Fix: Update Gradio 6.x API endpoint configuration

app.py

@@ -1,7 +1,6 @@
 """
 Labasni Recommender Service - Hugging Face Space
 Interface Gradio pour les recommandations d'outfits
-Version corrigée avec api_name="predict"
 """
 
 import gradio as gr
@@ -21,114 +20,47 @@ def recommend_outfit_api(clothes_json: str, preference: str, city: str = "Tunis"
         JSON avec l'outfit recommandé
     """
     try:
-        # Log pour debug
-        print(f"📥 Received request: preference={preference}, city={city}")
-        print(f"📦 Clothes data length: {len(clothes_json)}")
-        
-        # Parser le JSON des vêtements
+        # Parser le JSON
         clothes_data = json.loads(clothes_json)
-        print(f"✅ Parsed {len(clothes_data)} clothes items")
         
         # Appeler le modèle ML
         result = recommend_outfit_ml(clothes_data, preference, city)
-        print(f"✅ Model returned: success={result.get('success')}")
         
-        # Retourner le résultat en JSON formaté
+        # Retourner le résultat en JSON
         return json.dumps(result, indent=2)
-        
-    except json.JSONDecodeError as e:
-        error_msg = f"Invalid JSON format: {str(e)}"
-        print(f"❌ {error_msg}")
-        return json.dumps({"success": False, "error": error_msg})
-        
     except Exception as e:
-        error_msg = f"Error during recommendation: {str(e)}"
-        print(f"❌ {error_msg}")
-        return json.dumps({"success": False, "error": error_msg})
+        # Retourner l'erreur en JSON
+        return json.dumps({"success": False, "error": str(e)})
 
-# ✅ Interface Gradio avec api_name explicite
-# IMPORTANT: La variable DOIT s'appeler 'demo' pour Hugging Face Spaces
+# Interface Gradio avec api_name explicite
+# La variable DOIT s'appeler 'demo' pour Hugging Face Spaces
 demo = gr.Interface(
     fn=recommend_outfit_api,
     inputs=[
         gr.Textbox(
             label="Clothes Data (JSON)",
-            placeholder='[{"id":"top1","category":"top","style":"casual","color":"white","season":"summer","score":0.8,"imageURL":"https://example.com/top.jpg"}]',
-            lines=10,
-            info="Liste des vêtements au format JSON"
+            placeholder='[{"id":"top1","category":"top","style":"casual",...}]',
+            lines=10
         ),
         gr.Dropdown(
             choices=["casual", "formal", "sport", "chic"],
             label="Preference",
-            value="casual",
-            info="Style d'outfit souhaité"
+            value="casual"
         ),
-        gr.Textbox(
-            label="City",
-            value="Tunis",
-            info="Ville pour la météo"
-        )
+        gr.Textbox(label="City", value="Tunis")
     ],
-    outputs=gr.Textbox(
-        label="Recommended Outfit (JSON)",
-        lines=15,
-        show_copy_button=True
-    ),
+    outputs=gr.Textbox(label="Recommended Outfit (JSON)", lines=15),  # Removed show_copy_button
     title="🎽 Labasni Outfit Recommender",
-    description="""
-    Recommandations d'outfits basées sur Machine Learning.
-    
-    **Fonctionnalités:**
-    - 🤖 Algorithme ML intelligent (ResNet50 + Scikit-learn)
-    - 🌤️ Prise en compte de la météo réelle
-    - 🎨 Compatibilité des couleurs et styles
-    - 📊 Scoring basé sur l'historique
-    
-    **Usage API:**
-    ```bash
-    curl -X POST https://salma-mahjoub-styleto-recommender.hf.space/api/predict \\
-      -H "Content-Type: application/json" \\
-      -d '{"data": [CLOTHES_JSON, "casual", "Tunis"]}'
-    ```
-    """,
+    description="Recommandations d'outfits basées sur ML (TensorFlow + PyTorch)",
     examples=[
         [
-            # Exemple 1: Outfit casual d'été
-            '[{"id":"top1","category":"top","style":"casual","color":"white","season":"summer","score":0.8,"imageURL":"https://example.com/top.jpg"},{"id":"bottom1","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,"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"
-        ],
-        [
-            # Exemple 2: Outfit formel d'hiver
-            '[{"id":"shirt1","category":"top","style":"formal","color":"white","season":"winter","score":0.9,"imageURL":"https://example.com/shirt.jpg"},{"id":"pants1","category":"bottom","style":"formal","color":"black","season":"winter","score":0.8,"imageURL":"https://example.com/pants.jpg"},{"id":"dress_shoes","category":"footwear","style":"formal","color":"black","season":"winter","score":0.95,"imageURL":"https://example.com/dress_shoes.jpg"}]',
-            "formal",
-            "Paris"
-        ],
-        [
-            # Exemple 3: Outfit sport
-            '[{"id":"tshirt1","category":"top","style":"sport","color":"red","season":"all","score":0.75,"imageURL":"https://example.com/tshirt.jpg"},{"id":"shorts1","category":"bottom","style":"sport","color":"black","season":"summer","score":0.8,"imageURL":"https://example.com/shorts.jpg"},{"id":"sneakers1","category":"footwear","style":"sport","color":"white","season":"all","score":0.9,"imageURL":"https://example.com/sneakers.jpg"}]',
-            "sport",
-            "New York"
         ]
     ],
-    # ✅ CRUCIAL: api_name="predict" expose l'endpoint /api/predict
-    api_name="predict",
-    
-    # Options supplémentaires
-    allow_flagging="never",  # Désactiver le flagging
-    theme=gr.themes.Soft(),  # Theme moderne
+    api_name="predict"  # CRUCIAL pour exposer l'endpoint /api/predict
 )
 
-# ✅ Pour Hugging Face Spaces:
-# - L'interface est automatiquement détectée via la variable 'demo'
-# - Pas besoin d'appeler demo.launch()
-# - Le Space démarre automatiquement
-
-# Pour le développement local (optionnel):
-if __name__ == "__main__":
-    # Lancement local pour tester
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False
-    )
+# Pour Hugging Face Spaces, l'interface est automatiquement détectée et lancée
+# Pas besoin d'appeler demo.launch()