StockFlow AI 📸🤖

Automated AI-powered stock photo submission pipeline — Generate compelling metadata, enrich with trending tags, and upload stock photos to Shutterstock with minimal manual intervention.

📖 Table of Contents

Project Overview
Features
System Architecture
Quick Start
Configuration
Service Architecture
Extending for New Platforms
Development
Troubleshooting
Known Limitations & Future Work
License

Project Overview

StockFlow AI is a Node.js-based automation pipeline that orchestrates the entire stock photo submission workflow:

Discovers pending images in a source directory
Generates compelling titles, descriptions, and tags using LLM (Ollama)
Analyzes images with vision models for accurate metadata
Enriches tags with platform trending data for better discoverability
Validates all metadata against Shutterstock requirements
Automates web-based uploads via Playwright CDP
Logs all uploads with status tracking and error handling

Perfect for photographers and content creators who want to bulk-upload to stock platforms without manual metadata entry.

Features

✅ AI-Powered Metadata Generation — LLM-generated titles, descriptions, and tag suggestions
✅ Vision Model Analysis — Automatic image description using vision models (llama3.2-vision)
✅ Trending Tag Integration — Automatically fetches and ranks platform trending tags
✅ Comprehensive Validation — Checks title length, description length, tag count, categories
✅ Web Automation — Playwright CDP-based Shutterstock login and image upload
✅ CSV Bulk Upload Support — Generate CSV metadata files for batch submissions
✅ Upload Tracking — JSON logging of all processed images with status and errors
✅ Configurable Retry Logic — Automatic retries on metadata validation failures
✅ Platform-Agnostic Architecture — Designed for easy extension to Getty, Alamy, Adobe Stock
✅ Anti-Bot Timing — Configurable human-like delays to avoid detection
✅ Comprehensive Error Handling — Graceful failures with detailed error messages
✅ Dry-Run Mode — Test the pipeline without uploading

System Architecture

High-Level Pipeline Flow

┌─────────────────────────────────────────────────────────────────┐
│              StockFlow AI Pipeline             │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  1. GET PENDING IMAGES                                          │
│     ↓                                                            │
│     [FileService] → Scan image directory, batch by size          │
│                                                                 │
│  2. FETCH TRENDING TAGS                                         │
│     ↓                                                            │
│     [ShutterStockService] → Browser automation → Parse tags    │
│                                                                 │
│  3. FOR EACH IMAGE ──────────────────────────────────────────  │
│                                                                 │
│     A. GENERATE METADATA                                        │
│        [LangChainService] → Vision model + LLM → JSON output   │
│                                                                 │
│     B. VALIDATE & RETRY                                        │
│        [MetadataValidator] → Check constraints                  │
│        → Retry up to 4 times if invalid                         │
│                                                                 │
│     C. ENRICH TAGS                                             │
│        [TagEnrichmentService] → Score trending tags             │
│        → Merge with generated tags (1-25 total)                │
│                                                                 │
│     D. UPLOAD IMAGE                                            │
│        [ShutterStockService] → Browser automation              │
│        → Fill forms → Submit                                    │
│                                                                 │
│     E. LOG RESULT                                              │
│        [Pipeline] → JSON log + CSV row                          │
│        → Move file to processed/failed directory               │
│                                                                 │
│  4. GENERATE CSV & UPLOAD                                       │
│     ↓                                                            │
│     [CsvService] → Write batch CSV                              │
│     [ShutterStorage] → Upload CSV (if not dry-run)             │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Service Dependency Graph

                    ┌─────────────────────┐
                    │  PipelineService    │ ← Main Orchestrator
                    └──────────┬──────────┘
                              │
          ┌───────────────────┼───────────────────┐
          │                   │                   │
     ┌────▼────┐         ┌────▼────┐      ┌──────▼──────┐
     │ FileService        │ Platform │      │LangChainService
     │(file discovery)    │(uploads) │      │(AI metadata)
     └──────────┘         └────┬────┘      └──────┬──────┘
                               │                   │
                    ┌──────────┴────────────┐      │
                    │                      │      │
            ┌───────▼─────────┐   ┌───────▼──────▼────┐
            │ShutterStockSvc  │   │LLM Chain + Config │
            │(web automation) │   │(Ollama)           │
            └─────────────────┘   └──────────────────┘

