Welcome to the "Hack the Future" app, a forward-thinking solution created to address critical mental health challenges facing youth in Rwanda and across Africa. This project harnesses advanced AI and data analytics to provide actionable insights and support, leveraging a robust tech stack including Streamlit, Google Colab, and cutting-edge machine learning models.
Our mission is to create a data-driven mental health platform that provides powerful insights and predictive capabilities for mental health professionals, policy-makers, and communities alike. By empowering stakeholders with real-time analytics, we aim to enhance mental well-being and support for youth in Africa.
- Dynamic Data Visualization: Interactive charts and dashboards provide clear insights into mental health trends among youth.
- Predictive Modeling: Advanced ML models predict mental health outcomes based on various demographic and socio-economic factors.
- AI Chatbot for Mental Health: A responsive chatbot offers immediate support and answers to frequently asked questions about mental health.
- Professional Directory: Connect with a network of mental health professionals for additional guidance and support.
- Multilingual Support: Available in multiple languages, including English and Kinyarwanda, for broader accessibility.
- Community Forum: An anonymous space for users to share experiences and support each other in a safe environment.
Check out the live demo of the app on Streamlit:
- Frontend: Streamlit for a clean, interactive user interface.
- Backend: Google Colab-powered API for ML model predictions and data processing.
- Data Processing: Pandas and Numpy for data handling and preprocessing.
- Machine Learning: Scikit-learn and deep learning models for predictive analytics.
- NLP: TextBlob and NLTK for sentiment analysis and chatbot functionality.
Quickly get started with development using GitHub Codespaces:
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Clone the Repository:
git clone https://github.com/yourusername/hack-the-future.git cd hack-the-future -
Install Dependencies:
pip install -r requirements.txt
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Run the App:
streamlit run app.py
- Python 3.7+
- Streamlit:
pip install streamlit - Google Colab: For hosting the predictive model API
- Youth Mental Health Data: Contains various metrics on mental health, substance use, social support, and demographics.
- General Population Data: Used for comparative analysis and trend visualization.
- Mental Health Indicators: Annual indicators across different mental health diagnoses.
- Dementia and Care Data: Related to specific mental health challenges and treatment facilities in Rwanda.
Note: Ensure data privacy and sensitivity when handling personal information in any form. Anonymize data as necessary.
The model training process and API deployment are managed in Google Colab:
- Data Processing: Cleaning, feature engineering, and outlier detection.
- Model Training: Predictive models including Random Forest, Logistic Regression, and deep learning models for advanced analytics.
- API Integration: Models are exposed through a RESTful API to be called from the Streamlit app for real-time predictions.
Our custom chatbot, Menti, provides users with personalized responses. Menti is trained on common mental health FAQs and offers sentiment analysis to respond empathetically.
This project is licensed under the MIT License.
We welcome contributions! Please fork this repository, make your changes, and submit a pull request for review.
- Fork the Repository
- Create a Feature Branch (
git checkout -b feature-branch) - Commit Changes (
git commit -m "Your message") - Push to Branch (
git push origin feature-branch) - Open a Pull Request
For questions or feedback, please reach out to your-email@example.com or open an issue on GitHub.