Launching GlamFlow Skin Analysis API on RapidAPI

After building the GlamFlow AI web app — an end-to-end skincare analysis platform powered by YOLOv8, ResNet, and large language models — I realized the real value wasn’t just the application itself, but making the AI engine accessible to other developers.

So I packaged GlamFlow into a production-ready REST API and launched it on RapidAPI, allowing developers to integrate advanced skin analysis with a single HTTP request.

👉 Try the GlamFlow Skin Analysis API on RapidAPI

Why Build a Skin Analysis API?

Beauty brands, telehealth platforms, and e-commerce startups increasingly rely on AI-driven personalization, but most lack in-house machine learning expertise.

By exposing GlamFlow as an API, developers can:

• Integrate AI skin diagnostics into mobile and web apps
• Build personalized skincare product recommenders
• Create automated dermatology triage tools

All without managing ML infrastructure.

Core Features

• 🧠 Detection of 15+ skin conditions (acne, blackheads, wrinkles, dark spots, etc.)
• 💧 Skin type classification (dry, normal, oily) with confidence scores
• 💬 Multilingual AI advice (English & French) powered by Groq + Llama 3.3
• 📐 Bounding box coordinates for UI overlays or AR integrations
• ⚡ Sub-3 second average latency, optimized for real-world usage

API Request Flow

1. Client uploads a face image using multipart/form-data
2. Image preprocessing: validation, resizing (max 1024px), and skin presence checks
3. Parallel AI inference pipeline:
   • YOLOv8 for skin issue detection and localization
   • ResNet for skin type classification
   • Groq-powered LLM for human-readable skincare recommendations
4. Structured JSON response containing detections, analysis, and routine suggestions

Example API Request (cURL)

curl -X POST "https://skin-analysis-api.p.rapidapi.com/predict?lang=en" \
  -H "X-RapidAPI-Key: YOUR_API_KEY" \
  -H "X-RapidAPI-Host: skin-analysis-api.p.rapidapi.com" \
  -F "file=@face.jpg"

Example API Response

Key Technical Challenges

File Uploads & RapidAPI Threat Protection

Early versions returned 400 errors because RapidAPI's Threat Protection forwarded images as JSON-wrapped base64 in multipart/form-data. Solution: disable threat protection at the gateway and validate uploads at the application layer using RapidAPI proxy headers.

Performance Optimization

To meet RapidAPI's timeout limits:

• Parallelized YOLOv8 and ResNet inference
• Warmed up models on startup
• Timeout‑protected LLM calls with static fallbacks
• Auto-resized large images (max 1024px)

Result: 95th percentile latency dropped from ~12.4s to ~3.1s.

Adoption, Pricing & First Paying User

• Monetized via RapidAPI subscription plans (Free, PRO, higher tiers)
• First PRO subscriber acquired organically via the marketplace, validating demand.

Reliability & Stability

• Continuous health checks and request logging
• Graceful degradation when LLMs fail
• Strict authentication via RapidAPI proxy headers

Operational results for the first month: 99.97% uptime, sub‑3s average latency, zero critical incidents.

Who Is This API For?

• Beauty & skincare brands
• E‑commerce personalization engines
• Telehealth & dermatology startups
• Mobile app developers
• Agencies delivering AI features

Key Lessons Learned

• Distribution matters as much as the model
• Developer experience drives adoption
• Performance is critical for ML APIs
• Monetization should be planned from day one

Try It Yourself

🔗 https://rapidapi.com/rabihmadtak-rabihmadtak-default/api/skin-analysis-api

Built by Monsif Souri and Othman Sohab — Software & Machine Learning Engineers.

This project is not a medical device. Always consult a dermatologist for persistent skin issues.