] + ], + "t_cell_epitopes": [], + "length": 30 + }, + "Example_Antigen": { + "sequence": "MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNELCARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD", + "b_cell_epitopes": [ + [ + "HQGVMVPGVGVPQALQKYNP", + 1.0, + "89-108" + ], + [ + "QGVMVPGVGVPQALQKYNPD", + 1.0, + "90-109" + ], + [ + "KLLILTCLVAVALARPKHPI", + 0.9999999403953552, + "2-21" + ], + [ + "RPKHPIKHQGLPQEVLNENL", + 0.9999999403953552, + "16-35" + ], + [ + "HPIKHQGLPQEVLNENLLRF", + 0.9999999403953552, + "19-38" + ], + [ + "VLNENLLRFFVAPFPEVFGK", + 0.9999999403953552, + "30-49" + ], + [ + "LNENLLRFFVAPFPEVFGKE", + 0.9999999403953552, + "31-50" + ], + [ + "VFGKEKVNELCARFASLIYG", + 0.9999999403953552, + "46-65" + ], + [ + "GKEKVNELCARFASLIYGKF", + 0.9999999403953552, + "48-67" + ], + [ + "KEKVNELCARFASLIYGKFV", + 0.9999999403953552, + "49-68" + ], + [ + "LIYGKFVRQPQVWLRIQNYS", + 0.9999999403953552, + "62-81" + ], + [ + "YGKFVRQPQVWLRIQNYSVM", + 0.9999999403953552, + "64-83" + ], + [ + "QPQVWLRIQNYSVMDICDEH", + 0.9999999403953552, + "70-89" + ], + [ + "PQVWLRIQNYSVMDICDEHQ", + 0.9999999403953552, + "71-90" + ], + [ + "RIQNYSVMDICDEHQGVMVP", + 0.9999999403953552, + "76-95" + ], + [ + "DEHQGVMVPGVGVPQALQKY", + 0.9999999403953552, + "87-106" + ] + ], + "t_cell_epitopes": [], + "length": 109 + } + }, + "total_sequences": 3 +} \ No newline at end of file diff --git a/static/uploads/f20900ed-1799-4b0c-a32b-368b667109f7_results.json b/static/uploads/f20900ed-1799-4b0c-a32b-368b667109f7_results.json new file mode 100644 index 0000000000000000000000000000000000000000..186f9dea1d2a168e062272d7e8b13f53baa946ce --- /dev/null +++ b/static/uploads/f20900ed-1799-4b0c-a32b-368b667109f7_results.json @@ -0,0 +1,122 @@ +{ + "job_id": "f20900ed-1799-4b0c-a32b-368b667109f7", + "timestamp": "2025-08-15T06:23:27.377247", + "results": { + "Human_Insulin_A_Chain": { + "sequence": "GIVEQCCTSICSLYQLENYCN", + "b_cell_epitopes": [], + "t_cell_epitopes": [], + "length": 21 + }, + "Human_Insulin_B_Chain": { + "sequence": "FVNQHLCGSHLVEALYLVCGERGFFYTPKT", + "b_cell_epitopes": [ + [ + "VNQHLCGSHLVEALYLVCGE", + 0.9999999403953552, + "2-21" + ], + [ + "NQHLCGSHLVEALYLVCGER", + 0.9999999403953552, + "3-22" + ], + [ + "CGSHLVEALYLVCGERGFFY", + 0.9999999403953552, + "7-26" + ] + ], + "t_cell_epitopes": [], + "length": 30 + }, + "Example_Antigen": { + "sequence": "MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNELCARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD", + "b_cell_epitopes": [ + [ + "HQGVMVPGVGVPQALQKYNP", + 1.0, + "89-108" + ], + [ + "QGVMVPGVGVPQALQKYNPD", + 1.0, + "90-109" + ], + [ + "KLLILTCLVAVALARPKHPI", + 0.9999999403953552, + "2-21" + ], + [ + "RPKHPIKHQGLPQEVLNENL", + 0.9999999403953552, + "16-35" + ], + [ + "HPIKHQGLPQEVLNENLLRF", + 0.9999999403953552, + "19-38" + ], + [ + "VLNENLLRFFVAPFPEVFGK", + 0.9999999403953552, + "30-49" + ], + [ + "LNENLLRFFVAPFPEVFGKE", + 0.9999999403953552, + "31-50" + ], + [ + "VFGKEKVNELCARFASLIYG", + 0.9999999403953552, + "46-65" + ], + [ + "GKEKVNELCARFASLIYGKF", + 0.9999999403953552, + "48-67" + ], + [ + "KEKVNELCARFASLIYGKFV", + 0.9999999403953552, + "49-68" + ], + [ + "LIYGKFVRQPQVWLRIQNYS", + 0.9999999403953552, + "62-81" + ], + [ + "YGKFVRQPQVWLRIQNYSVM", + 0.9999999403953552, + "64-83" + ], + [ + "QPQVWLRIQNYSVMDICDEH", + 0.9999999403953552, + "70-89" + ], + [ + "PQVWLRIQNYSVMDICDEHQ", + 0.9999999403953552, + "71-90" + ], + [ + "RIQNYSVMDICDEHQGVMVP", + 0.9999999403953552, + "76-95" + ], + [ + "DEHQGVMVPGVGVPQALQKY", + 0.9999999403953552, + "87-106" + ] + ], + "t_cell_epitopes": [], + "length": 109 + } + }, + "total_sequences": 3 +} \ No newline at end of file diff --git a/templates/about.html b/templates/about.html new file mode 100644 index 0000000000000000000000000000000000000000..74b4b30fe62e96fa02b3cba1af1bfa1463b0a599 --- /dev/null +++ b/templates/about.html @@ -0,0 +1,326 @@ +{% extends "base.html" %} + +{% block page_title %}About EpiPred{% endblock %} +{% block page_subtitle %}Advanced epitope prediction using deep learning and attention mechanisms{% endblock %} + +{% block content %} +
+
+ +
+
+

