https://github.com/srijon57/disease-detector
Practice python3 detecting diseases based on symptoms
https://github.com/srijon57/disease-detector
flask machine-learning pandas python3 scikit-learn typescript vite
Last synced: 3 months ago
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Practice python3 detecting diseases based on symptoms
- Host: GitHub
- URL: https://github.com/srijon57/disease-detector
- Owner: srijon57
- License: mit
- Created: 2025-06-06T10:57:20.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-10-02T03:34:37.000Z (9 months ago)
- Last Synced: 2025-10-02T05:36:13.103Z (9 months ago)
- Topics: flask, machine-learning, pandas, python3, scikit-learn, typescript, vite
- Language: TypeScript
- Homepage: https://disease-detector-drab.vercel.app
- Size: 103 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Disease Detector
A project to detect diseases based on symptoms using a machine learning model. The frontend is built with Vite and TypeScript, and the backend is powered by Python using Flask.
## Table of Contents
- [Features](#features)
- [Technologies](#technologies)
- [Setup](#setup)
- [Frontend Setup](#frontend-setup)
- [Backend Setup](#backend-setup)
- [Usage](#usage)
- [Data](#data)
- [Contributing](#contributing)
- [License](#license)
## Features
- Input symptoms to predict potential diseases.
- View detailed descriptions and precautions for predicted diseases.
- User-friendly interface with search and selection capabilities.
## Technologies
### Frontend
- Vite
- TypeScript
- React
### Backend
- Python 3
- Flask
- scikit-learn
- pandas
## Setup
### Prerequisites
- vite/react (for frontend)
- Python 3 (for backend)
- pip (Python package installer)
### Frontend Setup
1. Navigate to the frontend directory:
cd frontend
2. Install dependencies:
npm install
3. Run the development server:
npm run dev
### Backend Setup
1. Navigate to the backend directory:
cd backend
2. Create a virtual environment (optional but recommended):
python -m venv venv
3. Activate the virtual environment:
source venv/bin/activate
4. Install dependencies:
pip install -r requirements.txt
5. Run the Flask server:
python app.py
## Usage
1. Start the frontend and backend servers as described in the setup instructions.
2. Open your browser and navigate to the frontend URL (usually http://localhost:5173 or similar).
3. Use the interface to select symptoms and predict potential diseases.
## Data
The machine learning model uses a dataset sourced from Kaggle. The dataset includes symptoms mapped to various diseases, along with descriptions and precautions.
## Contributing
Contributions are welcome! Please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with descriptive messages.
4. Push your changes to your fork.
5. Submit a pull request to the main repository.
## License
This project is licensed under the MIT License.