Behavioral Health Vault (BHV) Prototype

[safiya2610]

✅ - Implemented a minimal FastAPI-based BHV prototype focused on simplicity and easy deployment
✅ - Added basic authentication (email/password) with session handling

• Includes Google OAuth support
  ✅ Implemented image upload, view, and delete functionality
• Uses local filesystem storage (no external services)
  ✅ Designed a clean, gallery-style UI
• Single-page layout
• No heavy frontend framework
  ✅ Integrated an optional fuzzy color–emotion analysis module
• Extracts dominant color palette from images
• Generates neutral visual metadata (emotion space, confidence, visual stats)
• Metadata stored separately
• Does not override user intent
  ✅ Ensured privacy by default
• Images are private unless explicitly marked public
• Metadata hidden by default
  ✅ Implemented metadata modal (popup card)
• Opens only on user button click
• Displays full visual metadata
• Easy close using cross button
  ✅ Kept the system single-command runnable
• No separate frontend / backend / database services
  ✅ Used SQLite + local filesystem storage
• Lightweight setup
• Clinic-friendly and easy to maintain

Future Work

1. Cloud-based Image Storage
   Integrate Cloudinary for scalable and secure image storage

2. Support both local storage (default) and cloud storage (optional) modes
   Role-Based Access Control

3. Introduce Admin and User roles
   Allow administrators to:
   View and moderate all user submissions
   Upload or edit images and narratives on behalf of users
   Manage visibility and moderation actions

4. Enhanced Research Module
   Improve fuzzy color–emotion analysis with:
   Better color clustering
   Confidence calibration
   Support for comparative analysis across multiple images
   Enable researchers to analyze trends over time instead of single-image results
   Improved Privacy Controls
   Granular privacy settings per image and narrative

Commands to run Prototype

Create Virtual Environment
python -m venv venv

Activate Environment
Windows
venv\Scripts\activate

Linux / macOS
source venv/bin/activate

Install Dependencies
pip install -r requirements.txt
Minimal stack: FastAPI, SQLite, Jinja, Pillow, scikit-image

Run the Application (Single Command)
uvicorn main:app --reload

screen-capture.2.webm

[gemini-code-assist]

Summary of Changes

Hello @safiya2610, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces the foundational prototype for the Behavioral Health Vault (BHV) application. It establishes a robust backend using FastAPI for handling user authentication, secure image storage, and dynamic content delivery. A key innovation is the integration of a fuzzy color-emotion analysis system, which provides insightful, privacy-preserving metadata for uploaded images. The frontend is designed for a clean, gallery-like experience, ensuring ease of use and a focus on visual content.

Highlights

• Core Prototype Implementation: A minimal FastAPI-based Behavioral Health Vault (BHV) prototype has been implemented, focusing on simplicity and ease of deployment with a single-command runnable system using SQLite and local storage.
• Authentication and Session Management: Basic email/password authentication with session handling has been added, alongside integration for Google OAuth for user login.
• Image Management and UI: Functionality for image upload, viewing, and deletion is now available, utilizing local filesystem storage. A gallery-style single-page UI has been designed for a minimal user experience.
• Fuzzy Color-Emotion Analysis: An optional module for fuzzy color-emotion analysis has been integrated. This module extracts dominant color palettes from uploaded images and generates neutral visual metadata (emotion space, confidence, visual stats), which is stored separately and does not override user intent.
• Privacy Features: Privacy is ensured by default, with images remaining private unless explicitly marked public. Visual metadata is hidden behind a user-triggered popup modal, ensuring it's not shown by default.

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[gemini-code-assist]

Code Review

This pull request introduces a new FastAPI application for image management, incorporating user authentication (local and Google OAuth), image upload with fuzzy emotion detection, a gallery view, and image narrative updates. The changes include adding cleaned_data.json for color-emotion mapping, config.py for environment variables, db.py for SQLite database setup (users, images, and friends tables), and fuzzy_emotion.py for generating image metadata. The main.py file integrates these components, handling various user interaction routes, while narrative.py provides an endpoint for updating image narratives. Frontend updates involve new CSS files (cards.css, gallery.css, gallery_pg.css, login.css, navbar.css, style.css) and HTML templates (base.html, cards.html, gallery_pg.html, gallery.html, home.html, login.html, parallax.html, signup.html) to support the new features and enhance the user interface. Review comments identify several critical issues, including security vulnerabilities related to a hardcoded SECRET_KEY, hardcoded Google OAuth redirect_uri, path traversal in the image deletion endpoint, and missing authentication/authorization for narrative updates. Concurrency and reliability concerns are raised due to global database connections in db.py and overly broad exception handling in fuzzy_emotion.py and main.py. Dependency management in requirements.txt is noted as incomplete (missing scikit-learn, scikit-image) and bloated (unused tensorflow, deepface). Code quality issues include an unused variable in fuzzy_emotion.py, duplicate CSS definitions in gallery.css, invalid </img> tags in cards.html and parallax.html, and an overloaded narrative database column in gallery.html that conflates narrative text with visibility status.

[gemini-code-assist]

This endpoint is missing authentication and authorization. As it is, any user (even an unauthenticated one) can update the narrative for any image in the system simply by knowing its filename. This is a critical security vulnerability. You must add a check to ensure that a logged-in user is making the request and that they are the owner of the image they are trying to modify.