Vision 2026 is a goal-tracking and reflection system designed to help users set goals, track daily/weekly execution, and monitor long-term consistency (monthly → yearly).
This project focuses on behavioral feedback over time — not just checking tasks.
Most trackers answer: “Did you do the task?”
Vision 2026 answers:
- Are you consistent over time?
- Which life domains are neglected?
- Are you on track for long-term goals (monthly/quarter/semester/yearly)?
- Where do you need to adjust your behavior to reach your objectives?
- Goal Setup: create goals with a category + frequency
Daily, Weekly, Monthly, Quarterly, Semester, Yearly - Tracking Board
- Reports
- Daily consistency timeline
- Weekly consistency table
- Long-term strategic health
- Frontend: Streamlit
- Database: Neon (PostgreSQL)
- Data: Pandas
- Charts: Plotly
- Auth: simple email/password (MVP)
vision-2026/
├── app.py
├── core_logic.py
├── db_utils.py
├── requirements.txt
├── packages.txt
├── .gitignore
├── README.md
├── .streamlit/
│ ├── config.toml
│ └── secrets.toml # local only (never commit)
└── screenshots/
git clone https://github.com/sbendimerad/VisionBoard2026.git
cd vision-2026conda create -n vision2026 python=3.11 -y
conda activate vision2026pip install -r requirements.txtThis project reads the Neon connection string from Streamlit secrets.
Create this file locally:
mkdir -p .streamlit
touch .streamlit/secrets.tomlAdd your Neon connection string inside:
DATABASE_URL="postgresql://<user>:<password>@<host>/<dbname>?sslmode=require"Your .gitignore should include:
.streamlit/secrets.tomlstreamlit run app.py- For deployment, you should not upload
secrets.toml. - Instead, set the secret in your platform settings. 👉 You can deploy on : https://share.streamlit.io
This repository currently uses a simple MVP auth approach. For production, you should implement:
- secure password hashing (bcrypt/argon2)
- proper session management
- rate limiting and brute-force protection
- password reset flow
- Performance optimizations (reduce per-checkbox DB queries, caching, indexes)
- Cleaner UX for large goal catalogs
- CSV export/import for users
- Stronger auth and user onboarding
- Public demo deployment
Built by Sabrine Bendimerad AI / Data Science / MLOps