                    ┌──────────────────────────┐
                    │   Utility Services       │
                    ├──────────────────────────┤
                    │ • MetadataValidator      │
                    │ • TagEnrichmentService   │
                    │ • CsvService            │
                    └──────────────────────────┘

Platform Abstraction

All platform-specific logic extends PlatformService base class:

PlatformService (Abstract)
  ├── ShutterStockService (✅ Implemented)
  ├── GettyImagesService (🚀 Future)
  ├── AlamyService (🚀 Future)
  └── AdobeStockService (🚀 Future)

The PlatformFactory handles instantiation based on SERVICE_PLATFORMS env variable.

Quick Start

1. Prerequisites

Node.js >= 18.0.0
Chrome/Chromium browser with Chrome DevTools Protocol (CDP) enabled
Ollama running locally with llama3 and llama3.2-vision models

2. Installation

# Clone or download the repository
cd StockFlow AI

# Install dependencies
npm install

# Create environment file
cp .env.example .env  # OR create .env with configuration below

# Create required directories
mkdir -p data processed failed

3. Start Chrome with CDP

# macOS
/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \
  --remote-debugging-port=9222 \
  --user-data-dir=$(mktemp -d) &

# Linux
google-chrome \
  --remote-debugging-port=9222 \
  --no-sandbox \
  --user-data-dir=$(mktemp -d) &

# Windows (PowerShell)
& "C:\Program Files\Google\Chrome\Application\chrome.exe" `
  --remote-debugging-port=9222 `
  --user-data-dir="$env:TEMP\chrome-profile"

4. Run the Pipeline

# Full pipeline: generate metadata, validate, upload to Shutterstock
npm start

# Fetch trending tags only (dry run, no uploads)
npm run trends

# Run with specific environment
SERVICE_PLATFORMS=shutterstock PIPELINE_DRY_RUN=true npm start

5. Monitor Results

Upload Log: ./data/upload-log.json — JSON log of all processed images
CSV Output: ./data/shutterstock-metadata-batch.csv — Bulk metadata for Shutterstock
Processed Images: ./processed/ — Successfully uploaded images
Failed Images: ./failed/ — Images that failed validation or upload

Configuration

Environment Variables

Directory Configuration

Variable	Default	Description
`SCAN_PICTURES_DIRECTORY`	-	Source directory containing images to upload
`PROCESSED_PICTURES_DIRECTORY`	`./processed`	Destination for successfully uploaded images
`FAILED_PICTURES_DIRECTORY`	`./failed`	Destination for images that failed

Shutterstock Configuration

Variable	Default	Description
`SHUTTERSTOCK_USERNAME`	-	Shutterstock contributor username/email
`SHUTTERSTOCK_PASSWORD`	-	Shutterstock password
`SHUTTERSTOCK_MODE`	`web_automation`	Upload method (currently only web_automation)
`SHUTTERSTOCK_CONTRIBUTOR_LINK`	`https://submit.shutterstock.com/`	Shutterstock portal URL
`SHUTTERSTOCK_UPLOAD_PAGE_URL`	Built-in	Shutterstock upload page URL
`SHUTTERSTOCK_ENABLE_ANTI_BOT_DELAYS`	`true`	Enable human-like delays (disable for testing)
`SHUTTERSTOCK_SLOW_MIN_MS`	`2000`	Minimum delay (milliseconds)
`SHUTTERSTOCK_SLOW_MAX_MS`	`4800`	Maximum delay (milliseconds)

LLM Configuration (Ollama)

Variable	Default	Description
`OLLAMA_BASE_URL`	`http://127.0.0.1:11434`	Ollama API endpoint
`OLLAMA_MODEL`	`llama3`	Text generation model name
`OLLAMA_VISION_MODEL`	`llama3.2-vision`	Image analysis model name
`OLLAMA_TEMPERATURE`	`0.2`	LLM creativity (0.0 = deterministic, 1.0 = creative)
`OLLAMA_MAX_RETRIES`	`2`	LLM request retries
`OLLAMA_TIMEOUT_MS`	`60000`	LLM request timeout
`OLLAMA_REQUIRE_VISION`	`true`	Fail if vision model unavailable

