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--- |
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title: Microbial Susceptibility Analyzer |
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emoji: 👁 |
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colorFrom: blue |
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colorTo: indigo |
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sdk: streamlit |
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sdk_version: 1.43.2 |
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app_file: app.py |
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pinned: false |
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license: mit |
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thumbnail: >- |
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https://cdn-uploads.huggingface.co/production/uploads/64774490312e3909019550f0/JWfMLBEIsP543Hu2G5yAR.jpeg |
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--- |
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# Microbial Susceptibility Analyzer |
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## Overview |
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The Microbial Susceptibility Analyzer is a Streamlit-based web application that combines machine learning and rule-based decision making to predict antibiotic resistance patterns. The application provides: |
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- Susceptibility predictions for organism-antibiotic combinations |
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- Rule-based guidance for treatment decisions |
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- AI-powered explanations and recommendations |
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- Batch prediction capabilities for CSV datasets |
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## Installation |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/yourusername/microbial-susceptibility-analyzer.git |
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cd microbial-susceptibility-analyzer |
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``` |
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2. Create a virtual environment: |
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```bash |
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python -m venv venv |
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source venv/bin/activate # On Windows use: venv\Scripts\activate |
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``` |
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3. Install dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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4. Set up environment variables: |
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Create a `.streamlit/secrets.toml` file with your API keys: |
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```toml |
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OPENAI_API_KEY = "your_openai_api_key" |
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OPENAI_BASE_URL = "https://api.openai.com/v1" |
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``` |
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## Usage |
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Run the application: |
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```bash |
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streamlit run app.py |
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``` |
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The application provides several pages: |
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- **Home**: Project overview and introduction |
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- **Susceptibility Analysis**: Single prediction interface |
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- **AI Assistant**: Expert system for detailed explanations |
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- **Data Upload**: Batch prediction for CSV files |
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- **About**: Project information and contact details |
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## File Structure |
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``` |
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microbial-susceptibility-analyzer/ |
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├── app.py # Main application file |
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├── prediction.py # Prediction logic |
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├── ai_assistant.py # AI integration |
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├── utils.py # Utility functions |
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├── config.py # Configuration settings |
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├── requirements.txt # Python dependencies |
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├── README.md # This documentation |
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├── label_encoders/ # Label encoding files |
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│ ├── organism_label_encoder.json |
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│ ├── antibiotic_label_encoder.json |
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│ └── ... |
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├── models/ # Trained models |
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│ ├── best_model.pkl |
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│ └── best_model_1.pkl |
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└── data/ # Data files |
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├── implied_susceptibility_rules.csv |
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└── microbiology_cultures_implied_susceptibility.csv |
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``` |
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## Configuration |
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The application requires the following configuration: |
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1. **Model Path**: Set in `config.py` |
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2. **Encoder Directory**: Set in `config.py` |
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3. **OpenAI API**: Configure in `.streamlit/secrets.toml` |
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## License |
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
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## Contact |
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For questions or support, contact: |
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Chukwuebuka Anulunko |
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[email protected] |
