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🎓 OptiGrade – Intelligent Academic Companion

Streamlit AI Powered Accuracy Streamlit

License GitHub Stars GitHub Forks Demo

✨ Empowering students through smart academic forecasting, adaptive feedback, and personalized learning journeys

🔍 About OptiGrade

OptiGrade is an AI-powered academic assistant that analyzes student behavior and academic data to generate personalized study plans, predictive insights, and smart progress tracking. Built for higher institutions, OptiGrade empowers students with clarity, confidence, and tailored guidance through each semester.

  • 🚀 Powered by machine learning
  • 📊 Deployed via Streamlit Model Engine
  • 🎨 Designed as a responsive mobile prototype for intuitive student interaction
  • 📊 Project documentation via Zoe Tech Hub
  • 💻 Solo-developed, open-source, and ready to evolve through collaboration

🧩 Tags

ai edtech figma mobile-prototype uiux streamlit student-dashboard
machine-learning academic-planner higher-institution nigeria

📱 Mobile Prototype Preview

Explore the mobile concept design on Figma below:

✨ Live Preview:
"https://www.figma.com/embed?embed_host=streamlit&url=https://www.figma.com/proto/B2L8DOx0u3xuSWPhKpJpO5/OptiGrade-Mobile-App---EduTech?node-id=802-966&starting-point-node-id=802%3A966&scaling=scale-down"

🚀 Features

  • 📊 CGPA Prediction — Forecast with up to 92% accuracy
  • 🧠 Personalized Planning — AI study recommendations based on habits and performance
  • 📂 Course Manager — Organize courses and progress
  • ⏱️ Study Hub — Pomodoro timer, goal tracking, and achievement system
  • 📚 Resource Library — Curated content per department
  • 📈 Analytics Dashboard — Visual insights into patterns and planning

📍 Project Roadmap

🧭 Track progress in GitHub Projects and Issues

Milestone Status Badge
✅ MVP Development Completed MVP
🚧 Feedback Integration In Progress Feedback
⏳ LMS Integration Upcoming LMS
🔍 Model Optimization Upcoming Optimize
🪄 Gamified Learning Flow Planned Gamify
🌐 Global Deployment Future Global

📦 Installation

Clone the repository

git clone https://github.com/CryptoLab-service/OptiGrade-ML-model.git
cd optigrade

Virtual environment setup

python -m venv optigrade_env
optigrade-env\Scripts\activate    # Windows
source optigrade_env/bin/activate # macOS/Linux

Dependencies

pip install -r requirements.txt

Environment config

cp .env.example .env
# Add your Gemini API key

Launch app

streamlit run optigrade_app.py

🐳 For Docker Setup

Prerequisites

Build the Docker Image

docker build -t optigrade-app .

⚙️ Automated Setup

🪟 Windows (.bat)

install.bat

💻 PowerShell (.ps1)

.\setup.ps1

These scripts will:

  • Set up your virtual environment
  • Install required dependencies
  • Copy .env config file
  • Retrain the prediction model
  • Launch the Streamlit app

🔁 Model Retraining

To retrain CGPA prediction:

1. Place your dataset at

data/training_data.csv

2. Columns expected:

  • credit_load
  • study_hours
  • GPA_last_semester
  • current_CGPA
  • target_CGPA

3. Run training script:

python models/train_model.py

4. Generated model file:

models/model.pkl

🤝 Join the OptiGrade Mission

OptiGrade began as a one-developer vision. Now it’s a call for collaboration. Help expand access to intelligent learning tools worldwide.

We welcome:

  • 🏫 Institutions — Integrate with LMS systems
  • 💻 Developers — Build open-source features
  • 🧠 Experts — Review, challenge, and strengthen the logic
  • 🎯 Sponsors — Fund innovation and empower students globally

Together, we build tech that guides students smarter.

👥 Contributors


Toluwalope Oluwalowo
💻 🚀 🧠 🎨 📖 ⚙️ 💡 📋

💬 Want your avatar on this grid?

Submit a Pull Request or join Discussions!

📄 License

This project is licensed under the MIT License.
Feel free to use, modify, and share with proper attribution.

👉 View Full License

💬 Feedback & Contact

Always open to feedback, questions, and new ideas.
Reach out via GitHub Discussions or visit the Streamlit Demo

Built with 💙 for students everywhere.