Pipeline Configuration

Variable	Default	Description
`PIPELINE_DRY_RUN`	`true`	Simulate without uploading (set to `false` to upload)
`PIPELINE_MAX_IMAGES_PER_RUN`	`1`	Max images to process per run
`PIPELINE_BATCH_SIZE`	`1`	Number of images per batch
`PIPELINE_MAX_METADATA_RETRIES`	`4`	Retries for metadata generation on validation failure
`PIPELINE_UPLOAD_LOG_PATH`	`./data/upload-log.json`	JSON log file path
`PIPELINE_CSV_OUTPUT_PATH`	`./data/shutterstock-metadata-batch.csv`	CSV output path

Metadata Validation

Variable	Default	Description
`SHUTTERSTOCK_TITLE_MIN_LENGTH`	`8`	Minimum title length
`SHUTTERSTOCK_TITLE_MAX_LENGTH`	`120`	Maximum title length
`SHUTTERSTOCK_DESCRIPTION_MIN_LENGTH`	`40`	Minimum description length
`SHUTTERSTOCK_DESCRIPTION_MAX_LENGTH`	`200`	Maximum description length
`SHUTTERSTOCK_MIN_TAG_COUNT`	`10`	Minimum tags required
`SHUTTERSTOCK_MAX_TAGS`	`25`	Maximum tags allowed

Browser & Connectivity

Variable	Default	Description
`LOCAL_CHROMIUM_SERVER`	-	Chrome CDP endpoint (e.g., `http://127.0.0.1:9222`)

Platform (Future-Proofing)

Variable	Default	Description
`SERVICE_PLATFORMS`	`shutterstock`	Comma-separated platforms (e.g., `shutterstock,getty,alamy`)

Service Architecture

Core Services

🏗️ PipelineService

Location: services/Pipeline/index.js

Main orchestrator that coordinates the workflow:

run() — Execute full pipeline: discover images → generate metadata → validate → enrich → upload
getPendingImages() — Get unprocessed images (checks upload log)
buildValidatedMetadata(imagePath, trendingTags) — Generate and validate metadata with retries
getUploadLog() — Load JSON upload tracking log
logResult(entry) — Append result to upload log

// Usage
const pipeline = new PipelineService();
await pipeline.run();

📸 ShutterStockService

Location: services/shutterstock/index.js
Extends: PlatformService (abstract base class)

Handles all Shutterstock-specific operations:

Upload Operations:

uploadImage(input) — Upload single image with metadata
uploadMetadataCsv(csvPath) — Bulk upload metadata via CSV
fetchTrendingTags() — Get platform trending tags

Web Automation:

prefillUploadForm(imagePath) — Fill upload form + select file
prefillLoginForm() — Auto-fill credentials (requires manual submit due to bot detection)
prefillMetadataForm(metadata) — Fill title/description/tags (currently unused, requires manual completion)

Browser Management:

ensureBrowser() — Connect via Chrome CDP
closeBrowser() — Clean up resources

Utility Methods:

randomDelay() — Configurable human-like delays (respects SHUTTERSTOCK_ENABLE_ANTI_BOT_DELAYS)
findFirstAvailable(selectors) — Try multiple CSS selectors
detectPageState() — Identify page state (LOGIN, UPLOAD, etc.)

// Usage
const service = new ShutterStockService();

// Upload image
await service.uploadImage({
  imagePath: '/path/to/image.jpg',
  metadata: {
    title: 'Sunset Beach',
    description: 'Beautiful sunset at the beach...',
    tags: ['sunset', 'beach', 'golden hour'],
    primaryCategory: 'Nature',
  }
});

// Fetch trending tags
const tags = await service.fetchTrendingTags();
console.log(tags); // ['nature', 'landscape', 'travel', ...]

