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SopanhaZinII/
skincare-recommendation

Name: skincare-recommendation
Availability: InStock

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Skincare Product Recommendation API

An AI-powered skincare product recommendation system that predicts the best skin type match for skincare products based on their descriptions.

Features

Product Analysis: Analyzes skincare product descriptions using TF-IDF vectorization
Skin Type Classification: Classifies products into 7 skin type categories
Confidence Scores: Provides probability distributions across all skin types
Batch Processing: Support for multiple product predictions

Supported Skin Types

Acne-Prone Skin
Anti-Aging
Brightening
Dry Skin
Normal
Oily
Oily Skin

API Endpoints

`GET /`

Welcome endpoint with API information

`GET /health`

Health check endpoint showing model status and available skin types

`POST /predict`

Predict skin type for a single product

Request Body:

json

{
  "product_description": "Hydrating facial cleanser with ceramides and hyaluronic acid for all skin types"
}

Response:

json

{
  "product_description": "Hydrating facial cleanser...",
  "recommended_skin_type": "Dry Skin",
  "confidence": 0.85,
  "all_probabilities": {
    "Acne-Prone Skin": 0.05,
    "Anti-Aging": 0.03,
    "Brightening": 0.02,
    "Dry Skin": 0.85,
    "Normal": 0.03,
    "Oily": 0.01,
    "Oily Skin": 0.01
  }
}

`POST /batch-predict`

Predict skin types for multiple products at once

How It Works

Text Preprocessing: Product descriptions are processed using TF-IDF vectorization
Classification: A Logistic Regression model classifies the product
Results: Returns the predicted skin type with confidence scores

Technology Stack

FastAPI: Modern web framework
scikit-learn: Machine learning library
Docker: Containerization
Uvicorn: ASGI server

Usage Example

python

import requests

url = "https://your-space-url/predict"
data = {
    "product_description": "Lightweight gel moisturizer with hyaluronic acid"
}

response = requests.post(url, json=data)
print(response.json())

Model Information

Algorithm: Logistic Regression
Features: TF-IDF vectorized product descriptions
Classes: 7 skin type categories
Framework: scikit-learn 1.7.2

SopanhaZinII/skincare-recommendation