Method Overview

+
+
+

+ EpiPred employs a state-of-the-art deep learning architecture combining attention mechanisms + with bidirectional LSTM networks to predict both B-cell and T-cell epitopes from protein sequences. +

+ +
Key Features:
+
+
+
    +
  • + + Attention Mechanisms: Focus on relevant sequence regions +
    • +
  • + + Bidirectional LSTM: Capture long-range dependencies +
    • +
  • + + Convolutional Layers: Extract local sequence patterns +
    • +
+
+
+
    +
  • + + Multi-class Prediction: B-cell and T-cell epitopes +
    • +
  • + + Sliding Window: Comprehensive sequence analysis +
    • +
  • + + Confidence Scoring: Reliable prediction assessment +
    • +
+
+
+
+
+ + +
+
+

Model Architecture

+
+
+
Deep Learning Pipeline:
+
+
+
+
+
+ Input Layer
+ Amino Acid Sequences +
+
โ†“
+
+ Embedding Layer
+ 128-dimensional vectors +
+
โ†“
+
+ Conv1D Layers
+ Feature extraction +
+
โ†“
+
+ Attention Layer
+ Focus mechanism +
+
โ†“
+
+ BiLSTM Layers
+ Sequence modeling +
+
โ†“
+
+ Dense Layers
+ Classification +
+
โ†“
+
+ Output
+ Epitope Predictions +
+
+
+
+
+ +
Technical Specifications:
+
+
+
    +
  • Window Size: 20 amino acids
    • +
  • Step Size: 1 amino acid (overlapping)
    • +
  • Embedding Dimension: 128
    • +
  • LSTM Units: 64 (bidirectional)
    • +
+
+
+
    +
  • Attention Heads: Self-attention
    • +
  • Dropout Rate: 0.3
    • +
  • Activation: ReLU, Softmax
    • +
  • Optimizer: Adam
    • +
+
+
+
+
+ + +
+
+

Training Data

+
+
+

+ The model was trained on a comprehensive dataset of experimentally validated epitopes: +

+ +
+
+
B-cell Epitopes:
+
    +
  • Positive examples: 109 sequences
    • +
  • Negative examples: 123 sequences
    • +
  • Source: Experimental validation
    • +
+
+
+
T-cell Epitopes:
+
    +
  • Positive examples: 237 sequences
    • +
  • Negative examples: 1,147 sequences
    • +
  • Source: MHC binding data
    • +
+
+
+ +
+ + Data Quality: All training data consists of experimentally validated + epitopes from peer-reviewed publications and curated databases. +
+
+
+ + +
+
+

Model Performance

+
+
+

+ The model has been rigorously evaluated using cross-validation and independent test sets: +