🧠 LangChainService

Location: services/LangChainService/index.js

AI-powered metadata generation using Ollama:

generateMetadata(options) — Generate title, description, tags, and categories
- Analyzes image with vision model
- Processes filename hints and trending tags
- Invokes LLM with structured prompt
- Parses JSON response
describeImageWithVisionModel(imagePath) — Get literal image description
normalizeCategory(value) — Validate category against allowed list
buildFileHints(imagePath) — Extract keywords from filename

// Usage
const service = new LangChainService();

const metadata = await service.generateMetadata({
  imagePath: '/path/to/sunset_beach.jpg',
  trendingTags: ['sunset', 'travel', 'photography'],
  validationErrors: [] // From previous retry
});

console.log(metadata);
// {
//   title: 'Serene Sunset at Tropical Beach',
//   description: 'Golden hour sunset over calm beach waters...',
//   tags: ['sunset', 'beach', 'tropical', ...],
//   primaryCategory: 'Nature',
//   secondaryCategory: 'Travel'
// }

✅ MetadataValidator

Location: services/MetadataValidator/index.js

Validates metadata against Shutterstock requirements:

validate(metadata) — Comprehensive validation
- Title/description length checks
- Character set validation (ASCII + newlines)
- Tag count (10-25) and format
- Category existence
- Unique tags
normalizeTags(tags) — Deduplicate and lowercase tags
cleanText(value) — Remove extra whitespace
normalizeCategory(value) — Map to allowed categories

// Usage
const validator = new MetadataValidator();

const result = validator.validate({
  title: 'Sunset Beach',
  description: 'Beautiful sunset photograph...',
  tags: ['sunset', 'beach', 'nature'],
  primaryCategory: 'Nature'
});

if (!result.valid) {
  console.log(result.errors); // ['not enough tags (min 10)', ...]
}

🏷️ TagEnrichmentService

Location: services/TagEnrichmentService/index.js

Merges generated tags with platform trending tags:

enrich(options) — Rank trending tags by relevance to generated tags
- Scores based on token overlap with generated tags
- Merges both lists, prioritizing generated
- Returns up to maxTagCount unique tags
scoreTag(tag, baseTokens) — Calculate relevance score
normalizeTags(tags) — Normalize and deduplicate

// Usage
const service = new TagEnrichmentService();

const enriched = service.enrich({
  generatedTags: ['sunset', 'beach', 'tropical'],
  trendingTags: ['sunset', 'travel', 'photography', 'landscape']
});

console.log(enriched);
// ['sunset', 'beach', 'tropical', 'travel', 'photography', ...]

📊 CsvService

Location: services/CsvService/index.js

CSV generation for bulk metadata uploads:

toRow(input) — Convert metadata to CSV row
writeBatch(rows, outputPath) — Write CSV file
escape(value) — CSV-escape cell values
toCsvYesNo(value) — Boolean to "yes"/"no" conversion
toIsoOrEmpty(value) — Timestamp to ISO 8601

📁 FileService

Location: services/filesService/index.js

File system operations:

getImageFiles(dirPath, batch) — Recursively find .jpg/.jpeg files, batch by size
moveFileToDirectory(filePath, destDir) — Move file with auto-mkdir
ensureDirectory(dirPath) — Create directory if needed

🔧 PlatformFactory

Location: services/PlatformFactory.js

Factory for instantiating platform services:

// Get single platform service
const shutterstock = PlatformFactory.getPlatformService('shutterstock');

// Get all configured platforms
const services = PlatformFactory.getPlatformServices(); // Reads SERVICE_PLATFORMS env
// { shutterstock: ShutterStockService, getty: GettyImagesService, ... }

// Check platform availability
if (PlatformFactory.isSupported('getty')) { ... }

// Register new platform  (for future implementations)
PlatformFactory.registerPlatform('getty', GettyImagesService);

🏗️ PlatformService (Abstract Base)

Location: services/BaseService/PlatformService.js

Abstract interface all platform implementations must follow:

Required Methods:

uploadImage(input) — Upload single image
uploadMetadataCsv(csvPath) — Bulk metadata upload
fetchTrendingTags() — Get platform trending tags
authenticate(username, password) — Login
logout() — Logout
isAuthenticated() — Check auth status
validateMetadata(metadata) — Platform-specific validation
cleanup() — Resource cleanup
getPlatformInfo() — Platform capabilities/limits

This design enables adding new platforms (Getty, Alamy, Adobe) with custom implementations.

