AI Image Validator #

A Flutter package for validating images using Google's Gemini AI. Easily verify if images match specific classes with customizable confidence thresholds.

🎥 Demo #

See the package in action: image selection, AI validation, and result display.

✨ Features #

• 🤖 AI-Powered Validation: Uses Google's Gemini AI for accurate image classification
• 📸 Image Selection: Built-in support for camera and gallery
• 🎯 Customizable Classes: Define your own list of allowed image classes
• ⚙️ Confidence Threshold: Set minimum confidence levels for validation
• 🎨 Fully Customizable UI: Create your own interface, the package handles only the logic
• ⚡ Simple API: Easy to integrate with just a few lines of code

📦 Installation #

Add this to your package's pubspec.yaml file:

dependencies:
  ai_image_validator: ^1.0.0

Then run:

flutter pub get

🔑 Getting Started #

1. Get a Gemini API Key #

1. Visit Google AI Studio
2. Create a new API key (it's free!)
3. Copy your API key

2. Initialize the Package #

import 'package:ai_image_validator/ai_image_validator.dart';

void main() {
  // Initialize once at app startup
  AiImageValidator.initialize(
    apiKey: 'YOUR_GEMINI_API_KEY',
    model: 'gemini-2.0-flash', // Optional, this is the default
  );
  
  runApp(MyApp());
}

🚀 Usage #

Basic Example #

import 'package:ai_image_validator/ai_image_validator.dart';
import 'dart:io';

// 1. Pick an image
final File? imageFile = await AiImageValidator.pickImage(
  ImageSource.gallery, // or ImageSource.camera
);

if (imageFile != null) {
  // 2. Validate the image
  final ImageValidationResult result = await AiImageValidator.validateImage(
    imageFile: imageFile,
    allowedClasses: ['cat', 'dog', 'bird'],
    minConfidence: 0.7,
  );

  // 3. Check the result
  if (result.isAllowed) {
    print('✅ Valid image: ${result.detectedClass}');
    print('Confidence: ${result.confidence}');
  } else {
    print('❌ Invalid image');
  }
}

Complete Example with Custom UI #

import 'package:flutter/material.dart';
import 'package:ai_image_validator/ai_image_validator.dart';
import 'dart:io';

class ImageValidatorPage extends StatefulWidget {
  @override
  _ImageValidatorPageState createState() => _ImageValidatorPageState();
}

class _ImageValidatorPageState extends State<ImageValidatorPage> {
  File? _image;
  ImageValidationResult? _result;
  bool _isLoading = false;

  // Configure your validation parameters
  final List<String> allowedClasses = ['cat', 'dog', 'bird'];
  final double minConfidence = 0.7;

  Future<void> _validateImage(ImageSource source) async {
    setState(() => _isLoading = true);

    try {
      // Pick image
      final imageFile = await AiImageValidator.pickImage(source);
      
      if (imageFile == null) {
        setState(() => _isLoading = false);
        return;
      }

      // Validate image
      final result = await AiImageValidator.validateImage(
        imageFile: imageFile,
        allowedClasses: allowedClasses,
        minConfidence: minConfidence,
      );

      setState(() {
        _image = imageFile;
        _result = result;
        _isLoading = false;
      });

      // Show result
      ScaffoldMessenger.of(context).showSnackBar(
        SnackBar(
          content: Text(
            result.isAllowed 
              ? '✅ Valid: ${result.detectedClass}'
              : '❌ Invalid image',
          ),
          backgroundColor: result.isAllowed ? Colors.green : Colors.red,
        ),
      );
    } catch (e) {
      setState(() => _isLoading = false);
      ScaffoldMessenger.of(context).showSnackBar(
        SnackBar(content: Text('Error: $e')),
      );
    }
  }