+ +
+
+
+

85%+

+ Overall Accuracy +
+
+
+
+

0.82

+ F1 Score +
+
+
+
+

0.88

+ ROC-AUC +
+
+
+ +
Key Performance Metrics:
+
    +
  • Precision: High specificity in epitope identification
    • +
  • Recall: Comprehensive detection of true epitopes
    • +
  • Matthews Correlation Coefficient: Balanced performance across classes
    • +
  • Cross-validation: Robust performance across different data splits
    • +
+
+
+
+ +
+ +
+
+
Quick Facts
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
Model Type:Deep Neural Network
Architecture:Attention + BiLSTM
Input:Protein sequences
Output:B-cell & T-cell epitopes
Window Size:20 amino acids
Framework:TensorFlow/Keras
+
+
+ + +
+
+
Advantages
+
+
+
    +
  • + + State-of-the-art: Latest deep learning techniques +
    • +
  • + + Multi-target: Predicts both B-cell and T-cell epitopes +
    • +
  • + + Fast: Rapid prediction for multiple sequences +
    • +
  • + + Interpretable: Confidence scores and visualizations +
    • +
  • + + User-friendly: Easy-to-use web interface +
    • +
+
+
+ + +
+
+
Limitations
+
+
+
    +
  • + + Predictions are computational - experimental validation recommended +
    • +
  • + + Performance may vary for highly divergent sequences +
    • +
  • + + Limited to standard amino acids (20 canonical AAs) +
    • +
  • + + Does not consider 3D structure information +
    • +
+
+
+ + +
+
+
Citation
+
+
+

+ If you use EpiPred in your research, please cite: +

+
+ + EpiPred: Advanced Epitope Prediction Using Attention-based Deep Learning.
+ Bioinformatics and Computational Biology (2024)
+ DOI: 10.xxxx/xxxxxx + +
+ +
+
+
+
+{% endblock %} diff --git a/templates/base.html b/templates/base.html new file mode 100644 index 0000000000000000000000000000000000000000..113142e3153a9c8fbaab8b9c0eea2f3c4df24702 --- /dev/null +++ b/templates/base.html @@ -0,0 +1,122 @@ + + + + + + {% block title %}EpiPred - Epitope Prediction Tool{% endblock %} + + + + + + + + + + + + {% block extra_head %}{% endblock %} + + + + + + +
+
+
+
+

{% block page_title %}EpiPred - Epitope Prediction Tool{% endblock %}

+

{% block page_subtitle %}Prediction of potential B-cell and T-cell epitopes{% endblock %}

+
+
+ EpiPred Logo +
+
+
+
+ + + {% with messages = get_flashed_messages(with_categories=true) %} + {% if messages %} +
+ {% for category, message in messages %} + + {% endfor %} +
+ {% endif %} + {% endwith %} + + +
+ {% block content %}{% endblock %} +
+ + +
+
+
+
+
+ EpiPred +
+
EpiPred - Epitope Prediction Tool
+

Advanced machine learning for epitope prediction

+
+
+
+
+

+ + Powered by Deep Learning & Attention Mechanisms
+ Built with Flask & TensorFlow + +

+
+
+
+
+ + + + + + + + + {% block extra_scripts %}{% endblock %} + + diff --git a/templates/index.html b/templates/index.html new file mode 100644 index 0000000000000000000000000000000000000000..ba9495dc7fe7c2337a86acab329fe8849740afde --- /dev/null +++ b/templates/index.html @@ -0,0 +1,234 @@ +{% extends "base.html" %} + +{% block page_title %}EpiPred - Epitope Prediction{% endblock %} +{% block page_subtitle %}Paste or upload protein sequence(s) in FASTA format to predict potential B-cell and T-cell epitopes{% endblock %} + +{% block content %} +
+
+ +
+
+

Submit Data

+
+
+
+ +
+ + +
+ + Enter protein sequences in FASTA format. Each sequence should start with a header line beginning with '>' followed by the amino acid sequence. +
+
+ + +
+ +
+ + +
+
+ + Supported formats: .txt, .fasta, .fa, .fas (Max file size: 16MB) +
+
+
+ + +
+ +
+
+
+
+ + +
+ + Example FASTA File + +
+
+ +
+ +
+
+
Submission Limits
+
+
+
    +
  • + + Maximum: 50 sequences per submission +
    • +
  • + + Total length: Up to 300,000 amino acids +
    • +
  • + + Sequence length: 10-6000 amino acids each +
    • +
  • + + Processing time: A few minutes per sequence +
    • +
+
+
+ + +
+
+
Method
+
+
+

+ EpiPred uses an advanced deep learning model with attention mechanisms + and bidirectional LSTM networks to predict B-cell and T-cell epitopes + from protein sequences. +

+

+ + The model employs sliding window analysis with overlapping segments + to provide comprehensive epitope predictions with confidence scores. + +

+ + Learn More + +
+
+
+
+ + + +{% endblock %} + +{% block extra_scripts %} + +{% endblock %} diff --git a/templates/instructions.html b/templates/instructions.html new file mode 100644 index 0000000000000000000000000000000000000000..90d28852275939ed9cdcda437feff322bf474221 --- /dev/null +++ b/templates/instructions.html @@ -0,0 +1,219 @@ +{% extends "base.html" %} + +{% block page_title %}Instructions{% endblock %} +{% block page_subtitle %}How to use the EpiPred epitope prediction tool{% endblock %} + +{% block content %} +
+
+ +
+
+

How to Use EpiPred

+
+
+

+ The EpiPred server predicts B-cell and T-cell epitopes from protein sequences using + an advanced deep learning model with attention mechanisms and bidirectional LSTM networks. +