Extending for New Platforms

Adding a new platform (e.g., Getty Images) is straightforward:

Step 1: Create New Service Class

// services/getty/index.js
const PlatformService = require("../BaseService/PlatformService");

class GettyImagesService extends PlatformService {
  constructor() {
    super(config.getty);
    this.platformName = "getty";
  }

  async uploadImage(input) {
    // Getty-specific upload logic
  }

  async uploadMetadataCsv(csvPath) {
    // Getty CSV bulk upload
  }

  async fetchTrendingTags() {
    // Fetch Getty trending tags
  }

  // ... implement other interface methods
}

module.exports = GettyImagesService;

Step 2: Register with Factory

// services/PlatformFactory.js

const GettyImagesService = require("../getty");

class PlatformFactory {
  static PLATFORM_MAP = {
    shutterstock: ShutterStockService,
    getty: GettyImagesService,  // ← Add here
  };
}

Step 3: Configure Getty

# .env
SERVICE_PLATFORMS=shutterstock,getty
GETTY_USERNAME=your_getty_username
GETTY_PASSWORD=your_getty_password
# ... getty-specific config

Step 4: Use in Pipeline

The pipeline automatically uses all configured platforms (or you can explicitly target one):

// Upload to all platforms
SERVICE_PLATFORMS=shutterstock,getty npm start

// Or target single platform
const getty = PlatformFactory.getPlatformService('getty');
await getty.uploadImage({ imagePath, metadata });

That's it! The pipeline handles the rest.

Development

Project Scripts

# Run full pipeline  
npm start

# Fetch trending tags only (useful for testing)
npm run trends

# Dry run (test without uploading)
PIPELINE_DRY_RUN=true npm start

# Test with anti-bot delays disabled (speed up testing)
SHUTTERSTOCK_ENABLE_ANTI_BOT_DELAYS=false npm start

# Lint (once testing framework added)
# npm test

Code Style

Naming: camelCase for variables/methods, PascalCase for classes
Comments: JSDoc for functions, inline comments for complex logic
Async: Always use async/await, avoid callbacks
Error Handling: Try-catch with descriptive error messages
Logging: Use console.log for info, console.warn for warnings, console.error for errors

Adding Logging

Enable debug logging:

# Verbose upload details
DEBUG=* npm start

# Or check debug logs in files
cat /tmp/uploader-debug.log

Troubleshooting

❌ "No browser contexts were found"

Cause: Chrome not running with CDP or port incorrect
Solution:

# Start Chrome with CDP
/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \
  --remote-debugging-port=9222 &

# Verify connection
curl http://127.0.0.1:9222/json/version

❌ "Vision request failed"

Cause: Ollama vision model not installed
Solution:

# Pull vision model
ollama pull llama3.2-vision

# Verify it runs
ollama run llama3.2-vision

❌ "Model did not return JSON output"

Cause: LLM response not properly formatted
Workaround:

Reduce OLLAMA_TEMPERATURE (e.g., 0.1)
Increase OLLAMA_MAX_RETRIES
Check Ollama is online: curl http://127.0.0.1:11434/api/tags

❌ Images not uploading

Cause: Login required but credentials not submitted
Note: Browser automation currently requires manual login due to Shutterstock's bot detection. You'll be prompted to submit login manually when browser opens.

Workaround: Set PIPELINE_DRY_RUN=true to test without uploads.

❌ "Tag length out of bounds"

Cause: Generated tags exceed length limits (2-50 chars)
Solution: Update vocabulary in LLM prompt or adjust validation in MetadataValidator

⚠️ Slow Processing

Optimization:

Disable anti-bot delays for testing: SHUTTERSTOCK_ENABLE_ANTI_BOT_DELAYS=false
Reduce OLLAMA_TIMEOUT_MS for faster LLM inference
Increase PIPELINE_MAX_IMAGES_PER_RUN for batch processing
Use faster LLM: OLLAMA_MODEL=mistral instead of llama3

Known Limitations & Future Work

Current Limitations

❌ Manual Login Submission — Browser automation pauses for manual login due to Shutterstock anti-bot measures
❌ Web Automation Only — No direct API integration (Shutterstock doesn't have public submission API)
❌ Sequential Processing — Processes one image at a time (no concurrent uploads)
❌ No Email/Notification Alerts — Relies on log file monitoring
❌ Metadata Form Filling Incomplete — Title/description/tags still require manual entry after image upload
❌ Limited Error Recovery — Failed uploads require manual retry

