This is a submission for the Google AI Studio Multimodal Challenge
What I Built
This applet is designed to act as a personal troubleshooting assistant for messy or confusing physical spaces, specifically a tangled mess of charging cables. It uses Google's multimodal AI to analyze an image of the problem area, identify the key objects, and provide a clear, step-by-step guide to organize the space. The app provides practical, actionable advice with a focus on simple, everyday solutions.
Demo
Applet Link - https://troubleshooter-ai-31180167969.us-west1.run.app
Github Link - https://github.com/i-m-samarth-cs/troubleshooter-ai
Youtube Link - https://youtu.be/DlpJEBfY0d4
How I Used Google AI Studio
I leveraged the Gemini 2.5 Pro model to create this solution. Its multimodal capabilities were essential for the core functionality of the applet. I used its ability to:
Understand and interpret images: The model analyzes the uploaded photo to identify objects like cables, devices, power strips, and any other relevant items on the desk. This visual understanding is the foundation of the app.
Reason about spatial relationships: The AI can recognize the "tangled" nature of the cables and their relationship to the desk and other objects, allowing it to accurately diagnose the problem and suggest appropriate solutions.
Generate a structured, helpful response: Based on its visual analysis, the model generates a numbered list of instructions, a key component of the user experience.
Multimodal Features
The primary multimodal feature is the applet's ability to receive an image as a prompt and generate a text-based, actionable response. This goes beyond simple image recognition; the AI is performing a visual analysis to understand a user's problem and then providing a practical, text-based solution. This enhances the user experience by making troubleshooting immediate and highly personalized. Instead of searching for a generic "how-to" guide, the user gets a custom solution for their specific mess, complete with annotations and suggestions tailored to the objects in their photo.
By - Samarth Shendre - Sinhgad College of Engineering
Top comments (1)
Amazing work @samarth28 🔥