Personalized AI Stylist System (PASS)

The Personalized AI Stylist System (PASS) is an end-to-end intelligent framework designed to analyze user preferences, physical attributes, contextual data (weather, events), and real-time fashion trends to generate highly tailored outfit recommendations. It integrates computer vision, recommendation systems, generative AI, and user feedback loops into a modular, scalable architecture.

🧠 System Architecture Overview

PASS/
│
├── core/
│   ├── engine.py
│   ├── orchestrator.py
│   ├── config.py
│
├── data/
│   ├── user_profile.py
│   ├── wardrobe_db.py
│   ├── fashion_trends.py
│
├── ai_models/
│   ├── vision_model.py
│   ├── recommendation_model.py
│   ├── generative_model.py
│
├── services/
│   ├── weather_service.py
│   ├── event_service.py
│   ├── trend_service.py
│
├── modules/
│   ├── outfit_generator.py
│   ├── style_analyzer.py
│   ├── feedback_loop.py
│
├── api/
│   ├── routes.py
│
└── main.py

⚙️ Core Configuration

config.py

class Config:
DEBUG = True
MODEL_VERSION = "1.0.0"
WEATHER_API_KEY = "your_api_key"
TREND_REFRESH_INTERVAL = 86400
MAX_OUTFIT_VARIATIONS = 10

👤 User Profile Management

user_profile.py

class UserProfile:
def __init__(self, user_id):
    self.user_id = user_id
    self.preferences = {}
    self.body_metrics = {}
    self.color_palette = []
    self.style_history = []

def update_preferences(self, preferences):
    self.preferences.update(preferences)

def set_body_metrics(self, height, weight, body_shape):
    self.body_metrics = {
        "height": height,
        "weight": weight,
        "body_shape": body_shape
    }

def add_style_feedback(self, outfit_id, rating):
    self.style_history.append({
        "outfit_id": outfit_id,
        "rating": rating
    })

👗 Wardrobe Database

wardrobe_db.py

class WardrobeDB:
    def __init__(self):
        self.items = []

    def add_item(self, item):
        self.items.append(item)

    def filter_items(self, category=None, color=None):
        results = self.items
        if category:
            results = [i for i in results if i["category"] == category]
        if color:
            results = [i for i in results if i["color"] == color]
        return results

👁️ Vision Model (Outfit Understanding)

vision_model.py

class VisionModel:
def analyze_image(self, image_path):
    # Placeholder for CNN / transformer-based vision model
    return {
        "detected_items": ["shirt", "jeans"],
        "colors": ["blue", "white"],
        "style": "casual"
    }

📊 Recommendation Engine

recommendation_model.py

import random

class RecommendationModel:
def recommend(self, user_profile, wardrobe, context):
    scored_items = []

    for item in wardrobe.items:
        score = self._score_item(item, user_profile, context)
        scored_items.append((item, score))

    scored_items.sort(key=lambda x: x[1], reverse=True)
    return [item for item, _ in scored_items[:10]]

def _score_item(self, item, user_profile, context):
    score = 0

    if item["color"] in user_profile.color_palette:
        score += 2

    if context["weather"] == "hot" and item["category"] == "light":
        score += 3

    if item["style"] in user_profile.preferences.get("styles", []):
        score += 5

    return score + random.random()

🎭 Generative Outfit Model

generative_model.py

class GenerativeModel:
    def generate_outfit(self, items):
        outfit = {
            "top": None,
            "bottom": None,
            "accessories": []
        }

        for item in items:
            if item["type"] == "top":
                outfit["top"] = item
            elif item["type"] == "bottom":
                outfit["bottom"] = item
            else:
                outfit["accessories"].append(item)

        return outfit

🌦️ Context Services

weather_service.py

class WeatherService:
    def get_weather(self, location):
        # Mocked response
        return {
            "temperature": 30,
            "condition": "sunny",
            "weather": "hot"
        }

event_service.py

class EventService:
    def get_event_context(self, user_id):
        return {
            "event": "casual_meeting",
            "formality": "medium"
        }

trend_service.py

class TrendService:
    def get_trending_styles(self):
        return ["minimalist", "streetwear", "athleisure"]

🧩 Style Analyzer

style_analyzer.py

class StyleAnalyzer:
def analyze_user_style(self, user_profile):
    styles = [entry["rating"] for entry in user_profile.style_history]
    avg_score = sum(styles) / len(styles) if styles else 0

    return {
        "dominant_style": user_profile.preferences.get("styles", ["casual"]),
        "engagement_score": avg_score
    }

👗 Outfit Generator Module

outfit_generator.py

class OutfitGenerator:
def __init__(self, recommender, generator):
    self.recommender = recommender
    self.generator = generator

def create_outfits(self, user_profile, wardrobe, context):
    recommended_items = self.recommender.recommend(user_profile, wardrobe, context)
    outfits = []

    for _ in range(5):
        outfit = self.generator.generate_outfit(recommended_items)
        outfits.append(outfit)

    return outfits

🔁 Feedback Loop System

feedback_loop.py

class FeedbackLoop:
def refine_model(self, user_profile, recommendation_model):
    for feedback in user_profile.style_history:
        if feedback["rating"] < 3:
            # Penalize similar items
            pass
        else:
            # Reinforce preferences
            pass

🧠 Orchestrator

orchestrator.py

from services.weather_service import WeatherService
from services.event_service import EventService
from services.trend_service import TrendService

class Orchestrator:
def __init__(self, recommender, generator):
    self.weather_service = WeatherService()
    self.event_service = EventService()
    self.trend_service = TrendService()
    self.recommender = recommender
    self.generator = generator

def run(self, user_profile, wardrobe, location):
    weather = self.weather_service.get_weather(location)
    event = self.event_service.get_event_context(user_profile.user_id)
    trends = self.trend_service.get_trending_styles()

    context = {
        "weather": weather["weather"],
        "event": event["event"],
        "trends": trends
    }

    return self.generator.create_outfits(user_profile, wardrobe, context)

🌐 API Layer

routes.py

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route("/generate-outfit", methods=["POST"])
def generate_outfit():
data = request.json

user_profile = data["user_profile"]
wardrobe = data["wardrobe"]

# Mock response
return jsonify({
    "outfits": ["outfit1", "outfit2"]
})

🚀 Main Entry Point

main.py

from core.orchestrator import Orchestrator 
from ai_models.recommendation_model import RecommendationModel from ai_models.generative_model import GenerativeModel 
from modules.outfit_generator import OutfitGenerator 
from data.user_profile import UserProfile 
from data.wardrobe_db import WardrobeDB

def main():
user = UserProfile(user_id=1)
user.update_preferences({"styles": ["casual", "minimalist"]})
user.color_palette = ["black", "white", "blue"]

wardrobe = WardrobeDB()
wardrobe.add_item({"type": "top", "color": "white", "style": "casual", "category": "light"})
wardrobe.add_item({"type": "bottom", "color": "blue", "style": "casual", "category": "light"})

recommender = RecommendationModel()
generator_model = GenerativeModel()
outfit_generator = OutfitGenerator(recommender, generator_model)

orchestrator = Orchestrator(recommender, outfit_generator)

outfits = orchestrator.run(user, wardrobe, location="Ho Chi Minh City")

print(outfits)

if __name__ == "__main__":
    main()

---

Key Capabilities

• Multi-modal input (images, preferences, context)

• Dynamic outfit generation

• Real-time personalization via feedback loops

• Context-aware styling (weather, events, trends)

• Modular AI pipeline for extensibility