Update app.py

app.py

@@ -18,7 +18,7 @@ from yolov5.models.experimental import attempt_load
 from yolov5.utils.general import non_max_suppression
 from yolov5.utils.augmentations import letterbox
 
-
+'''
 # Example URLs for downloading images
 file_urls = [
     "https://www.dropbox.com/scl/fi/n3bs5xnl2kanqmwv483k3/1_jpg.rf.4a59a63d0a7339d280dd18ef3c2e675a.jpg?rlkey=4n9dnls1byb4wm54ycxzx3ovi&st=ue5xv8yx&dl=0",
@@ -40,7 +40,7 @@ def download_file(url, save_name):
 # Download images
 for i, url in enumerate(file_urls):
     download_file(url, f"image_{i}.jpg")
-
+'''
 
 # Load YOLOv5 model (placeholder)
 model_path = "best.pt"  # Path to your YOLOv5 model
@@ -113,7 +113,7 @@ def show_preds_image(filepath):
     img0 = cv2.imread(filepath)
     img_with_boxes = draw_bounding_boxes(img0, results)
     return cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB)
-
+'''
 # Define Gradio components
 input_component = gr.components.Image(type="filepath", label="Input Image")
 output_component = gr.components.Image(type="numpy", label="Output Image")
@@ -163,6 +163,8 @@ def read_and_preprocess_dicom(file_path: str):
             np.uint8)
     image_pil = Image.fromarray(pixel_array)
     #convert to cv2 format
+    image_pil = image_pil.reshape((image_pil.shape[0], image_pil.shape[1], 1))
+    print("In preprocess dicom:", image_pil.shape)
     image = np.array(image_pil)[::-1].copy()
     
     # Collect metadata in dictionary format and convert to DataFrame