Future Enhancements

🚀 Multi-Platform Support — Getty, Alamy, Adobe Stock (architecture ready)
🚀 Direct API Integration — Use official APIs instead of web automation where available
🚀 Concurrent Processing — Upload multiple images in parallel (with rate limiting)
🚀 Queue-Based System — Persistent job queue for large batches
🚀 Advanced Error Handling — Auto-retry with exponential backoff
🚀 Email/Slack Notifications — Alert on upload completion/failure
🚀 Web Dashboard — Monitor pipeline status, logs, and analytics
🚀 Scheduled Runs — Cron-based automated processing
🚀 OAuth Authentication — Securely store credentials instead of env vars
🚀 Unit & Integration Tests — Comprehensive test coverage
🚀 Docker Support — Containerized deployment

Architecture Decisions

Why Playwright CDP?

Real browser automation for handling authentication and form filling
No API required for web interactions
JavaScript execution for complex page logic
Event-driven interaction tracking

Why Ollama (Local LLM)?

Privacy — No data sent to third parties
Cost — Free open-source models
Speed — Local inference, no network latency
Control — Customize models and prompts freely

Why Abstract Factory Pattern?

Extensibility — Easy to add new platforms
Maintainability — Platform-specific logic isolated
Testability — Mock platforms for unit tests
Configuration-driven — Platforms loaded via env vars

Why JSON + CSV Logging?

JSON for programmatic analysis and detailed logging
CSV for direct Shutterstock bulk upload
Dual logging ensures visibility and compatibility

License

Feel free to use, modify, and share this project.

Contributing

This project is open for improvements! Common areas for contribution:

Support

Found an issue or have a feature request? Open a GitHub issue with:

Environment: OS, Node version, Chrome version
Steps to reproduce: Clear reproduction instructions
Expected vs actual behavior
Logs: Relevant console output or upload-log.json excerpt

Acknowledgments

Playwright — Powerful browser automation
LangChain — Excellent LLM orchestration framework
Ollama — Accessible local LLM inference
Shutterstock — For the submission platform

Changelog

v1.0.0 (Current)

✅ Initial release
✅ Shutterstock integration via web automation
✅ LLM-powered metadata generation
✅ Trending tag enrichment
✅ Comprehensive validation
✅ Job tracking and logging
✅ Platform-agnostic architecture
✅ Comprehensive documentation

Happy uploading! 📸

Name		Name	Last commit message	Last commit date
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.github/workflows		.github/workflows
config		config
services		services
.gitignore		.gitignore
README.md		README.md
index.js		index.js
package-lock.json		package-lock.json
package.json		package.json

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StockFlow AI 📸🤖

📖 Table of Contents

Project Overview

Features

System Architecture

High-Level Pipeline Flow

Service Dependency Graph

Platform Abstraction

Quick Start

1. Prerequisites

2. Installation

3. Start Chrome with CDP

4. Run the Pipeline

5. Monitor Results

Configuration

Environment Variables

Directory Configuration

Shutterstock Configuration

LLM Configuration (Ollama)

Pipeline Configuration

Metadata Validation

Browser & Connectivity

Platform (Future-Proofing)

Service Architecture

Core Services

🏗️ PipelineService

📸 ShutterStockService

🧠 LangChainService

✅ MetadataValidator

🏷️ TagEnrichmentService

📊 CsvService

📁 FileService

🔧 PlatformFactory

🏗️ PlatformService (Abstract Base)

Extending for New Platforms

Step 1: Create New Service Class

Step 2: Register with Factory

Step 3: Configure Getty

Step 4: Use in Pipeline

Development

Project Scripts

Code Style

Adding Logging

Troubleshooting

❌ "No browser contexts were found"

❌ "Vision request failed"

❌ "Model did not return JSON output"

❌ Images not uploading

❌ "Tag length out of bounds"

⚠️ Slow Processing

Known Limitations & Future Work

Current Limitations

Future Enhancements

Architecture Decisions

Why Playwright CDP?

Why Ollama (Local LLM)?

Why Abstract Factory Pattern?

Why JSON + CSV Logging?

License

Contributing

Support

Acknowledgments

Changelog

v1.0.0 (Current)

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