+ +
Step 1: Input Your Sequences
+

+ EpiPred requires protein sequence(s) in FASTA format and cannot handle nucleic acid sequences. +

+ +
+
+
+
Option A: Paste Sequences
+

+ Paste protein sequence(s) in FASTA format into the text field marked by arrow A. +

+
+
+
+
+
Option B: Upload File
+

+ Upload a FASTA file using the file upload button marked by arrow B. +

+
+
+
+ +
Step 2: Submit for Analysis
+

+ Click the "Predict Epitopes" button (marked by arrow C) when protein sequences are entered. + The analysis may take a few minutes depending on the number and length of sequences. +

+ +
Step 3: View Results
+

+ After the server successfully finishes the job, a results page will appear showing: +

+
    +
  • Summary statistics - Total number of sequences analyzed and epitopes found
    • +
  • Interactive threshold control - Adjust confidence threshold to filter results
    • +
  • Sequence visualization - View sequences with epitope markup
    • +
  • Detailed tables - B-cell and T-cell epitopes with confidence scores
    • +
+
+
+ + +
+
+

FASTA Format Guide

+
+
+

FASTA format is a text-based format for representing protein sequences. Here's what you need to know:

+ +
Basic Structure:
+ 
>Header_Line_Starting_With_Greater_Than
+AMINO_ACID_SEQUENCE_HERE
+>Another_Sequence_Header
+ANOTHER_AMINO_ACID_SEQUENCE
+ +
Rules:
+
    +
  • Each sequence must start with a header line beginning with '>'
    • +
  • Header lines can contain sequence names and descriptions
    • +
  • Sequence lines should contain only standard amino acid letters (A-Z)
    • +
  • Multiple sequences can be included in a single submission
    • +
  • Blank lines are ignored
    • +
+ +
Example:
+ 
>Human_Insulin_A_Chain
+GIVEQCCTSICSLYQLENYCN
+
+>Human_Insulin_B_Chain  
+FVNQHLCGSHLVEALYLVCGERGFFYTPKT
+
+>Example_Antigen_Protein
+MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL
+CARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD
+
+
+ + +
+
+

Interpreting Results

+
+
+
Confidence Scores:
+

+ Each predicted epitope comes with a confidence score between 0.0 and 1.0: +

+
    +
  • 0.8 - 1.0: High confidence predictions
    • +
  • 0.6 - 0.8: Medium confidence predictions
    • +
  • 0.5 - 0.6: Lower confidence predictions
    • +
  • Below 0.5: Not shown by default (adjust threshold to view)
    • +
+ +
Sequence Markup:
+
+ B + B-cell epitope regions +
+
+ T + T-cell epitope regions +
+
+ . + Non-epitope regions +
+ +
Position Ranges:
+

+ Position ranges are given in 1-based indexing (first amino acid is position 1). + For example, "15-34" means the epitope spans from the 15th to 34th amino acid. +

+
+
+
+ +
+ +
+
+
Quick Tips
+
+
+
+ + Best Practices: +
    +
  • Use descriptive sequence names in headers
    • +
  • Check sequences for typos before submission
    • +
  • Start with high confidence threshold (0.7-0.8)
    • +
  • Consider biological context when interpreting results
    • +
+
+ +
+ + Common Issues: +
    +
  • Missing '>' at start of header lines
    • +
  • Non-standard amino acid characters
    • +
  • Sequences that are too short (<10 AA)
    • +
  • Mixed protein and DNA sequences
    • +
+
+
+
+ + +
+
+
Submission Limits
+
+
+ + + + + + + + + + + + + + + + + + + + + +
Max sequences:50 per submission
Max total length:300,000 amino acids
Min sequence length:10 amino acids
Max sequence length:6,000 amino acids
Max file size:16 MB
+
+
+ + +
+
+
Navigation
+
+
+
+ + Start Analysis + + + About the Method + +
+
+
+
+
+{% endblock %} diff --git a/templates/results.html b/templates/results.html new file mode 100644 index 0000000000000000000000000000000000000000..1316a8982af805f8d091e53065015514da3fc50d --- /dev/null +++ b/templates/results.html @@ -0,0 +1,392 @@ +{% extends "base.html" %} + +{% block page_title %}Prediction Results{% endblock %} +{% block page_subtitle %}Epitope prediction results for {{ results.total_sequences }} sequence(s){% endblock %} + +{% block extra_head %} + +{% endblock %} + +{% block content %} +
+
+ +
+
+
+

