Update app.py

app.py

@@ -18,6 +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",
@@ -39,6 +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
@@ -105,7 +107,7 @@ 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")
@@ -129,3 +131,82 @@ interface = gr.Interface(
 )
 
 interface.launch()
+'''
+
+def read_and_preprocess_dicom(file_path: str):
+    """
+    Function to read and preprocess DICOM files
+    :param file_path: Path to the DICOM file
+    :return: Image data (in PIL format) and metadata (in pandas DataFrame format)
+    """
+    try:
+        # Read the DICOM file
+        dicom_data = pydicom.dcmread(file_path)
+    except InvalidDicomError:
+        raise gr.Error("The uploaded file is not a valid DICOM file.")
+
+    # Get the pixel data
+    try:
+        pixel_array = dicom_data.pixel_array
+    except AttributeError:
+        raise gr.Error("The uploaded DICOM file has no pixel data.")
+
+    # Normalize the pixel data to 8-bit and convert to a PIL image
+    if pixel_array.dtype != np.uint8:
+        pixel_array = ((pixel_array - np.min(pixel_array)) / (np.max(pixel_array) - np.min(pixel_array)) * 255).astype(
+            np.uint8)
+    image = Image.fromarray(pixel_array)
+
+    # Collect metadata in dictionary format and convert to DataFrame
+    metadata_dict = {elem.name: str(elem.value) for elem in dicom_data.iterall() if elem.name != 'Pixel Data'}
+    df_metadata = pl.DataFrame({
+        "Key": list(metadata_dict.keys()),
+        "Value": list(metadata_dict.values())
+    })
+
+    return image, df_metadata.to_pandas()  # Convert to pandas DataFrame for Gradio compatibility
+
+
+def build_interface():
+    """
+    Function to build the Gradio interface
+    """
+    theme = gr.themes.Soft(
+        primary_hue=gr.themes.colors.emerald,
+        secondary_hue=gr.themes.colors.emerald
+    )
+
+    with gr.Blocks(title='DICOM Viewer', theme=theme) as demo:
+        gr.Markdown(
+            """
+            # DICOM Viewer
+            This app reads a DICOM file and displays the image and metadata.
+            """
+        )
+        with gr.Column():
+            file_path = gr.File(label="Input DICOM Data")
+
+            with gr.Row():
+                dicom_image = gr.Image(type="pil", label="DICOM Image")
+                dicom_meta = gr.Dataframe(headers=None, label="Metadata")
+
+            inputs = [file_path]
+            outputs = [dicom_image, dicom_meta]
+            file_path.upload(fn=read_and_preprocess_dicom, inputs=inputs, outputs=outputs)
+
+            clear_button = gr.ClearButton(components=inputs + outputs, )
+
+            example = gr.Examples(
+                ['samples/81_80.dcm','samples/110_109.dcm','samples/189_188.dcm'],
+                inputs=inputs,
+                outputs=outputs,
+                fn=read_and_preprocess_dicom,
+                cache_examples=True
+            )
+
+    return demo
+
+
+if __name__ == '__main__':
+    demo = build_interface()
+    demo.launch()