Fix: Update Gradio 6.x API endpoint configuration

app.py

@@ -1,7 +1,7 @@
 """
 Labasni Recommender Service - Hugging Face Space
 Interface Gradio pour les recommandations d'outfits
-Compatible avec Gradio 6.x
+Version simplifiée avec gr.Interface
 """
 
 import gradio as gr
@@ -27,63 +27,38 @@ def recommend_outfit_api(clothes_json: str, preference: str, city: str = "Tunis"
     except Exception as e:
         return json.dumps({"success": False, "error": str(e)})
 
-# ✅ CORRECTION : Configuration pour Gradio 6.x
-# Utiliser gr.Blocks au lieu de gr.Interface pour plus de contrôle
-with gr.Blocks(title="🎽 Labasni Outfit Recommender") as demo:
-    gr.Markdown("# 🎽 Labasni Outfit Recommender")
-    gr.Markdown("Recommandations d'outfits basées sur ML (TensorFlow + PyTorch)")
-    
-    with gr.Row():
-        with gr.Column():
-            clothes_input = gr.Textbox(
-                label="Clothes Data (JSON)",
-                placeholder='[{"id":"top1","category":"top","style":"casual",...}]',
-                lines=10,
-                value='[{"id":"top1","category":"top","style":"casual","color":"white","season":"summer","score":0.8,"imageURL":"https://example.com/top.jpg"}]'
-            )
-            
-            preference_input = gr.Dropdown(
-                choices=["casual", "formal", "sport", "chic"],
-                label="Preference",
-                value="casual"
-            )
-            
-            city_input = gr.Textbox(
-                label="City",
-                value="Tunis"
-            )
-            
-            submit_btn = gr.Button("🚀 Générer Recommandation", variant="primary")
-        
-        with gr.Column():
-            output = gr.Textbox(
-                label="Recommended Outfit (JSON)",
-                lines=15,
-                show_copy_button=True
-            )
-    
-    # Event handler
-    submit_btn.click(
-        fn=recommend_outfit_api,
-        inputs=[clothes_input, preference_input, city_input],
-        outputs=output
-    )
-    
-    # Exemples
-    gr.Examples(
-        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"
-            ]
-        ],
-        inputs=[clothes_input, preference_input, city_input]
-    )
+# ✅ Interface Gradio 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",...}]',
+            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","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 : Définir api_name pour exposer l'endpoint
+    api_name="predict"
+)
 
-# ✅ IMPORTANT : api_name="predict" expose l'endpoint pour l'API externe
 if __name__ == "__main__":
-    demo.launch(
+    iface.launch(
         server_name="0.0.0.0",
         server_port=7860,
         share=False