Changing input of prediction function & move function to after enhancement.

app.py

@@ -52,27 +52,14 @@ def load_model():
     )
     return model
 
-def preprocess_image(image):
-    """ Preprocess the image to the required size and normalization. """
-    image = cv2.resize(image, (W, H))
-    image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
-    image = np.expand_dims(image, axis=0).astype(np.float32)
-    return image
+# def preprocess_image(image):
+#     """ Preprocess the image to the required size and normalization. """
+#     image = cv2.resize(image, (W, H))
+#     image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
+#     image = np.expand_dims(image, axis=0).astype(np.float32)
+#     return image
 
-def predict(model, image):
-    """ Predict bounding box and label for the input image. """
-    pred_bbox, pred_class = model.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
 
-def draw_bbox(image, bbox):
-    """ Draw bounding box on the image. """
-    h, w, _ = image.shape
-    x1, y1, x2, y2 = bbox
-    x1, y1, x2, y2 = int(x1 * w), int(y1 * h), int(x2 * w), int(y2 * h)
-    image = cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
-    return image
 
 # st.title("Chest X-ray Disease Detection")
 
@@ -352,6 +339,28 @@ def redirect_button(url):
     if button:
         st.markdown(f'<meta http-equiv="refresh" content="0;url={url}" />', unsafe_allow_html=True)
 
+
+###########################################################################################
+########################### Bounding Box Function ###########################################
+###########################################################################################
+
+
+def predict(model, image):
+    """ Predict bounding box and label for the input image. """
+    pred_bbox, pred_class = model.predict(enhanced_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
+
+def draw_bbox(image, bbox):
+    """ Draw bounding box on the image. """
+    h, w, _ = image.shape
+    x1, y1, x2, y2 = bbox
+    x1, y1, x2, y2 = int(x1 * w), int(y1 * h), int(x2 * w), int(y2 * h)
+    image = cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
+    return image
+
+
 ###########################################################################################
 ########################### Streamlit Interface ###########################################
 ###########################################################################################