Update app.py

app.py

@@ -205,18 +205,6 @@ def handle_user_choice(choice, percentage_idx=None, uploaded_file=None):
     else:
         return "Invalid choice", "Invalid choice", ""  # Return empty string for console output
 
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 # Custom class to capture print output
 class PrintCapture(io.StringIO):
     def __init__(self):
@@ -249,15 +237,6 @@ def display_predefined_images(percentage_idx):
 
     return raw_image, embeddings_image
 
-# Updated los_nlos_classification to handle missing outputs properly
-#def los_nlos_classification(file, percentage_idx):
-#    if file is not None:
-#        raw_cm_image, emb_cm_image, console_output = process_hdf5_file(file, percentage_idx)
-#        return raw_cm_image, emb_cm_image, console_output
-#    else:
-#        raw_image, embeddings_image = display_predefined_images(percentage_idx)
-#        return raw_image, embeddings_image, "No file uploaded. Displaying predefined images."
-
 def los_nlos_classification(file, percentage_idx):
     if file is not None:
         raw_cm_image, emb_cm_image, console_output = process_hdf5_file(file, percentage_idx)
@@ -271,13 +250,6 @@ def create_random_image(size=(300, 300)):
     random_image = np.random.rand(*size, 3) * 255
     return Image.fromarray(random_image.astype('uint8'))
 
-# Function to load the pre-trained model from your cloned repository
-def load_custom_model():
-    from lwm_model import LWM  # Assuming the model is defined in lwm_model.py
-    model = LWM()  # Modify this according to your model initialization
-    model.eval()
-    return model
-
 import importlib.util
 
 # Function to dynamically load a Python module from a given file path
@@ -350,7 +322,6 @@ def plot_confusion_matrix(y_true, y_pred, title):
     plt.savefig(f"{title}.png")
     return Image.open(f"{title}.png")
 
-
 def identical_train_test_split(output_emb, output_raw, labels, percentage_idx):
     N = output_emb.shape[0]  # Get the total number of samples
     
@@ -502,7 +473,7 @@ with gr.Blocks(css="""
         choice_radio = gr.Radio(choices=["Use Default Dataset", "Upload Dataset"], label="Choose how to proceed", value="Use Default Dataset")
         
         # Dropdown for selecting percentage for predefined data
-        percentage_dropdown_los = gr.Dropdown(choices=list(range(20)), value=0, label="Percentage of Data for Training")
+        percentage_dropdown_los = gr.Dropdown(choices=[f"{value:.3f}" for value in percentage_values_los], value=f"{percentage_values_los[0]:.3f}", label="Percentage of Data for Training")
 
         # File uploader for dataset (only visible if user chooses to upload a dataset)
         file_input = gr.File(label="Upload HDF5 Dataset", file_types=[".h5"], visible=False)