replace interface with block and remove unused

.dockerignore

@@ -3,5 +3,7 @@
 !weights/
 !utils/
 !examples/
+!figures/
 !main.py
 !requirements.txt
+!DESCRIPTION.md

DESCRIPTION.md

@@ -0,0 +1,17 @@
+## Overview
+
+Breast cancer classification using 2-views (bilateral) that compose a mammography exam, owing to the correlations contained in mammography views, which present crucial information for identifying tumors.
+
+Reference: https://arxiv.org/abs/2204.05798
+
+## Data
+
+The model is trained using CBIS [INBReast Dataset](https://pubmed.ncbi.nlm.nih.gov/22078258/)
+
+- Target: Lesions (masses, calcifications, asymmetries, and distortions)
+- Task: Segmentation
+- Modality: Grayscale
+
+## Demo
+
+Please select the example below or upload 2 pairs of mammography exam result.

main.py

@@ -21,12 +21,6 @@ BILATERAL_MODEL.eval()
 INPUT_HEIGHT, INPUT_WIDTH = 600, 500
 
 SUPPORTED_IMG_EXT = ['.png', '.jpg', '.jpeg']
-INPUT_FILES = [
-    gr.File(file_count='single', file_types=SUPPORTED_IMG_EXT, label='CC View'),
-    gr.File(file_count='single', file_types=SUPPORTED_IMG_EXT, label='MLO View'),
-]
-OUTPUT_GALLERY = gr.Gallery(
-    label='Highlighted Area').style(grid=[2], height='auto')
 EXAMPLE_IMAGES = [
     ['examples/f4b2d377f43ba0bd_left_cc.png',
         'examples/f4b2d377f43ba0bd_left_mlo.jpg'],
@@ -186,15 +180,69 @@ def predict_bilateral(cc_file, mlo_file):
 
 def run():
     """Run Gradio App."""
-    demo = gr.Interface(
-        fn=predict_bilateral,
-        inputs=INPUT_FILES,
-        outputs=[OUTPUT_GALLERY, gr.Label(label='Cancer Type')],
-        examples=EXAMPLE_IMAGES,
-    )
+    with open('DESCRIPTION.md', encoding='utf-8') as f:
+        description = f.read()
+
+    with gr.Blocks() as demo:
+        with gr.Column():
+            gr.Markdown(
+                """
+                <h1 style="text-align: center;">Bilateral View Hypercomplex Breast Classification</h1>
+                """
+            )
+            with gr.Row():
+                gr.Markdown(description)
+                gr.Markdown(
+                    """
+                    ## Model Architecture
+                    <img src="file/figures/physenet.png" width=auto>
+
+                    Parameterized Hypercomplex Shared Encoder network (PHYSEnet).
+                    """
+                )
+        with gr.Row():
+            with gr.Column():
+                cc_file = gr.File(file_count='single',
+                                  file_types=SUPPORTED_IMG_EXT, label='CC View')
+                mlo_file = gr.File(file_count='single',
+                                   file_types=SUPPORTED_IMG_EXT, label='MLO View')
+                with gr.Row():
+                    process_btn = gr.Button('Process')
+                    clear_btn = gr.Button('Clear')
+            with gr.Column():
+                output_gallery = gr.Gallery(
+                    label='Highlighted Area').style(grid=[2], height='auto')
+                cancer_type = gr.Label(label='Cancer Type')
+        gr.Examples(
+            examples=EXAMPLE_IMAGES,
+            inputs=[cc_file, mlo_file],
+        )
+        gr.Markdown('Note that this method is sensitive to input image types.\
+            Current pipeline expect the values between 0.0-255.0')
+
+        process_btn.click(
+            fn=predict_bilateral,
+            inputs=[cc_file, mlo_file],
+            outputs=[output_gallery, cancer_type]
+        )
+
+        clear_btn.click(
+            lambda _: (
+                gr.update(value=None),
+                gr.update(value=None),
+                gr.update(value=None),
+                gr.update(value=None),
+            ),
+            inputs=None,
+            outputs=[
+                cc_file,
+                mlo_file,
+                output_gallery,
+                cancer_type,
+            ],
+        )
 
     demo.launch(server_name='0.0.0.0', server_port=7860)  # nosec B104
-    demo.close()
 
 
 if __name__ == '__main__':