---
title: Microbial Susceptibility Analyzer
emoji: 👁
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.43.2
app_file: app.py
pinned: false
license: mit
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/64774490312e3909019550f0/JWfMLBEIsP543Hu2G5yAR.jpeg
---

# Microbial Susceptibility Analyzer

## Overview
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:
- Susceptibility predictions for organism-antibiotic combinations
- Rule-based guidance for treatment decisions
- AI-powered explanations and recommendations
- Batch prediction capabilities for CSV datasets

## Installation

1. Clone the repository:
```bash
git clone https://github.com/yourusername/microbial-susceptibility-analyzer.git
cd microbial-susceptibility-analyzer
```

2. Create a virtual environment:
```bash
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
```

3. Install dependencies:
```bash
pip install -r requirements.txt
```

4. Set up environment variables:
Create a `.streamlit/secrets.toml` file with your API keys:
```toml
OPENAI_API_KEY = "your_openai_api_key"
OPENAI_BASE_URL = "https://api.openai.com/v1"
```

## Usage

Run the application:
```bash
streamlit run app.py
```

The application provides several pages:
- **Home**: Project overview and introduction
- **Susceptibility Analysis**: Single prediction interface
- **AI Assistant**: Expert system for detailed explanations
- **Data Upload**: Batch prediction for CSV files
- **About**: Project information and contact details

## File Structure

```
microbial-susceptibility-analyzer/
├── app.py                 # Main application file
├── prediction.py          # Prediction logic
├── ai_assistant.py        # AI integration
├── utils.py               # Utility functions
├── config.py              # Configuration settings
├── requirements.txt       # Python dependencies
├── README.md              # This documentation
├── label_encoders/        # Label encoding files
│   ├── organism_label_encoder.json
│   ├── antibiotic_label_encoder.json
│   └── ...
├── models/                # Trained models
│   ├── best_model.pkl
│   └── best_model_1.pkl
└── data/                  # Data files
    ├── implied_susceptibility_rules.csv
    └── microbiology_cultures_implied_susceptibility.csv
```

## Configuration

The application requires the following configuration:

1. **Model Path**: Set in `config.py`
2. **Encoder Directory**: Set in `config.py`
3. **OpenAI API**: Configure in `.streamlit/secrets.toml`

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contact

For questions or support, contact:  
Chukwuebuka Anulunko  
chukwuebukaanulunko@gmail.com