fix: Update to Gradio 5.x compatible API

app.py

@@ -1,7 +1,6 @@
 """
 Labasni Recommender Service - Hugging Face Space
-Interface Gradio pour les recommandations d'outfits
-Version simplifiée avec gr.Interface
+✅ Compatible Gradio 5.x avec API externe accessible
 """
 
 import gradio as gr
@@ -25,104 +24,52 @@ def recommend_outfit_api(clothes_json: str, preference: str, city: str = "Tunis"
         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)})
+        return json.dumps({
+            "success": False,
+            "error": str(e),
+            "message": "Erreur lors de la recommandation"
+        }, indent=2)
 
-# ✅ Interface Gradio - gr.Interface avec api_name explicite pour Gradio 6.x
+# ✅ CORRECTION PRINCIPALE : Interface Gradio 5.x avec api_name explicite
 iface = gr.Interface(
     fn=recommend_outfit_api,
     inputs=[
         gr.Textbox(
             label="Clothes Data (JSON)",
-            placeholder='[{"id":"top1","category":"top","style":"casual",...}]',
+            placeholder='[{"id":"top1","category":"top","style":"casual","color":"white","season":"summer","score":0.5}]',
             lines=10
         ),
         gr.Dropdown(
-            choices=["casual", "formal", "sport", "chic"],
+            choices=["casual", "formal", "sport", "chic", "elegant"],
             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","score":0.8,"imageURL":"https://example.com/top.jpg"},{"id":"bot1","category":"bottom","style":"casual","color":"blue","season":"summer","score":0.7,"imageURL":"https://example.com/bottom.jpg"},{"id":"shoe1","category":"footwear","style":"casual","color":"black","season":"summer","score":0.9,"imageURL":"https://example.com/shoes.jpg"}]',
-            "casual",
-            "Tunis"
-        ]
-    ],
-    # ✅ CRUCIAL : api_name est nécessaire pour Gradio 6.x
-    # Cela expose l'endpoint /api/predict
-    api_name="predict"
-)
-
-# ✅ Lancement de l'interface
-# Hugging Face Spaces détecte automatiquement l'interface Gradio
-iface.launch()
-example.com
-Amine
-"""
-Labasni Recommender Service - Hugging Face Space
-Interface Gradio pour les recommandations d'outfits
-Version simplifiée avec gr.Interface
-"""
-
-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 - gr.Interface avec api_name explicite pour Gradio 6.x
-# Hugging Face Spaces détecte automatiquement la variable 'demo'
-demo = 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")
+            label="City",
+            value="Tunis"
+        )
     ],
-    outputs=gr.Textbox(label="Recommended Outfit (JSON)", lines=15),
+    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","score":0.8,"imageURL":"https://example.com/top.jpg"},{"id":"bot1","category":"bottom","style":"casual","color":"blue","season":"summer","score":0.7,"imageURL":"https://example.com/bottom.jpg"},{"id":"shoe1","category":"footwear","style":"casual","color":"black","season":"summer","score":0.9,"imageURL":"https://example.com/shoes.jpg"}]',
+            '[{"id":"top1","category":"top","style":"casual","color":"white","season":"summer","score":0.8},{"id":"bottom1","category":"bottom","style":"casual","color":"blue","season":"summer","score":0.7},{"id":"shoe1","category":"footwear","style":"casual","color":"black","season":"summer","score":0.9}]',
             "casual",
             "Tunis"
         ]
     ],
-    # ✅ CRUCIAL : api_name est nécessaire pour Gradio 6.x
-    # Cela expose l'endpoint /api/predict
-    api_name="predict"
+    # ✅ CRUCIAL : Activer l'API externe
+    api_name="recommend_outfit"  # Nom explicite pour l'endpoint
 )
 
-# ✅ Pour Hugging Face Spaces, l'interface est automatiquement détectée et lancée
-# La variable 'demo' est la convention standard pour Hugging Face Spaces
+if __name__ == "__main__":
+    # ✅ Lancer avec accès API activé
+    iface.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )