How to Add Vision AI to a React Native App Using Expo

How do you add Vision AI to a React Native app?

Add Vision AI to React Native by integrating Expo Camera with GPT-4 Vision or Google Cloud Vision API. Process images locally with TensorFlow.js or send to cloud APIs for analysis. AI Mobile Launcher's Vision Pack provides ready-to-use image classification, OCR, and object detection modules that work both online and offline.

Vision AI transforms mobile apps with powerful capabilities: image classification, object detection, OCR, facial analysis, and more. This guide covers implementation from simple cloud-based solutions to advanced on-device processing.

What Vision AI features can you add to mobile apps?

• Image Classification - Identify objects, scenes, and categories in photos
• Object Detection - Locate and label multiple objects with bounding boxes
• OCR (Text Recognition) - Extract text from images and documents
• Face Detection & Analysis - Detect faces and analyze expressions
• Image Generation - Create images with DALL-E or Stable Diffusion
• Visual Q&A - Answer questions about images using GPT-4 Vision

How do you capture images with Expo Camera?

First, set up Expo Camera to capture images for AI processing:

// Install dependencies
npx expo install expo-camera expo-image-picker expo-file-system

// CameraCapture.tsx
import { CameraView, useCameraPermissions } from 'expo-camera';
import { useState, useRef } from 'react';
import { View, TouchableOpacity, Text, Image } from 'react-native';

export function VisionAICamera() {
  const [permission, requestPermission] = useCameraPermissions();
  const [capturedImage, setCapturedImage] = useState<string | null>(null);
  const cameraRef = useRef<CameraView>(null);

  const captureImage = async () => {
    if (cameraRef.current) {
      const photo = await cameraRef.current.takePictureAsync({
        base64: true, // Include base64 for API calls
        quality: 0.8, // Balance quality vs size
      });
      setCapturedImage(photo.uri);
      
      // Send to Vision AI
      await analyzeWithVisionAI(photo.base64);
    }
  };

  if (!permission?.granted) {
    return (
      <View>
        <Text>Camera access required</Text>
        <TouchableOpacity onPress={requestPermission}>
          <Text>Grant Permission</Text>
        </TouchableOpacity>
      </View>
    );
  }

  return (
    <View style={{ flex: 1 }}>
      <CameraView ref={cameraRef} style={{ flex: 1 }}>
        <TouchableOpacity onPress={captureImage}>
          <Text>Capture & Analyze</Text>
        </TouchableOpacity>
      </CameraView>
    </View>
  );
}

How do you integrate GPT-4 Vision for image analysis?

GPT-4 Vision provides the most versatile image understanding:

// services/visionAI.ts
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

interface VisionAnalysisResult {
  description: string;
  objects: string[];
  text?: string;
  confidence: number;
}

export async function analyzeImageWithGPT4V(
  imageBase64: string,
  prompt: string = 'Describe this image in detail'
): Promise<VisionAnalysisResult> {
  const response = await openai.chat.completions.create({
    model: 'gpt-4-vision-preview',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: prompt },
          {
            type: 'image_url',
            image_url: {
              url: `data:image/jpeg;base64,${imageBase64}`,
              detail: 'high', // or 'low' for faster/cheaper
            },
          },
        ],
      },
    ],
    max_tokens: 500,
  });

  return {
    description: response.choices[0].message.content || '',
    objects: extractObjects(response.choices[0].message.content),
    confidence: 0.95,
  };
}

// Specialized prompts for different use cases
export const VISION_PROMPTS = {
  productIdentification: 
    'Identify the product in this image. Return: name, brand, category, and estimated price.',
  documentOCR: 
    'Extract all text from this document. Preserve formatting and structure.',
  foodAnalysis: 
    'Identify the food items and estimate nutritional information (calories, protein, carbs, fat).',
  plantIdentification: 
    'Identify this plant. Return: common name, scientific name, care instructions.',
};

How do you use Google Cloud Vision API?

Google Cloud Vision offers specialized detection features:

// services/googleVision.ts
interface GoogleVisionResult {
  labels: Array<{ description: string; score: number }>;
  text?: string;
  faces?: Array<{ joy: string; sorrow: string; anger: string }>;
  objects?: Array<{ name: string; confidence: number }>;
}

export async function analyzeWithGoogleVision(
  imageBase64: string,
  features: string[] = ['LABEL_DETECTION', 'TEXT_DETECTION']
): Promise<GoogleVisionResult> {
  const response = await fetch(
    `https://vision.googleapis.com/v1/images:annotate?key=${process.env.GOOGLE_VISION_KEY}`,
    {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        requests: [
          {
            image: { content: imageBase64 },
            features: features.map(type => ({ type, maxResults: 10 })),
          },
        ],
      }),
    }
  );

  const data = await response.json();
  return parseGoogleVisionResponse(data.responses[0]);
}

// Feature types available:
// LABEL_DETECTION - Object/scene labels
// TEXT_DETECTION - OCR
// FACE_DETECTION - Face analysis
// OBJECT_LOCALIZATION - Object bounding boxes
// LOGO_DETECTION - Brand logos
// LANDMARK_DETECTION - Famous landmarks
// SAFE_SEARCH_DETECTION - Content moderation

How do you implement on-device Vision AI?

For privacy and offline support, use TensorFlow.js or ONNX:

// Install TensorFlow.js for React Native
npm install @tensorflow/tfjs @tensorflow/tfjs-react-native
npm install @tensorflow-models/mobilenet

// services/localVision.ts
import * as tf from '@tensorflow/tfjs';
import '@tensorflow/tfjs-react-native';
import * as mobilenet from '@tensorflow-models/mobilenet';

let model: mobilenet.MobileNet | null = null;

export async function initializeLocalVision() {
  await tf.ready();
  model = await mobilenet.load({
    version: 2,
    alpha: 1.0,
  });
  console.log('Local Vision AI ready');
}

export async function classifyImageLocally(
  imageTensor: tf.Tensor3D
): Promise<Array<{ className: string; probability: number }>> {
  if (!model) {
    throw new Error('Model not initialized');
  }
  
  const predictions = await model.classify(imageTensor);
  return predictions.map(p => ({
    className: p.className,
    probability: p.probability,
  }));
}

// Convert image to tensor
export async function imageToTensor(imageUri: string): Promise<tf.Tensor3D> {
  const response = await fetch(imageUri);
  const imageData = await response.arrayBuffer();
  const imageTensor = tf.node.decodeImage(new Uint8Array(imageData), 3);
  return imageTensor as tf.Tensor3D;
}

What are real-world Vision AI use cases?

• E-commerce - Visual product search, virtual try-on, price comparison
• Healthcare - Medical image analysis, skin condition detection, pill identification
• Food & Nutrition - Calorie estimation, ingredient recognition, recipe suggestions
• Education - Homework help, math problem solving, document scanning
• Accessibility - Scene description for visually impaired users
• Agriculture - Plant disease detection, crop monitoring

People Also Ask

Add Vision AI with AI Mobile Launcher

For Developers: AI Mobile Launcher's Vision Pack includes camera integration, GPT-4 Vision, Google Cloud Vision, and offline TensorFlow.js—all pre-configured and production-ready.

For Founders: Building an app with visual AI features? Contact CasaInnov to develop your Vision AI mobile application.