[Proposal] Hybrid Edge–Cloud Vision Pipeline on Raspberry Pi (YOLO + Gemini)

[BlueWaves-afk]

Description of the feature request:

Summary

I propose contributing a production-oriented tutorial to the Google Gemini Cookbook that demonstrates how to build a trigger-based hybrid vision system. This tutorial combines on-device object detection (YOLO-family models on Raspberry Pi) with selective cloud-based multimodal reasoning using the Gemini API.

The goal is to provide developers with a blueprint for real-world IoT deployments where bandwidth, latency, and cloud costs are primary constraints.

Proposed Solution: Hybrid Architecture

This tutorial demonstrates a two-layer "Intelligence Handoff" pattern:

1. Edge Layer (Raspberry Pi)

• Runs a lightweight, quantized YOLO-family detector (via TFLite or ONNX).
• Evaluates frames against Event Triggers (e.g., hazard detection) to gate API calls.

2. Cloud Layer (Gemini API)

• Invoked only when a trigger condition is met.
• Uses Gemini 2.5 Flash / Gemini 3 multimodal reasoning to assess situational severity and generate structured JSON reports for downstream alerts.

Tutorial Structure

1. Environment Setup: Configuring the Raspberry Pi and secure API management.
2. Edge Logic: Implementing the detection loop and trigger thresholds.
3. Intelligence Handoff: Frame encoding and system prompting for multimodal analysis.
4. Performance Analysis: An illustrative comparison showing why this hybrid approach is the standard for modern IoT.

What problem are you trying to solve with this feature?

Existing Cookbook examples focus heavily on static inputs or fully cloud-streamed workflows. Furthermore, there is a significant gap in documentation for low-power edge devices like the Raspberry Pi, which is a primary deployment target for real-world IoT.

Real-world deployments (traffic monitoring, safety systems) cannot support continuous video streaming due to bandwidth constraints and high operational costs. This tutorial fills that gap by providing a reusable architectural template for IoT and robotics developers.

Any other information you'd like to share?

I am an undergraduate CS student at NIT Rourkela and a recent winner of i.mobilothon 5.0 (1st place among 1,900+ teams), where I built VIGIA, a real-time road intelligence system based on these principles.

I am very interested in contributing this tutorial as part of GSoC 2026. To ensure the contribution is high-quality and easy to maintain, I am committed to following the Google Python Style Guide and nblint standards, specifically focusing on:

• Second-Person Voice: Writing for the user (e.g., "You will configure X").
• Modular Logic: Breaking cells into distinct logical steps for readability.
• Reproducibility: Ensuring the notebook is executable from top to bottom.

While I do not have a completed Gemini-integrated prototype yet, I have successfully developed the edge-detection triggers for my hackathon project and am now porting that logic to the Gemini SDK. I would appreciate feedback on scope and alignment before proceeding with the full implementation.