DREAMS

Digitization for Recovery: Exploring Arts with Mining for Societal well-being.

DREAMS is an extension of the Beehive project, focused on exploring time and ordering across photo memories to better understand personal recovery journeys. The goal is to build tools that help track and analyze visual narratives over time using data mining and intelligent processing.

Current Progress

• Set up core infrastructure using Flask and Hugging Face models.
• Implemented a basic Caption Sentiment Analysis API to classify emotional tone in user-submitted captions.
• Integrating this API into Beehive to capture sentiment when users upload photos.
• Exploring time-based data structuring and narrative analysis features.

View the API Module

Repositories

• Beehive: github.com/KathiraveluLab/beehive
• DREAMS: github.com/KathiraveluLab/DREAMS

Repository Structure

DREAMS/
├── dreamsApp/                  # Main application package
│   ├── app/                    # Flask app package (app factory + blueprints)
│   │   ├── __init__.py         # create_app() factory
│   │   ├── config.py           # App configuration
│   │   ├── models.py           # Database models
│   │   ├── auth.py             # Authentication routes
│   │   │
│   │   ├── ingestion/          # Image ingestion & processing
│   │   │   ├── __init__.py
│   │   │   └── routes.py
│   │   │
│   │   ├── dashboard/          # Dashboard & analytics views
│   │   │   ├── __init__.py
│   │   │   └── main.py
│   │   │
│   ├── core/                   # Decoupled Core ML / NLP Engine
│   │   ├── pipeline.py         # Standalone ingestion orchestrator
│   │   ├── graph/              # Temporal narrative modeling & analytics
│   │   ├── database.py         # SQLite / Vector storage logic
│   │   └── sentiment.py        # RoBERTa classification logic
│   │
│   └── docs/                   # Project documentation
│
├── data_integrity/             # Data validation utilities
├── location_proximity/         # Location-based analysis (future)
├── dream-integration/          # Integration & experimental code
├── tests/                      # Unit and integration tests
│
├── requirements.txt            # Python dependencies
├── pytest.ini                  # Pytest configuration
└── README.md                   # Project documentation

Installation and Setup

Clone the repository

git clone https://github.com/KathiraveluLab/DREAMS.git
cd DREAMS

(Optional but recommended) Create and activate a virtual environment

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install the required dependencies

pip install -r requirements.txt

For CPU-only installations

pip install -r requirements-cpu.txt

Run tests to verify everything is working

pytest

Start the Flask server in debug mode

flask --app "dreamsApp.app:create_app()" run --debug

Run the Core Pipeline Standalone

The ML algorithm layer (dreamsApp/core/) is designed to operate completely independently from the Flask API layer. The Flask UI runs on MongoDB, whereas the native standalone pipeline operates safely on embedded SQLite / ChromaDB databases for fast local research testing.

Note: A small synthetic dataset for running the pipeline is available in this repository:

git clone [email protected]:ayusrjn/dreams-synthetic-dataset.git

For an in-depth, step-by-step tutorial on how to programmatically execute the DREAMS pipeline and generate visual trajectory plots, please see Example.ipynb.