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(title: Text('AI Image Validator')),
      body: Center(
        child: Column(
          mainAxisAlignment: MainAxisAlignment.center,
          children: [
            if (_image != null) Image.file(_image!, height: 200),
            if (_result != null) ...[
              Text('Class: ${_result!.detectedClass}'),
              Text('Confidence: ${(_result!.confidence * 100).toInt()}%'),
              Text(_result!.isAllowed ? '✅ Valid' : '❌ Invalid'),
            ],
            if (_isLoading) CircularProgressIndicator(),
            SizedBox(height: 20),
            ElevatedButton.icon(
              onPressed: () => _validateImage(ImageSource.gallery),
              icon: Icon(Icons.photo_library),
              label: Text('Pick from Gallery'),
            ),
            ElevatedButton.icon(
              onPressed: () => _validateImage(ImageSource.camera),
              icon: Icon(Icons.camera_alt),
              label: Text('Take Photo'),
            ),
          ],
        ),
      ),
    );
  }
}

📖 API Reference #

AiImageValidator.initialize() #

Initializes the Gemini AI client. Must be called once before using the package.

Parameters:

• apiKey (String, required): Your Gemini API key
• model (String, optional): Gemini model to use (default: 'gemini-2.0-flash')

AiImageValidator.initialize(
  apiKey: 'YOUR_API_KEY',
  model: 'gemini-2.0-flash',
);

AiImageValidator.pickImage() #

Opens the device's image picker to select an image.

Parameters:

• source (ImageSource, required): Either ImageSource.camera or ImageSource.gallery

Returns: Future<File?> - The selected image file, or null if cancelled

final File? image = await AiImageValidator.pickImage(ImageSource.gallery);

AiImageValidator.validateImage() #

Validates an image using Gemini AI.

Parameters:

• imageFile (File, required): The image file to validate
• allowedClasses (List
• minConfidence (double, optional): Minimum confidence threshold (default: 0.7)

Returns: Future<ImageValidationResult>

final result = await AiImageValidator.validateImage(
  imageFile: imageFile,
  allowedClasses: ['cat', 'dog', 'bird'],
  minConfidence: 0.7,
);

ImageValidationResult #

The result object returned by validateImage().

Properties:

• detectedClass (String): The class detected by the AI
• confidence (double): Confidence level (0.0 - 1.0)
• isAllowed (bool): Whether the image is valid (class is in allowedClasses and confidence >= minConfidence)

print(result.detectedClass); // e.g., "cat"
print(result.confidence);    // e.g., 0.95
print(result.isAllowed);     // e.g., true

💡 Use Cases #

• Profile Picture Validation: Ensure users upload appropriate profile photos
• Product Image Verification: Validate that uploaded images match product categories
• Content Moderation: Filter images based on specific categories
• Document Verification: Check if uploaded images are valid documents
• Animal/Pet Recognition: Verify pet images for pet-related apps
• Food Classification: Validate food images for recipe or restaurant apps

🎨 Customization #

This package provides only the core validation logic. You have complete freedom to:

• Design your own UI/UX
• Implement custom image pickers
• Add your own loading states
• Create custom error handling
• Integrate with state management (GetX, Provider, Bloc, etc.)

See the example folder for a complete implementation with GetX, BottomSheet, and SnackBar.

⚙️ Requirements #

• Flutter SDK: >=3.10.0
• Dart SDK: >=3.10.4
• Google Gemini API key (free at Google AI Studio)

🔒 Supported Platforms #

• ✅ Android
• ✅ iOS
• ✅ Web
• ✅ macOS
• ✅ Windows
• ✅ Linux

📱 Permissions #

Android #

Add to android/app/src/main/AndroidManifest.xml:

<uses-permission android:name="android.permission.CAMERA"/>
<uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>

iOS #

Add to ios/Runner/Info.plist:

<key>NSCameraUsageDescription</key>
<string>We need access to your camera to take photos</string>
<key>NSPhotoLibraryUsageDescription</key>
<string>We need access to your photo library to select images</string>

🤝 Contributing #

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License #

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

🙏 Credits #

• Powered by Google Gemini AI
• Uses image_picker for image selection
• Uses google_generative_ai for AI integration

📞 Support #

• 🐛 Report bugs
• 💡 Request features
• 📧 Email: your-email@example.com

🌟 Show Your Support #

If this package helped you, please ⭐️ the repo and share it with others!