Change model to mode_detection.

app.py

@@ -37,7 +37,7 @@ H = 224
 W = 224
 
 @st.cache_resource
-def load_model():
+def load_model_detection():
     model = tf.keras.models.load_model("model-detection.h5", compile=False)
     model.compile(
         loss={
@@ -65,7 +65,7 @@ def load_model():
 
 # st.write("Upload a chest X-ray image and click on 'Detect' to find out if there's any disease.")
 
-model = load_model()
+model_detection = load_model_detection()
 
 # uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
@@ -353,9 +353,9 @@ def redirect_button(url):
 ###########################################################################################
 
 
-def predict(model, image):
+def predict(model_detection, image):
     """ Predict bounding box and label for the input image. """
-    pred_bbox, pred_class = model.predict(image)
+    pred_bbox, pred_class = model_detection.predict(image)
     pred_label_confidence = np.max(pred_class, axis=1)[0]
     pred_label = np.argmax(pred_class, axis=1)[0]
     return pred_bbox[0], pred_label, pred_label_confidence
@@ -472,7 +472,7 @@ if uploaded_file is not None:
             input_image = cv2.resize(enhanced_image, (W, H))  # Resize the enhanced image to the required input size
             input_image = (input_image - 127.5) / 127.5  # Normalize to [-1, +1]
             input_image = np.expand_dims(input_image, axis=0).astype(np.float32)  # Expand dimensions and convert to float32
-            pred_bbox, pred_label, pred_label_confidence = predict(model, input_image)
+            pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image)
         
             # Updated label mapping based on the dataset
             label_mapping = {