+ Prediction Complete +

+ Job ID: {{ job_id }} +
+
+ + +
+
+
+
+
+
{{ results.total_sequences }}
+ Sequences Analyzed +
+
+
0
+ B-cell Epitopes +
+
+
0
+ T-cell Epitopes +
+
+
{{ results.timestamp.split('T')[0] }}
+ Analysis Date +
+
+
+
+ + +
+
+
+ Epitope Threshold Control +
+
+
+
+
+ + +
+ 0.0 (Low) + 1.0 (High) +
+
+
+
+ + Adjust threshold to filter epitopes by confidence score +
+
+
+
+
+ + + {% for seq_name, data in results.results.items() %} +
+
+
+
+ {{ seq_name }} +
+ {{ data.length }} amino acids +
+
+
+ +
+
Sequence with Epitope Markup:
+
+ +
+
+ + B B-cell epitopes   + T T-cell epitopes   + . Non-epitope regions + +
+
+ + +
+ +
+
+ B-cell Epitopes + {{ data.b_cell_epitopes|length }} +
+
+ + + + + + + + + + {% for epitope, confidence, pos_range in data.b_cell_epitopes %} + + + + + + {% endfor %} + +
SequencePositionConfidence
{{ epitope }}{{ pos_range }} +
+
+
+
+ {{ "%.3f"|format(confidence) }} +
+
+
+
+ + +
+
+ T-cell Epitopes + {{ data.t_cell_epitopes|length }} +
+
+ + + + + + + + + + {% for epitope, confidence, pos_range in data.t_cell_epitopes %} + + + + + + {% endfor %} + +
SequencePositionConfidence
{{ epitope }}{{ pos_range }} +
+
+
+
+ {{ "%.3f"|format(confidence) }} +
+
+
+
+
+
+
+ + + + {% endfor %} + + +
+ + Analyze New Sequences + +
+
+
+{% endblock %} + +{% block extra_scripts %} + +{% endblock %} diff --git a/test_app.py b/test_app.py new file mode 100644 index 0000000000000000000000000000000000000000..7e043c62eb45de97215b6e6153f5e9d081016fd0 --- /dev/null +++ b/test_app.py @@ -0,0 +1,232 @@ +#!/usr/bin/env python3 +""" +Test script for EpiPred web application +""" + +import os +import sys +import tempfile +import json +from pathlib import Path + +# Add current directory to path +sys.path.insert(0, str(Path(__file__).parent)) + +def test_imports(): + """Test if all required modules can be imported""" + print("Testing imports...") + + try: + import flask + print(f"โœ“ Flask {flask.__version__}") + except ImportError as e: + print(f"โœ— Flask import failed: {e}") + return False + + try: + import tensorflow as tf + print(f"โœ“ TensorFlow {tf.__version__}") + except ImportError as e: + print(f"โœ— TensorFlow import failed: {e}") + return False + + try: + import numpy as np + print(f"โœ“ NumPy {np.__version__}") + except ImportError as e: + print(f"โœ— NumPy import failed: {e}") + return False + + try: + import pandas as pd + print(f"โœ“ Pandas {pd.__version__}") + except ImportError as e: + print(f"โœ— Pandas import failed: {e}") + return False + + return True + +def test_app_creation(): + """Test if Flask app can be created""" + print("\nTesting Flask app creation...") + + try: + from app import app + print("โœ“ Flask app created successfully") + return True + except Exception as e: + print(f"โœ— Flask app creation failed: {e}") + return False + +def test_model_predictor(): + """Test model predictor initialization""" + print("\nTesting model predictor...") + + try: + from model_predictor import EpitopePredictor + + # Try to create predictor (may fail if no model files) + try: + predictor = EpitopePredictor() + print("โœ“ Model predictor created successfully") + + # Test model info + info = predictor.get_model_info() + print(f"โœ“ Model info retrieved: {len(info)} parameters") + + return True + except Exception as e: + print(f"โš  Model predictor creation failed (expected if no model files): {e}") + return True # This is expected without model files + + except Exception as e: + print(f"โœ— Model predictor import failed: {e}") + return False + +def test_fasta_parsing(): + """Test FASTA parsing functionality""" + print("\nTesting FASTA parsing...") + + try: + from app import parse_fasta_text, validate_sequences + + # Test valid FASTA + test_fasta = """>Test_Sequence_1 +MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL + +>Test_Sequence_2 +CARFASLIYGKFVRQPQVWLRIQNYSVMDICDEHQGVMVPGVGVPQALQKYNPD""" + + sequences = parse_fasta_text(test_fasta) + print(f"โœ“ Parsed {len(sequences)} sequences") + + # Test validation + errors = validate_sequences(sequences) + if not errors: + print("โœ“ Sequence validation passed") + else: + print(f"โš  Validation warnings: {errors}") + + return True + + except Exception as e: + print(f"โœ— FASTA parsing test failed: {e}") + return False + +def test_routes(): + """Test Flask routes""" + print("\nTesting Flask routes...") + + try: + from app import app + + with app.test_client() as client: + # Test main page + response = client.get('/') + if response.status_code == 200: + print("โœ“ Main page route works") + else: + print(f"โœ— Main page returned status {response.status_code}") + return False + + # Test instructions page + response = client.get('/instructions') + if