MindCareAI is an innovative platform developed to analyze, predict, and provide actionable insights into mental health trends in Rwanda. Using advanced AI-driven analytics and user-friendly visualizations, it supports mental health professionals, policymakers, educators, and individuals in understanding and addressing mental health challenges effectively.
Mental health issues are an escalating concern globally, including in Rwanda. Key barriers like stigma, limited resources, and lack of data hinder effective intervention and policy-making. MindCareAI seeks to address these issues by providing tools that:
- Assess and predict mental health conditions.
- Visualize mental health trends.
- Deliver AI-driven support for mental health assistance.
MindCareAI integrates data collection, predictive analytics, interactive visualizations, and AI-powered insights to empower mental health support and policy formulation.
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Mental Health Survey Form
- Interactive data collection for demographics, assessment scores, and lifestyle factors.
- Real-time validation to ensure data accuracy.
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Prediction Results Display
- Predicts conditions like depression and anxiety using machine learning models.
- Probability scores and personalized recommendations.
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Interactive Data Visualizations
- Dynamic charts, geographical heatmaps, and sentiment analysis word clouds.
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AI-Powered Insights and Q&A Interface
- AI-based conversational support for personalized mental health responses.
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Feedback and Support Mechanism
- User feedback forms and a directory of mental health resources.
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Accessibility and Localization
- Responsive design and multilingual support (English, French, and Kinyarwanda).
- Languages and Libraries: Python, Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, Plotly.js.
- AI Models: Falcon-7B or Falcon-40B (using Hugging Face Transformers).
- Deployment: Docker for containerization, Jupyter Notebooks for prototyping.
- Framework: React.js (with Material-UI, Plotly.js, and Tailwind CSS).
- State Management: React Context API or Redux.
- Hosting: Vercel or Netlify.
- Framework: Django with Django REST Framework (DRF).
- Database: PostgreSQL (ElephantSQL).
- AI Integration: Hugging Face Inference API.
- Hosting: Render.com or Railway.app.
- Frontend: Built with React.js for survey collection, prediction display, visualizations, AI insights, and feedback forms.
- Backend: Django REST API processes survey data, manages storage, integrates AI services, and supports visualizations.
- AI Service: Hosted Falcon-7B model to provide AI-powered responses through Hugging Face Inference API.
- User Interaction: Data collection, feedback, AI query submission.
- API Communication: RESTful API connects frontend and backend.
- Data Processing: Backend processes data, stores it, and generates predictions.
- AI Integration: AI responses are generated by the Falcon-7B model.
- Visualization Data: Aggregated data is visualized on the frontend.
- Feedback Handling: User feedback informs continuous platform improvements.
- SurveyForm: Collects user data through an interactive survey.
- PredictionResults: Displays predictive analysis.
- DataVisualizations: Renders interactive mental health data charts.
- AIInsights: Q&A interface for AI-generated responses.
- FeedbackForm: Collects user feedback.
- Navbar: Provides navigation across sections.
- Dashboard: Integrates all components for a cohesive UI.
- DRF API: RESTful API endpoints for survey submissions, predictions, AI queries, and feedback.
- ML Models: Predictive models for mental health conditions.
- AI Service Integration: Falcon-7B model generates AI-powered responses.
- Database Management: Stores data using Django ORM with PostgreSQL.
- Initial Setup: Set up repository, create separate frontend and backend directories.
- Data Management: Collect, clean, and preprocess data.
- Model Training: Train and deploy machine learning models.
- Frontend Development: Build and style components, integrate visualizations, AI Q&A, and responsive design.
- Backend Development: Create API endpoints, set up database, integrate AI services.
- Deployment: Deploy frontend (Vercel) and backend (Render.com), connect to database (ElephantSQL).
- Testing: Unit and integration tests for backend and frontend.
- Documentation: Write README, code documentation, and deployment instructions.
- Django Backend: Host on Render.com with environment variables for API tokens and database credentials.
- React Frontend: Deploy on Vercel and configure API URLs.
- Database: Use ElephantSQL for PostgreSQL.
- Frontend: Node.js, npm
- Backend: Python, Django, PostgreSQL
- Clone the repository:
git clone https://github.com/username/MindCareAI.git