This is the Streamlit Cloud version of the thesis prototype. It is intentionally lightweight and deployment-safe.
The cloud app demonstrates how network KPI values can be used to estimate Quality of Service behavior and generate practical optimization recommendations.
The cloud app focuses on:
- Manual network parameter input
- CSV upload for multiple network observations
- Latency prediction
- Throughput prediction
- QoS class prediction
- KPI visualizations
- Rule-based optimization strategies
The heavier research model comparison, correlation analysis, and thesis figures are kept in the separate local research project.
Main file path:
app.py
Required repository structure:
5g-cloud-app/
├── app.py
├── requirements.txt
├── data/
│ ├── 5g_network_data.csv
│ └── sample_input.csv
└── docs/
TensorFlow was removed from the cloud version because Streamlit Cloud may use Python versions where TensorFlow wheels are unavailable. The cloud app uses scikit-learn models only, which are reliable for deployment.