Update app.py

app.py

@@ -96,26 +96,6 @@ 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")
-#input_component = gr.components.Image(type="pil", label="Input Image")
-input_component = gr.File(label="Input DICOM Data")
-output_component = gr.components.Image(type="numpy", label="Output Image")
-
-# Create Gradio interface
-interface = gr.Interface(
-    fn=show_preds_image,
-    inputs=input_component,
-    outputs=output_component,
-    title="Lung Nodule Detection",
-    examples=['samples/81_80.dcm','samples/110_109.dcm','samples/189_188.dcm'],
-    description=' "This online deployment proves the effectiveness and efficient function of the machine learning model in identifying lung cancer nodules. The implementation of YOLO for core detection tasks is employed that is an efficient and accurate algorithm for object detection. Through the precise hyper-parameter tuning process, the model proposed in this paper has given an impressive boost in the performance. Moreover, the model uses Retinanet algorithm which is recognized as the powerful tool effective in dense object detection. In an attempt to enhance the model’s performance, the backbone of this architecture consists of a Feature Pyramid Network (FPN). The FPN plays an important role in boosting the model’s capacity in recognizing objects in different scales through the construction of high semantic feature map in different resolutions. In conclusion, this deployment encompasses YOLOv5, hyperparameter optimization, Retinanet, and FPN as one of the most effective and modern solutions for the detection of lung cancer nodules." ~ Basil Shaji 😇',
-    live=False,
-)
-
-interface.launch()
 
 def read_and_preprocess_dicom(file_path: str):
     """
@@ -159,6 +139,25 @@ def read_and_preprocess_dicom(file_path: str):
 
     return image, df_metadata.to_pandas()  # Convert to pandas DataFrame for Gradio compatibility
 
+# Define Gradio components
+#input_component = gr.components.Image(type="filepath", label="Input Image")
+#input_component = gr.components.Image(type="pil", label="Input Image")
+input_component = gr.File(label="Input DICOM Data")
+output_component = gr.components.Image(type="numpy", label="Output Image")
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=show_preds_image,
+    inputs=input_component,
+    outputs=output_component,
+    title="Lung Nodule Detection",
+    examples=['samples/81_80.dcm','samples/110_109.dcm','samples/189_188.dcm'],
+    description=' "This online deployment proves the effectiveness and efficient function of the machine learning model in identifying lung cancer nodules. The implementation of YOLO for core detection tasks is employed that is an efficient and accurate algorithm for object detection. Through the precise hyper-parameter tuning process, the model proposed in this paper has given an impressive boost in the performance. Moreover, the model uses Retinanet algorithm which is recognized as the powerful tool effective in dense object detection. In an attempt to enhance the model’s performance, the backbone of this architecture consists of a Feature Pyramid Network (FPN). The FPN plays an important role in boosting the model’s capacity in recognizing objects in different scales through the construction of high semantic feature map in different resolutions. In conclusion, this deployment encompasses YOLOv5, hyperparameter optimization, Retinanet, and FPN as one of the most effective and modern solutions for the detection of lung cancer nodules." ~ Basil Shaji 😇',
+    live=False,
+)
+
+interface.launch()
+
 '''
 def build_interface():
     """