Viso

Viso is a mobile information-measurement system (iOS app) for recognizing objects in images and generating human-readable explanations. It uses Apple’s Vision framework for on-device object detection and FoundationModels to generate GPT-style text descriptions — all running fully offline.

Demo

2025-10-11.17.42.14.mov

🧠 Features

Load images from camera or photo library
Detect multiple objects using the Vision framework
Generate natural language explanations using FoundationModels
Export results (object labels and confidence scores) to CSV
Entirely offline — no internet required
Built with The Composable Architecture (TCA)

📱 Interface Overview

Main screen: View analyzed images and top predictions
Result screen: Tap an image to view a generated explanation
Export: One-tap CSV export with all object data
Smooth navigation, iOS-native UX following Human Interface Guidelines

🛠 Technology Stack

Swift 5.9 / Swift 6
Vision Framework
FoundationModels
The Composable Architecture (TCA)
SwiftUI & UIKit
Local CSV export module

📂 Example CSV Output

ID,Observation,Confidence
E9D4732F...,table,0.87
E9D4732F...,vegetable,0.76
E9D4732F...,salad,0.72

Each row represents an object detected in an image with its confidence score.

📦 Installation (for developers)

Clone the repository:

git clone https://github.com/jaroshevskii/Viso.git

Open the project in Xcode:
```
open Viso.xcodeproj
```
Build and run on a physical iOS device (iOS 26+ recommended)

⚠️ Note: FoundationModels require Apple Silicon or A17+ for local inference

🧪 Sample Result

Given an image of a salad on a wooden table:

Detected objects:

structure (0.93)
wood_processed (0.93)
food (0.76)
salad (0.69)

Generated explanation:

"The image shows a wooden table with a vegetable-based dish, likely a salad, and accompanying kitchen utensils."

📃 License

This project is licensed under the MIT License. See the LICENSE file for details.

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