Update app.py

app.py

@@ -13,12 +13,10 @@ import cv2
 st.title("🧠 Stroke Patient Pain Intensity Detector")
 st.markdown("Upload a full-face image. The system will detect the affected side and use the other side to predict pain intensity.")
 
-load_model = tf.keras.models.load_model
-
 @st.cache_resource
 def download_models():
     model_urls = {
-        "cnn_stroke_model.keras": "https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/cnn_stroke_model.keras",
+        "cnn_stroke_model.h5": "https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/cnn_stroke_model.h5",
         "left_side_pain_classifier.pth": "https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/left_side_pain_classifier.pth",
         "right_side_pain_classifier.pth": "https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/right_side_pain_classifier.pth",
     }
@@ -42,23 +40,23 @@ def download_models():
             f.write(r.content)
 
     # Load stroke model
-    stroke_model = load_model("cnn_stroke_model.keras")
+    stroke_model = tf.keras.models.load_model("cnn_stroke_model.h5")
 
-    # Load pain models with correct architecture
+    # Define corrected PainModel class
     class PainModel(nn.Module):
         def __init__(self):
             super(PainModel, self).__init__()
             from torchvision.models import resnet18, ResNet18_Weights
-            self.model = resnet18(weights=ResNet18_Weights.DEFAULT)
-            self.model.fc = nn.Linear(self.model.fc.in_features, 1)
+            self.convnet = resnet18(weights=ResNet18_Weights.DEFAULT)
+            self.convnet.fc = nn.Linear(self.convnet.fc.in_features, 1)
 
         def forward(self, x):
-            return self.model(x)
+            return self.convnet(x)
 
     left_model = PainModel()
     right_model = PainModel()
-    left_model.load_state_dict(torch.load("left_side_pain_classifier.pth", map_location=torch.device("cpu")))
-    right_model.load_state_dict(torch.load("right_side_pain_classifier.pth", map_location=torch.device("cpu")))
+    left_model.load_state_dict(torch.load("left_side_pain_classifier.pth", map_location="cpu"))
+    right_model.load_state_dict(torch.load("right_side_pain_classifier.pth", map_location="cpu"))
     left_model.eval()
     right_model.eval()
 
@@ -94,8 +92,7 @@ if uploaded_file is not None:
     left_half = full_image.crop((0, 0, mid, h))
     right_half = full_image.crop((mid, 0, w, h))
 
-    _, H, W, C = stroke_model.input_shape
-    stroke_input = full_image.resize((W, H))
+    stroke_input = full_image.resize((224, 224))
     stroke_array = np.array(stroke_input).astype("float32") / 255.0
     stroke_array = np.expand_dims(stroke_array, axis=0)