Fix: Update Gradio 6.x API endpoint configuration

app.py

@@ -27,50 +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)})
 
-# ✅ Interface Gradio avec Blocks pour meilleur contrôle de l'API
-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
-            )
-            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("Recommend", variant="primary")
-        
-        with gr.Column():
-            output = gr.Textbox(label="Recommended Outfit (JSON)", lines=15)
-    
-    # ✅ Exemples
-    gr.Examples(
-        examples=[[
+# ✅ Interface Gradio - gr.Interface est plus fiable pour l'API
+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"
-        ]],
-        inputs=[clothes_input, preference_input, city_input]
-    )
-    
-    # ✅ Exposer l'API avec api_name explicite
-    submit_btn.click(
-        fn=recommend_outfit_api,
-        inputs=[clothes_input, preference_input, city_input],
-        outputs=output,
-        api_name="predict"  # ✅ CRUCIAL : Expose l'endpoint /api/predict
-    )
+        ]
+    ],
+    # ✅ Pour Gradio 6.x, l'endpoint standard est /run/predict (pas besoin d'api_name)
+    # L'API est automatiquement exposée via /run/predict
+)
 
-if __name__ == "__main__":
-    demo.launch(
+if _name_ == "_main_":
+    iface.launch(
         server_name="0.0.0.0",
         server_port=7860,
         share=False

requirements.txt

@@ -1,4 +1,5 @@
 gradio==6.1.0
 numpy==1.24.3
 scikit-learn==1.3.0
-requests==2.31.0
+requests==2.31.0
+pydantic==2.10.6