fix: Use gr.Interface for REST API exposure

app.py

@@ -1,6 +1,6 @@
 """
 Labasni Recommender Service - Hugging Face Space
-✅ Compatible Gradio 6.x avec API externe accessible
+✅ Compatible Gradio 6.x avec API REST accessible via /api/predict
 """
 
 import gradio as gr
@@ -47,61 +47,46 @@ def recommend_outfit_api(clothes_json: str, preference: str, city: str = "Tunis"
             "message": "Erreur lors de la recommandation"
         }, indent=2)
 
-# ✅ CORRECTION GRADIO 6.x : Utiliser gr.Blocks au lieu de gr.Interface
-with gr.Blocks(title="🎽 Labasni Outfit Recommender") as demo:
-    gr.Markdown("""
-    # 🎽 Labasni Outfit Recommender
-    Recommandations d'outfits basées sur Machine Learning
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            clothes_input = gr.Textbox(
-                label="Clothes Data (JSON)",
-                placeholder='[{"id":"top1","category":"top","style":"casual","color":"white","season":"summer","score":0.5}]',
-                lines=10,
-                value='[{"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}]'
-            )
-            
-            preference_input = gr.Dropdown(
-                choices=["casual", "formal", "sport", "chic", "elegant"],
-                label="Preference",
-                value="casual"
-            )
-            
-            city_input = gr.Textbox(
-                label="City",
-                value="Tunis"
-            )
-            
-            submit_btn = gr.Button("🎯 Get Recommendation", variant="primary")
-        
-        with gr.Column():
-            output = gr.Textbox(
-                label="Recommended Outfit (JSON)",
-                lines=15
-            )
-    
-    # ✅ Lier le bouton à la fonction
-    submit_btn.click(
-        fn=recommend_outfit_api,
-        inputs=[clothes_input, preference_input, city_input],
-        outputs=output
-    )
-    
-    gr.Markdown("""
-    ### 📋 Exemple de données
-    ```json
-    [
-      {"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}
-    ]
-    ```
-    """)
+# ✅ SOLUTION : Utiliser gr.Interface qui expose automatiquement /api/predict
+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":0.5}]',
+            lines=10,
+            value='[{"id":"top1","category":"top","style":"casual","color":"white","season":"all","score":0.8},{"id":"bottom1","category":"bottom","style":"casual","color":"blue","season":"all","score":0.7},{"id":"shoe1","category":"footwear","style":"casual","color":"black","season":"all","score":0.9}]'
+        ),
+        gr.Dropdown(
+            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":"all","score":0.8},{"id":"bottom1","category":"bottom","style":"casual","color":"blue","season":"all","score":0.7},{"id":"shoe1","category":"footwear","style":"casual","color":"black","season":"all","score":0.9}]',
+            "casual",
+            "Tunis"
+        ]
+    ],
+    # ✅ CRUCIAL : Ces paramètres exposent l'API
+    api_name="predict",
+    allow_flagging="never"
+)
 
-# ✅ CRUCIAL : Lancer avec l'API activée
 if __name__ == "__main__":
+    # ✅ Lancer avec l'API activée
     demo.launch(
         server_name="0.0.0.0",
         server_port=7860,