https://github.com/glenmiracle18/machine_learning_summative
Ima go dumb on this one
https://github.com/glenmiracle18/machine_learning_summative
Last synced: 2 months ago
JSON representation
Ima go dumb on this one
- Host: GitHub
- URL: https://github.com/glenmiracle18/machine_learning_summative
- Owner: glenmiracle18
- Created: 2024-11-23T17:02:52.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-23T18:08:04.000Z (6 months ago)
- Last Synced: 2025-02-02T02:31:14.445Z (4 months ago)
- Language: Python
- Size: 72.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Learning Mode Recommendation System
A Flutter application that predicts student adaptability in online learning environments using a machine learning model deployed on Google Cloud Run.
## Vidoe Submission Link
(Comment: I wanted to submit to youtube but I had some issues so I submitted to loom)
https://www.loom.com/share/77261ca7691945f6bf68058c16b74a0a?sid=5367985d-a753-4ee6-9182-43fa3f7219e7
## Project Overview
This system helps predict a student's adaptability level (High, Moderate, or Low) for online learning based on various parameters including:
- Personal Information (Gender, Age, Education Level)
- Environmental Factors (Location, Financial Condition)
- Technical Setup (Internet Type, Device, Network Type)
### Architecture
- Frontend: Flutter mobile application
- Backend: FastAPI Python service
- ML Model: Trained model deployed on Google Cloud Run
- Deployment: Docker container on Google Cloud Run
### Public API Endpoint
```
https://summative-ml-4-794291747792.us-central1.run.app
```
API Routes:
- Health Check: `GET /`
- Predict: `POST /predict`
Example prediction request:
```bash
curl -X POST \
-H "Content-Type: application/json" \
-d '{
"Gender": "Boy",
"Age": "21-25",
"Education_Level": "College",
"Institution_Type": "Government",
"IT_Student": "Yes",
"Location": "Yes",
"Load_shedding": "Low",
"Financial_Condition": "Mid",
"Internet_Type": "Wifi",
"Network_Type": "4G",
"Class_Duration": "3-6",
"Self_Lms": "No",
"Device": "Mobile"
}' \
https://summative-ml-4-794291747792.us-central1.run.app/predict
```
## Project Structure
```
frontend/
├── lib/
│ ├── main.dart
│ ├── models/
│ │ └── learning_data.dart
│ ├── screens/
│ │ ├── personal_info_screen.dart
│ │ ├── environment_screen.dart
│ │ ├── technical_setup_screen.dart
│ │ └── prediction_results_screen.dart
│ ├── services/
│ │ └── api_service.dart
│ ├── utils/
│ │ └── constants.dart
│ └── widgets/
│ ├── custom_dropdown.dart
│ ├── navigation_buttons.dart
│ └── step_indicator.dart
├── android/
├── ios/
├── pubspec.yaml
└── README.md
📦src
┣ 📂__pycache__
┃ ┣ 📜app.cpython-312.pyc
┃ ┣ 📜predict.cpython-312.pyc
┃ ┗ 📜utils.cpython-312.pyc
┣ 📂templates
┃ ┗ 📜index.html
┣ 📜__init__.py
┣ 📜app.py
┣ 📜predict.py
┣ 📜train_model.py
┗ 📜utils.py
|-Dcokerfile
|-requirements.txt
|-README.md
```
## Backend Deployment
The backend is containerized and deployed on Google Cloud Run:
1. Built Docker image:
```bash
docker build -t yaralexie/summative_ml:v5.0 .
```
2. Pushed to Docker Hub:
```bash
docker tag summative_ml yaralexie/summative_ml:v5.0
docker push username/yaralexie/summative_ml:v5.0
```
3. Deployed to Google Cloud Run:
```bash
gcloud run deploy summative-ml \
--image gcr.io/PROJECT_ID/learning-mode-api \
--platform managed \
--region us-central1 \
--allow-unauthenticated
```
## Local Development Setup
### Prerequisites
- Flutter SDK
- Dart SDK
- Android Studio or VS Code
- Python 3.9+
- Docker (optional)
### Running the Flutter App
1. Clone the repository:
```bash
git clone https://github.com/glenmiracle18/machine_learning_summative.git
cd machine_learning_summative
```
2. Get dependencies:
```bash
flutter pub get
```
3. Run the app:
```bash
flutter run
```
### Running the Backend Locally
1. Install Python dependencies:
```bash
pip install -r requirements.txt
```
2. Run the FastAPI server:
```bash
uvicorn app:app --reload
```
The API will be available at `http://localhost:8000`
### Running with Docker
1. Build the Docker image:
```bash
docker build -t summative_ml:v5.0 .
```
2. Run the container:
```bash
docker run -p 8000:8000 summative_ml:v5.0
```
## Features
- Multi-step form for data collection
- Real-time validation
- Loading states and error handling
- Responsive design
- Clean architecture
- State management
- API integration
- Proper error handling
- Form validation
- User feedback
## Technical Details
### Frontend
- Flutter 3.x
- HTTP package for API calls
- Custom widgets for reusability
- Form validation
- State management using setState
- Proper null safety implementation
### Backend
- FastAPI
- Python 3.9
- Machine Learning model
- Docker containerization
- Google Cloud Run deployment
- CORS enabled
- Input validation using Pydantic
- Error handling
- Logging
### API Response Format
```json
{
"prediction": "High",
"status": "success"
}
```
## Future Improvements
- Add user authentication
- Save prediction history
- Offline support
- More detailed predictions
- Enhanced error handling
- Analytics dashboard
- Dark mode support
- Localization
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Push to the branch
5. Create a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.