This assistant helps you to find out if you or someone you care about may be experiencing depression. This application utilizes a Hugging Face's fine-tuned RoBERTa model for sentiment analysis to predict whether a person's input text suggests signs of depression and classifies whether the person is depressed or not.
- Quick Analysis: Input text and get instant feedback on potential depression indicators.
- User-Friendly Interface: Simplistic design for seamless user experience.
- State-of-the-Art Technology: Powered by advanced Hugging Face's fine-tuned RoBERTa model for accurate predictions.
- Input Text: Enter your thoughts, feelings, or any text you want to analyze.
- Analysis: AI model processes the input and identifies patterns associated with depression.
- Results: Receive a clear indication of whether the input suggests signs of depression.
- Python: Machine learning model implementation.
- Flask: Web framework for building the application.
- PyTorch: Deep learning library for training and deploying machine learning models.
- Transformers: Library for natural language processing tasks.
- HTML/Javascript: Frontend design and user interface development.
To run the Depression Detection Web App locally, follow these steps:
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Clone this repository:
git clone https://github.com/your-username/depression-detection-web-app.git
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Navigate to the project directory:
cd depression-detection-web-app -
Install dependencies:
pip install __
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Run the Flask application:
python app.py
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Open your web browser and go to
http://localhost:5000to access the app.
- Pramod Koujalagi
- Contribute to update the list with your name! 😉
We welcome your feedback! If you have any suggestions, ideas, or issues, please open an issue or create a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.