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<div align="center">
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<img width="1200" height="475" alt="GHBanner" src="https://github.com/user-attachments/assets/0aa67016-6eaf-458a-adb2-6e31a0763ed6" />
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</div>
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# Airguard VisionEdge
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# Run and deploy your AI Studio app
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**Airguard VisionEdge** is a concept for an advanced system that enables environmental researchers and on-site analysts to detect, visualize, and interpret greenhouse gas (GHG) anomalies in near-real-time using a fusion of Edge AI and satellite data.
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This contains everything you need to run your app locally.
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## 🌍 Concept Summary
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View your app in AI Studio: https://ai.studio/apps/drive/1v-6gmO3NbzaoIYMnQoFR10ib-7kjQWrO
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**Purpose:** To enable environmental researchers and on-site analysts to detect, visualize, and interpret greenhouse gas (GHG) anomalies in near-real-time using Edge AI + satellite fusion.
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## Run Locally
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## System Architecture
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**Prerequisites:** Node.js
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The system is designed with three core layers:
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1. **Edge Impulse Node:**
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* Runs optimized Machine Learning models (TinyML) for local emission pattern detection.
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* Performs inference on satellite raster tiles and ground sensor data (e.g., $CO_2$, $PM2.5$).
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1. Install dependencies:
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`npm install`
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2. Set the `GEMINI_API_KEY` in [.env.local](.env.local) to your Gemini API key
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3. Run the app:
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`npm run dev`
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2. **Web Dashboard (Android-first):**
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* Displays fused insights through interactive maps, charts, and anomaly markers.
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* Syncs with Google Colab notebooks for advanced analytics and deep-dive visualization.
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3. **LLM Assistant (VisionEdge Copilot):**
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* An on-device LLM assistant that explains observed trends.
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* Recommends research insights and provides context for data anomalies.
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## 📱 UX Flow Overview
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1. **Login & Device Sync Screen**
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* Users sign in via Google or their institutional account.
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* Sync connected Edge Impulse devices via Bluetooth/WiFi.
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* The "Add New Station" feature detects and registers a local AI node.
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2. **Home Dashboard**
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* **Top Bar:** "VisionEdge" title with quick filters (Region | Model | Timeframe).
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* **Live Map Panel:** Displays raster data tiles with overlay layers for GHG, $NO_2$, and temperature. Edge inferences are highlighted as colored hotspots.
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* **Mini Stats Bar:** Shows key metrics like Emission Index, Confidence Level, and Anomaly Count. Tapping opens an expanded metrics view.
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3. **Analysis Panel**
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* Organized into tabs: `AI Inference` | `Time Series` | `Correlations` | `Ground Data`.
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* Features interactive plots generated from Edge outputs.
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* An "Open in Colab" option launches a notebook session with linked data for deeper analysis.
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4. **Ingestion**
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* Upload sensor data files directly to Edge Impulse project for training and analysis.
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5. **Copilot Assistant**
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* A floating chat widget allows users to "Ask VisionEdge Copilot."
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* Users can query the system with natural language, e.g., *“Explain today’s emission spike in the Cairo region”* to receive an AI-driven explanation.
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6. **Export & Share**
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* Download comprehensive reports as PDF or GeoTIFF files.
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* Push results directly to a shared research group or an institutional drive.
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## 🎨 Design Direction
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* **Theme:** A space black background with green-cyan gradients to represent emission heatmaps.
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* **UI Style:** Sleek and minimal, following Material 3 design principles with a card-based layout.
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* **Interactions:** Smooth map transitions, animated data updates, and collapsible charts for a fluid user experience.
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* **Data Visualization:**
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* 2D raster overlays with opacity controls.
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* Dynamic graphs for comparing local inferences and historical data.
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## Contributing
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Contributions, issues, and feature requests are welcome.
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For significant contributions, please propose an issue first to discuss what you would like to change.
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## License
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Licensed under the MIT License. See `LICENSE` for details.
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## Authors
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* [Ahmed Ibrahim Metawee]
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* [AIMTY]

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