Update app.py

app.py

@@ -12,7 +12,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"Using device: {device}")
 
 # -----------------------------
-# Model definition (MATCHES TRAINING)
+# Model definition
 # -----------------------------
 class FoodIngredientClassifier(nn.Module):
     def __init__(self, num_classes):
@@ -31,7 +31,7 @@ class FoodIngredientClassifier(nn.Module):
         return self.backbone(x)
 
 # -----------------------------
-# Load checkpoint (PyTorch 2.6+ safe)
+# Load checkpoint
 # -----------------------------
 checkpoint = torch.load(
     "best_model.pth",
@@ -69,7 +69,7 @@ def clean_name(name):
     return name.replace("_", " ").title()
 
 # -----------------------------
-# Prediction function (BULLETPROOF)
+# Prediction function
 # -----------------------------
 def predict(image, threshold):
     if image is None:
@@ -85,14 +85,14 @@ def predict(image, threshold):
         logits = model(input_tensor)
         probs = torch.sigmoid(logits).cpu().numpy()[0]
 
-    # Threshold-based predictions
+    # Threshold-based results
     results = {
         clean_name(class_names[i]): float(probs[i])
         for i in range(len(probs))
         if probs[i] >= threshold
     }
 
-    # 🔒 Fallback: always return top 5
+    # Fallback: always return top 5
     if not results:
         top_idx = np.argsort(probs)[-5:][::-1]
         results = {
@@ -100,13 +100,10 @@ def predict(image, threshold):
             for i in top_idx
         }
 
-    # Sort by confidence
-    results = dict(sorted(results.items(), key=lambda x: x[1], reverse=True))
-
-    return results
+    return dict(sorted(results.items(), key=lambda x: x[1], reverse=True))
 
 # -----------------------------
-# Gradio UI
+# Gradio Interface (NO deprecated args)
 # -----------------------------
 iface = gr.Interface(
     fn=predict,
@@ -122,13 +119,11 @@ iface = gr.Interface(
     ],
     outputs=gr.JSON(label="Detected Ingredients"),
     title="Food Ingredient Detection (Multi-Label)",
-    description=(
-        "Upload a food image to detect **multiple ingredients at once**.\n\n"
-        "This is a **multi-label ResNet-50 model** using sigmoid outputs."
-    ),
-    theme=gr.themes.Soft(),
-    allow_flagging="never"
+    description="Upload a food image to detect multiple ingredients using a ResNet-50 model."
 )
 
+# -----------------------------
+# Launch (Gradio 6.x style)
+# -----------------------------
 if __name__ == "__main__":
-    iface.launch(enable_queue=True)
+    iface.launch(theme=gr.themes.Soft())