feat: Initial Recommender service with ML

README.md

@@ -1,13 +1,30 @@
 ---
-title: Styleto Recommender
-emoji: 🚀
+title: Labasni Recommender
+emoji: 👔
 colorFrom: blue
-colorTo: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.1.0
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
-license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎽 Labasni Outfit Recommender
+
+Service de recommandation d'outfits basé sur Machine Learning.
+
+## Features
+- Recommandations intelligentes selon le style
+- Prise en compte de la météo et des saisons
+- API REST accessible depuis NestJS
+
+## Usage
+```python
+import requests
+response = requests.post(
+    "https://VOTRE_USERNAME-labasni-recommender.hf.space/api/predict",
+    json={
+        "data": [clothes_json, "casual", "Tunis"]
+    }
+)
+```

app.py

@@ -0,0 +1,54 @@
+"""
+Labasni Recommender Service - Hugging Face Space
+Interface Gradio pour les recommandations d'outfits
+"""
+
+import gradio as gr
+import json
+from recommender_model import recommend_outfit_ml
+
+def recommend_outfit_api(clothes_json: str, preference: str, city: str = "Tunis"):
+    """
+    API endpoint pour les recommandations d'outfit
+    
+    Args:
+        clothes_json: JSON string contenant la liste des vêtements
+        preference: Style préféré (casual, formal, sport)
+        city: Ville pour la météo
+    
+    Returns:
+        JSON avec l'outfit recommandé
+    """
+    try:
+        clothes_data = json.loads(clothes_json)
+        result = recommend_outfit_ml(clothes_data, preference, city)
+        return json.dumps(result, indent=2)
+    except Exception as e:
+        return json.dumps({"success": False, "error": str(e)})
+
+# Interface Gradio
+iface = gr.Interface(
+    fn=recommend_outfit_api,
+    inputs=[
+        gr.Textbox(
+            label="Clothes Data (JSON)",
+            placeholder='[{"id":"top1","category":"top","style":"casual",...}]',
+            lines=10
+        ),
+        gr.Dropdown(
+            choices=["casual", "formal", "sport", "chic"],
+            label="Preference",
+            value="casual"
+        ),
+        gr.Textbox(label="City", value="Tunis")
+    ],
+    outputs=gr.Textbox(label="Recommended Outfit (JSON)", lines=15),
+    title="🎽 Labasni Outfit Recommender",
+    description="Recommandations d'outfits basées sur ML (TensorFlow + PyTorch)",
+    examples=[
+        ['[{"id":"top1","category":"top","style":"casual","color":"white","season":"summer"}]', "casual", "Tunis"]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()

recommender_model.py

@@ -0,0 +1,103 @@
+"""
+Logique ML pour les recommandations d'outfits
+Adapté de votre fichier recommendations.py
+"""
+
+import numpy as np
+import requests
+from sklearn.metrics.pairwise import cosine_similarity
+import json
+
+# Configuration météo
+OPENWEATHER_API_KEY = "a92f907ace22631f8af40374ae0b30b6"
+
+def get_weather(city: str):
+    """Récupère la météo depuis OpenWeather API"""
+    try:
+        url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid={OPENWEATHER_API_KEY}&units=metric"
+        response = requests.get(url, timeout=5)
+        data = response.json()
+        return {
+            "temperature": data["main"]["temp"],
+            "condition": data["weather"][0]["main"]
+        }
+    except:
+        return {"temperature": 20, "condition": "Clear"}
+
+def get_season_from_weather(temp: float):
+    """Détermine la saison selon la température"""
+    if temp > 25:
+        return "summer"
+    elif temp > 17:
+        return "spring"
+    elif temp > 0:
+        return "fall"
+    return "winter"
+
+def recommend_outfit_ml(clothes_data: list, preference: str, city: str):
+    """
+    Recommande un outfit complet basé sur ML
+    
+    Args:
+        clothes_data: Liste des vêtements disponibles
+        preference: Style préféré
+        city: Ville pour la météo
+    
+    Returns:
+        Dict avec l'outfit recommandé et l'explication
+    """
+    # 1. Obtenir la météo
+    weather = get_weather(city)
+    season = get_season_from_weather(weather["temperature"])
+    
+    # 2. Filtrer par style et saison
+    pref_lower = preference.lower()
+    
+    def matches_season(item_season, target_season):
+        if not item_season or item_season.lower() in ["all", "toutes"]:
+            return True
+        return item_season.lower() == target_season
+    
+    tops = [c for c in clothes_data if c.get("category") == "top" and c.get("style") == pref_lower and matches_season(c.get("season"), season)]
+    bottoms = [c for c in clothes_data if c.get("category") == "bottom" and c.get("style") == pref_lower and matches_season(c.get("season"), season)]
+    footwear = [c for c in clothes_data if c.get("category") in ["footwear", "shoes"] and c.get("style") == pref_lower and matches_season(c.get("season"), season)]
+    
+    # 3. Vérifier si on a assez de vêtements
+    if not tops or not bottoms or not footwear:
+        return {
+            "success": False,
+            "message": f"Pas assez de vêtements '{preference}' pour la saison '{season}'",
+            "weather": weather,
+            "season": season
+        }
+    
+    # 4. Sélectionner le meilleur outfit (basé sur les scores)
+    top = max(tops, key=lambda x: x.get("score", 0))
+    bottom = max(bottoms, key=lambda x: x.get("score", 0))
+    shoe = max(footwear, key=lambda x: x.get("score", 0))
+    
+    return {
+        "success": True,
+        "outfit": {
+            "top": top["id"],
+            "bottom": bottom["id"],
+            "footwear": shoe["id"]
+        },
+        "explanation": {
+            "top": {
+                "reason": f"Best rated (Score: {top.get('score', 0):.2f})",
+                "score": top.get("score", 0)
+            },
+            "bottom": {
+                "reason": f"Best match (Score: {bottom.get('score', 0):.2f})",
+                "score": bottom.get("score", 0)
+            },
+            "footwear": {
+                "reason": f"Best match (Score: {shoe.get('score', 0):.2f})",
+                "score": shoe.get("score", 0)
+            }
+        },
+        "weather": weather,
+        "season": season,
+        "preference": preference
+    }

requirements.txt

@@ -0,0 +1,4 @@
+gradio==4.44.1
+numpy==1.24.3
+scikit-learn==1.3.0
+requests==2.31.0