Update app.py

app.py

@@ -1,46 +1,46 @@
-import gradio as gr
-from PIL import Image
-import torch
-from pathlib import Path
-
-# ------------------------
-# تحميل الموديل
-# ------------------------
-MODEL_PATH = Path("best.pt")
-model = torch.load(MODEL_PATH, map_location="cpu")
-model.eval()
-
-CLASS_NAMES = ["ax", "co", "sa"]
-
-# ------------------------
-#  prediction
-# ------------------------
-def predict_orientation(image: Image.Image):
-    import torchvision.transforms as transforms
-    transform = transforms.Compose([
-        transforms.Resize((224,224)),  
-        transforms.ToTensor(),
-    ])
-    img_tensor = transform(image).unsqueeze(0)  # batch dimension
-
-    with torch.no_grad():
-        outputs = model(img_tensor)
-        probs = torch.softmax(outputs, dim=1)
-        conf, pred = torch.max(probs, 1)
-        orientation = CLASS_NAMES[pred.item()]
-        confidence = round(conf.item(), 2)
-    
-    return f"Orientation: {orientation} | Confidence: {confidence}"
-
-# ------------------------
-#  Gradio
-# ------------------------
-iface = gr.Interface(
-    fn=predict_orientation,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="MRI Orientation Predictor",
-    description="upload your image and the model output prediction and confidence"
-)
-
-iface.launch()
+import gradio as gr
+from PIL import Image
+import torch
+from pathlib import Path
+
+# ------------------------
+# تحميل الموديل
+# ------------------------
+MODEL_PATH = Path("best.pt")
+model = torch.load(MODEL_PATH, map_location="cpu",weights_only=False)
+model.eval()
+
+CLASS_NAMES = ["ax", "co", "sa"]
+
+# ------------------------
+#  prediction
+# ------------------------
+def predict_orientation(image: Image.Image):
+    import torchvision.transforms as transforms
+    transform = transforms.Compose([
+        transforms.Resize((224,224)),  
+        transforms.ToTensor(),
+    ])
+    img_tensor = transform(image).unsqueeze(0)  # batch dimension
+
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        probs = torch.softmax(outputs, dim=1)
+        conf, pred = torch.max(probs, 1)
+        orientation = CLASS_NAMES[pred.item()]
+        confidence = round(conf.item(), 2)
+    
+    return f"Orientation: {orientation} | Confidence: {confidence}"
+
+# ------------------------
+#  Gradio
+# ------------------------
+iface = gr.Interface(
+    fn=predict_orientation,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="MRI Orientation Predictor",
+    description="upload your image and the model output prediction and confidence"
+)
+
+iface.launch()