Update app.py

app.py

@@ -16,7 +16,7 @@ RAW_PATH = os.path.join("images", "raw")
 EMBEDDINGS_PATH = os.path.join("images", "embeddings")
 
 # Specific values for percentage of data for training
-percentage_values = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
+percentage_values = np.arange(10) + 1
 
 # Custom class to capture print output
 class PrintCapture(io.StringIO):
@@ -228,6 +228,9 @@ def process_hdf5_file(uploaded_file, percentage_idx):
                                                                                                                              percentage_idx)
         
         # Step 8: Perform classification using the Euclidean distance for both raw and embeddings
+        print(f'train_data_emb: {train_data_emb.shape}')
+        print(f'train_labels: {train_labels.shape}')
+        print(f'test_data_emb: {test_data_emb.shape}')
         pred_raw = classify_based_on_distance(train_data_raw, train_labels, test_data_raw)
         pred_emb = classify_based_on_distance(train_data_emb, train_labels, test_data_emb)
         print(f'pred_emb: {pred_emb}')
@@ -279,7 +282,12 @@ with gr.Blocks(css="""
         with gr.Row():
             with gr.Column(elem_id="slider-container"):
                 gr.Markdown("Percentage of Data for Training")
-                percentage_slider_bp = gr.Slider(minimum=0, maximum=4, step=1, value=0, interactive=True, elem_id="vertical-slider")
+                #percentage_slider_bp = gr.Slider(minimum=0, maximum=4, step=1, value=0, interactive=True, elem_id="vertical-slider")
+                percentage_dropdown_los = gr.Dropdown(choices=percenatge_values*10, 
+                                                      value=10, 
+                                                      label="Percentage of Data for Training", 
+                                                      interactive=True)
+
 
         with gr.Row():
             raw_img_bp = gr.Image(label="Raw Channels", type="pil", width=300, height=300, interactive=False)