DeepEcho Edge-AI Water Intelligence

DeepEcho is a Streamlit dashboard for pond health monitoring with simulated telemetry, 3D sludge visualization, GPS-referenced mapping, risk scoring, historical logging, and report export.

What The App Does

• Simulates pond bathymetry, sludge accumulation, and environmental readings
• Calculates hypoxia risk and alert state (NORMAL, WARNING, CRITICAL)
• Visualizes pond state in:

1. 3D bathymetry/sludge map
2. Optional GPS-referenced 2D contour map
3. Gauges, radar, and trend charts

• Detects sludge hotspots and tags them with GPS coordinates
• Stores measurements in local SQLite (deepecho.db)
• Exports:

1. PDF report (reportlab)
2. CSV history

Dashboard Sections

• Sidebar controls

1. Operation mode (Monitoring, Survey, Calibration)
2. Auto refresh toggle (2s)
3. GPS center inputs
4. Display toggles for survey path and GPS map

• KPI cards: temperature, hypoxia, ammonia, sludge stats
• Main visual row:

1. 3D pond view (Sludge Depth, Bottom Terrain, Combined)
2. Risk and ammonia gauges

• Secondary row:

1. Water quality radar
2. System health bars

• Tabs:

1. Ammonia forecast
2. Historical trends
3. Raw history table

• AI treatment action plan
• Export buttons (PDF and CSV)

Tech Stack

• Python
• Streamlit
• Plotly
• NumPy
• Pandas
• SciPy
• SQLite
• ReportLab

Repository Structure

app.py                     Streamlit app entrypoint and UI
config.py                  Global config, thresholds, GPS defaults
data_simulator.py          Pond/sludge/environment/GPS simulation
risk_engine.py             Hypoxia risk + alert logic
database.py                SQLite init/insert/load helpers
system_monitor.py          Simulated device health metrics
requirements.txt           Python dependencies
README.md                  Project documentation
design.md                  Architecture/design notes
GPS_INTEGRATION.md         GPS implementation details
Untitled40 (1).ipynb       Notebook/prototype notes
deepecho.db                Local SQLite database (runtime data)
*.h5 / *_quant.tflite      Model artifacts currently stored in repo
DeepEcho_Presentation.pptx Presentation asset
incubation_Report (1).pdf  Document asset

Local Setup

1. Clone the repository:

git clone https://github.com/aazeeem11/DeepEcho-Edge-AI-Water-Intelligence.git
cd DeepEcho-Edge-AI-Water-Intelligence

2. Create and activate a virtual environment:

Windows (PowerShell):

python -m venv .venv
.venv\Scripts\Activate.ps1

Linux/macOS:

python -m venv .venv
source .venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Run:

streamlit run app.py

Default local URL is typically http://localhost:8501.

Configuration

Update config.py for thresholds and pond/GPS defaults:

• Pond size/grid and sludge cap
• Hypoxia and ammonia warning/critical thresholds
• GPS center and pond dimensions in meters

Data And Persistence

• Runtime inserts one measurement per app rerun into SQLite table measurements
• Database file: deepecho.db in project root
• On ephemeral hosting, local DB may reset on restart/redeploy

Exports

• PDF report includes:

1. Snapshot metrics
2. Sludge analysis
3. Treatment plan
4. Recent history

• CSV export contains all stored measurement rows

Troubleshooting

• ModuleNotFoundError: No module named 'reportlab'

1. Run pip install -r requirements.txt

• Port already in use (8501)

1. Stop old Streamlit process, or run streamlit run app.py --server.port 8502

• Empty/short history

1. Let app run with auto-refresh enabled to accumulate rows

Current Limitations

• Inputs are simulated, not live hardware telemetry
• No authentication or role-based access
• Local SQLite only (no managed production backend)

License

No license file is currently included. Add a LICENSE file to define usage terms.