🧠 AI-Powered Mental Health Support System

A comprehensive, safety-first, and scalable platform leveraging machine learning and generative AI to detect, respond to, and manage mental health risks—while also providing journaling and mood-tracking capabilities.

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Table of Contents

1. Overview
2. Features
3. System Architecture
4. Tech Stack
5. Key Modules
6. Installation
7. Usage
8. Data Privacy & Compliance
9. Contributing / Journal
10. License
11. Disclaimer
12. Acknowledgments
13. Contact

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Overview

This system is designed to identify and respond to potential suicide risks in real-time using advanced machine learning classification (TF-IDF and Logistic Regression) and Retrieval Augmented Generation (RAG), integrated with generative models (e.g., GitHub Models API with GPT-4, Claude) to provide empathetic, context-aware responses. In addition to AI-based direct support, it offers a journal and mood tracking feature set for users to record personal reflections and monitor their emotional well-being over time.

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Features

• Automatic Suicide Risk Detection
  Uses a Machine Learning Classification Pipeline (MCP) with TF-IDF and logistic regression to flag concerning user-generated text.

• Keyword Screening
  Real-time filter for suicide/self-harm keywords to initially flag content for review.

• RAG (Retrieval Augmented Generation)
  Integration of curated resources and knowledge bases to enhance AI responses with relevant context.

• Generative AI Support
  Empathetic and context-rich conversations powered by GitHub Models API with access to GPT-4, Claude, and other state-of-the-art models.

• Admin Dashboard
  A React-based interface for administrators to:

  • Review flagged messages
  • Escalate urgent cases
  • Annotate conversation histories
  • View mood/journal summaries (according to user consent)

• Mood & Journal Tracking
  Enables users to log daily/weekly moods and maintain private journals with optional sentiment analysis and personal progress charts.

• Compliance & Audit Logging
  Facilitates compliance with data privacy regulations (GDPR, HIPAA-like requirements for health data).

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System Architecture

1. FastAPI Backend
   • Hosts the classification model, manages user data, handles API routes.
2. GitHub Models API Integration
   • Powers generative responses using state-of-the-art models like GPT-4 and Claude.
3. React Admin Dashboard
   • Allows moderators or mental health professionals to review and manage flagged content.

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Tech Stack

• Backend: Python, FastAPI, Scikit-learn, SQLite/PostgreSQL
• Frontend: React, Material-UI
• AI APIs: GitHub Models API (GPT-4, Claude, o1)
• Others: Docker (optional), JWT-based Auth, RESTful Services

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Key Modules

1. MCP Model

   • Preprocessing: Text cleaning & tokenization
   • Feature Extraction: TF-IDF
   • Classifier: Logistic Regression
   • Keyword Matching for initial detection

2. Mood & Journal Tracking

   • Users can log their mood on a daily/weekly schedule (numeric scale, emoji-based, etc.).
   • A journal for personal reflections—can optionally run sentiment analysis.
   • Trend charts to visualize emotional patterns.

3. RAG Pipeline

   • Combines AI knowledge retrieval with generative models for context-aware responses.
   • Ideal for referencing relevant mental health resources/articles.

4. Generative Response Integration

   • Uses GitHub Models API with GPT-4, Claude, and other advanced models to generate empathetic, context-tuned messages.
   • Automatic escalation triggers for high-risk user statements.

5. Admin Dashboard

   • Secure login for mental health professionals or moderators.
   • Flagged messages review, risk assessment, and escalation.
   • Oversees mood/journal entries (with the user’s permission).
   • Maintains audit logs for compliance.

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Installation

1. Clone the Repository

   git clone https://github.com/your-org/ai-mental-health-support.git
   cd ai-mental-health-support
   

Screenshots