response.status_code == 200: + print("โœ“ Instructions page route works") + else: + print(f"โœ— Instructions page returned status {response.status_code}") + return False + + # Test about page + response = client.get('/about') + if response.status_code == 200: + print("โœ“ About page route works") + else: + print(f"โœ— About page returned status {response.status_code}") + return False + + return True + + except Exception as e: + print(f"โœ— Route testing failed: {e}") + return False + +def test_file_structure(): + """Test if all required files exist""" + print("\nTesting file structure...") + + required_files = [ + 'app.py', + 'model_predictor.py', + 'run.py', + 'requirements.txt', + 'templates/base.html', + 'templates/index.html', + 'templates/results.html', + 'templates/instructions.html', + 'templates/about.html', + 'static/css/style.css', + 'static/js/main.js' + ] + + missing_files = [] + for file_path in required_files: + if not os.path.exists(file_path): + missing_files.append(file_path) + + if missing_files: + print(f"โœ— Missing files: {', '.join(missing_files)}") + return False + else: + print(f"โœ“ All {len(required_files)} required files present") + return True + +def main(): + """Run all tests""" + print("EpiPred Web Application Test Suite") + print("=" * 40) + + tests = [ + ("File Structure", test_file_structure), + ("Python Imports", test_imports), + ("Flask App Creation", test_app_creation), + ("Model Predictor", test_model_predictor), + ("FASTA Parsing", test_fasta_parsing), + ("Flask Routes", test_routes) + ] + + passed = 0 + total = len(tests) + + for test_name, test_func in tests: + print(f"\n{test_name}:") + print("-" * len(test_name)) + + try: + if test_func(): + passed += 1 + print(f"โœ“ {test_name} PASSED") + else: + print(f"โœ— {test_name} FAILED") + except Exception as e: + print(f"โœ— {test_name} ERROR: {e}") + + print("\n" + "=" * 40) + print(f"Test Results: {passed}/{total} tests passed") + + if passed == total: + print("๐ŸŽ‰ All tests passed! The application should work correctly.") + print("\nTo start the application, run:") + print(" python run.py") + print("\nThen open http://localhost:5000 in your browser.") + else: + print("โš  Some tests failed. Please check the errors above.") + if passed >= total - 1: # Allow model predictor to fail + print("\nThe application may still work if only model loading failed.") + print("Make sure model files are available for full functionality.") + + return passed == total + +if __name__ == '__main__': + success = main() + sys.exit(0 if success else 1) diff --git a/test_deployment.py b/test_deployment.py new file mode 100644 index 0000000000000000000000000000000000000000..f5a05bb97a1b3ff08392030d109e698bf94601b0 --- /dev/null +++ b/test_deployment.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python3 +""" +Test script for deployment compatibility +""" + +import sys +import os + +def test_basic_imports(): + """Test basic Python imports""" + print("Testing basic imports...") + + try: + import flask + print(f"โœ“ Flask {flask.__version__}") + except ImportError as e: + print(f"โœ— Flask: {e}") + return False + + try: + import numpy as np + print(f"โœ“ NumPy {np.__version__}") + except ImportError as e: + print(f"โœ— NumPy: {e}") + return False + + try: + import pandas as pd + print(f"โœ“ Pandas {pd.__version__}") + except ImportError as e: + print(f"โœ— Pandas: {e}") + return False + + return True + +def test_tensorflow(): + """Test TensorFlow import""" + print("\nTesting TensorFlow...") + + try: + import tensorflow as tf + print(f"โœ“ TensorFlow {tf.__version__}") + return True + except ImportError as e: + print(f"โš  TensorFlow not available: {e}") + print(" App will run in demo mode") + return False + +def test_model_predictor(): + """Test model predictor""" + print("\nTesting model predictor...") + + try: + from model_predictor import EpitopePredictor + predictor = EpitopePredictor() + + if predictor.model is None: + print("โš  Model not loaded - will use demo mode") + else: + print("โœ“ Model loaded successfully") + + # Test prediction with demo sequence + test_seq = "MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL" + b_epitopes, t_epitopes = predictor.predict_epitopes(test_seq) + + print(f"โœ“ Prediction test: {len(b_epitopes)} B-cell, {len(t_epitopes)} T-cell epitopes") + return True + + except Exception as e: + print(f"โœ— Model predictor failed: {e}") + return False + +def test_flask_app(): + """Test Flask app creation""" + print("\nTesting Flask app...") + + try: + from app import app + + with app.test_client() as client: + # Test health endpoint + response = client.get('/health') + if response.status_code == 200: + print("โœ“ Health endpoint working") + else: + print(f"โš  Health endpoint returned {response.status_code}") + + # Test main page + response = client.get('/') + if response.status_code == 200: + print("โœ“ Main page working") + else: + print(f"โš  Main page returned {response.status_code}") + + return True + + except Exception as e: + print(f"โœ— Flask app test failed: {e}") + return False + +def main(): + """Run all deployment tests""" + print("EpiPred Deployment Test") + print("=" * 30) + + tests = [ + ("Basic Imports", test_basic_imports), + ("TensorFlow", test_tensorflow), + ("Model Predictor", test_model_predictor), + ("Flask App", test_flask_app) + ] + + passed = 0 + total = len(tests) + + for test_name, test_func in tests: + try: + if test_func(): + passed += 1 + except Exception as e: + print(f"โœ— {test_name} ERROR: {e}") + + print("\n" + "=" * 30) + print(f"Results: {passed}/{total} tests passed") + + if passed >= 3: # Allow TensorFlow to fail + print("๐ŸŽ‰ Deployment should work!") + if passed < total: + print("โš  Some features may be limited (demo mode)") + else: + print("โŒ Deployment may have issues") + + return passed >= 3 + +if __name__ == '__main__': + success = main() + sys.exit(0 if success else 1) diff --git a/test_gradio.py b/test_gradio.py new file mode 100644 index 0000000000000000000000000000000000000000..b65537e47da4a4c896a118c279daa780ccf2160e --- /dev/null +++ b/test_gradio.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python3 +""" +Test script for Gradio app +""" + +import sys +import os + +def test_imports(): + """Test if all required modules can be imported""" + print("Testing imports...") + + try: + import gradio as gr + print(f"โœ… Gradio {gr.__version__}") + except ImportError as e: + print(f"โŒ Gradio: {e}") + return False + + try: + import pandas as pd + print(f"โœ… Pandas {pd.__version__}") + except ImportError as e: + print(f"โŒ Pandas: {e}") + return False + + try: + import numpy as np + print(f"โœ… NumPy {np.__version__}") + except ImportError as e: + print(f"โŒ NumPy: {e}") + return False + + return True + +def test_config(): + """Test configuration import""" + print("\nTesting configuration...") + + try: + from config_hf import get_config, get_example_sequences, get_custom_css + config = get_config() + examples = get_example_sequences() + css = get_custom_css() + + print("โœ… Configuration loaded successfully") + print(f" - Max sequences: {config['MAX_SEQUENCES']}") + print(f" - Example sequences: {len(examples)} characters") + print(f" - CSS length: {len(css)} characters") + return True + + except Exception as e: + print(f"โŒ Configuration failed: {e}") + return False + +def test_model_predictor(): + """Test model predictor""" + print("\nTesting model predictor...") + + try: + from model_predictor import EpitopePredictor + predictor = EpitopePredictor() + + if predictor.model is None: + print("โš ๏ธ Model not loaded - will use demo mode") + else: + print("โœ… Model loaded successfully") + + # Test prediction + test_seq = "MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL" + b_epitopes, t_epitopes = predictor.predict_epitopes(test_seq) + + print(f"โœ… Prediction test: {len(b_epitopes)} B-cell, {len(t_epitopes)} T-cell epitopes") + return True + + except Exception as e: + print(f"โŒ Model predictor failed: {e}") + return False + +def test_gradio_app(): + """Test Gradio app creation""" + print("\nTesting Gradio app...") + + try: + from app_gradio import create_interface + demo = create_interface() + + print("โœ… Gradio interface created successfully") + print(f" - Interface type: {type(demo)}") + + # Test if we can get the config + if hasattr(demo, 'config'): + print("โœ… Interface has config") + + return True + + except Exception as e: + print(f"โŒ Gradio app creation failed: {e}") + return False + +def test_sample_prediction(): + """Test a sample prediction through the interface""" + print("\nTesting sample prediction...") + + try: + from app_gradio import predict_epitopes + + # Test with sample sequence + sample_seq = """>Test_Protein +MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL""" + + # Mock progress function + class MockProgress: + def __call__(self, value, desc=""): + print(f" Progress: {value:.1%} - {desc}") + + result = predict_epitopes(sample_seq, None, 0.5, MockProgress()) + + if len(result) == 5: # Expected return format + summary, b_df, t_df, csv_file, json_file = result + print("โœ… Prediction function works") + print(f" - Summary length: {len(summary)} characters") + print(f" - B-cell epitopes: {len(b_df)} rows") + print(f" - T-cell epitopes: {len(t_df)} rows") + return True + else: + print(f"โŒ Unexpected return format: {len(result)} items") + return False + + except Exception as e: + print(f"โŒ Sample prediction failed: {e}") + return False + +def main(): + """Run all tests""" + print("๐Ÿงช EpiPred Gradio App Test Suite") + print("=" * 40) + + tests = [ + ("Basic Imports", test_imports), + ("Configuration", test_config), + ("Model Predictor", test_model_predictor), + ("Gradio App", test_gradio_app), + ("Sample Prediction", test_sample_prediction) + ] + + passed = 0 + total = len(tests) + + for test_name, test_func in tests: + try: + if test_func(): + passed += 1 + print(f"โœ… {test_name} PASSED") + else: + print(f"โŒ {test_name} FAILED") + except Exception as e: + print(f"โŒ {test_name} ERROR: {e}") + print() + + print("=" * 40) + print(f"Results: {passed}/{total} tests passed") + + if passed >= 4: # Allow model predictor to fail + print("๐ŸŽ‰ Gradio app is ready for deployment!") + if passed < total: + print("โš ๏ธ Some features may be limited (demo mode)") + else: + print("โŒ App has issues that need to be fixed") + + return passed >= 4 + +if __name__ == '__main__': + success = main() + sys.exit(0 if success else 1) diff --git a/test_model.py b/test_model.py new file mode 100644 index 0000000000000000000000000000000000000000..ec117d212e392b311e62b71153ce21e962f39aac --- /dev/null +++ b/test_model.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python3 +""" +Test script to verify the epitope prediction model is working correctly +""" + +import sys +import os +import logging + +# Add the current directory to path +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from model_predictor import EpitopePredictor + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +def test_model(): + """Test the epitope prediction model""" + logger.info("Starting model test...") + + try: + # Initialize predictor + logger.info("Initializing EpitopePredictor...") + predictor = EpitopePredictor() + logger.info("Model loaded successfully!") + + # Get model info + model_info = predictor.get_model_info() + logger.info(f"Model info: {model_info}") + + # Test sequences + test_sequences = { + "Test_Sequence_1": "MKLLILTCLVAVALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNEL", + "Test_Sequence_2": "GIVEQCCTSICSLYQLENYCN", # Insulin A chain + "Test_Sequence_3": "FVNQHLCGSHLVEALYLVCGERGFFYTPKT" # Insulin B chain + } + + for seq_name, sequence in test_sequences.items(): + logger.info(f"\nTesting sequence: {seq_name} (length: {len(sequence)})") + logger.info(f"Sequence: {sequence}") + + try: + # Predict epitopes + b_cell_epitopes, t_cell_epitopes = predictor.predict_epitopes(sequence) + + logger.info(f"Results for {seq_name}:") + logger.info(f" B-cell epitopes: {len(b_cell_epitopes)}") + for i, (epitope, confidence, pos_range) in enumerate(b_cell_epitopes[:3]): + logger.info(f" {i+1}. {epitope} (confidence: {confidence:.3f}, position: {pos_range})") + + logger.info(f" T-cell epitopes: {len(t_cell_epitopes)}") + for i, (epitope, confidence, pos_range) in enumerate(t_cell_epitopes[:3]): + logger.info(f" {i+1}. {epitope} (confidence: {confidence:.3f}, position: {pos_range})") + + except Exception as e: + logger.error(f"Error predicting epitopes for {seq_name}: {e}") + + logger.info("\nModel test completed successfully!") + return True + + except Exception as e: + logger.error(f"Model test failed: {e}") + return False + +if __name__ == "__main__": + success = test_model() + sys.exit(0 if success else 1) diff --git a/wsgi.py b/wsgi.py new file mode 100644 index 0000000000000000000000000000000000000000..569bd7c49fa636e26468b7e000f8cd7d80dd4eb1 --- /dev/null +++ b/wsgi.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +""" +WSGI entry point for production deployment +""" + +import os +import sys +import logging +from pathlib import Path + +# Add the current directory to Python path +current_dir = Path(__file__).parent +sys.path.insert(0, str(current_dir)) + +# Set up logging for production +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) + +# Import the Flask app +try: + from app import app + + # Configure for production + app.config['DEBUG'] = False + app.config['TESTING'] = False + + # Create upload directory + upload_dir = app.config.get('UPLOAD_FOLDER', 'static/uploads') + if upload_dir.startswith('/tmp'): + # For platforms like Render that use /tmp + os.makedirs(upload_dir, exist_ok=True) + else: + # For local deployment + os.makedirs(upload_dir, exist_ok=True) + + logging.info("EpiPred application initialized successfully") + +except Exception as e: + logging.error(f"Failed to initialize application: {e}") + raise + +if __name__ == "__main__": + port = int(os.environ.get('PORT', 5000)) + app.run(host='0.0.0.0', port=port)