disabled wandb

app.py

@@ -9,7 +9,7 @@ import torch.optim as optim
 import torch.nn.functional as F
 import torch.backends.cudnn as cudnn
 import gradio as gr
-import wandb
+#import wandb
 import math
 import numpy as np
 import matplotlib.pyplot as plt
@@ -74,7 +74,7 @@ def class_names(class_num, class_list): # converts the raw number label to text
 
 ### MAIN FUNCTION
 best_acc = 0
-def main(drop_type, epochs_sldr, train_sldr, test_sldr, learning_rate, optimizer, sigma_sldr, adv_attack, username, scheduler):
+def main(drop_type, epochs_sldr, train_sldr, test_sldr, learning_rate, optimizer, sigma_sldr, adv_attack, scheduler):
 
     ## Input protection
     if not drop_type:
@@ -104,9 +104,7 @@ def main(drop_type, epochs_sldr, train_sldr, test_sldr, learning_rate, optimizer
     attack = str(adv_attack)
     scheduler_choose = str(scheduler)
     
-    # REPLACE ENTITY WITH USERNAME BELOW
-    wandb.init(entity=username, project="model-training")
-    wandb.login(key=["a10127d2a41d4e05f8e7a1d3e69d3f719f4657b5"])
+    #wandb.init(entity=username, project="model-training")
     
     parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
     parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
@@ -505,7 +503,6 @@ with gr.Blocks(css=".caption-label {display:none}") as functionApp:
         gr.Markdown("## Parameters")
     with gr.Row():
         inp = gr.Dropdown(choices=names, label="Training Model", value="ResNet18", info="Choose one of 13 common models provided in the dropdown to use for training.")
-        username = gr.Textbox(label="Weights and Biases", info="Enter your username or team name from the Weights and Biases API.")
         epochs_sldr = gr.Slider(label="Number of Epochs", minimum=1, maximum=100, step=1, value=1, info="How many times the model will see the entire dataset during trianing.")
         with gr.Column():
             setting_radio = gr.Radio(["Basic", "Advanced"], label="Settings", value="Basic")
@@ -539,7 +536,7 @@ with gr.Blocks(css=".caption-label {display:none}") as functionApp:
         use_attacks.change(fn=attacks, inputs=use_attacks, outputs=[attack_method, use_sigma, adv_attack])
         use_sigma.change(fn=gaussian, inputs=use_sigma, outputs=[sigma_sldr, gaussian_pics])
         adv_attack.change(fn=adversarial, inputs=adv_attack, outputs=attack_pics)
-        btn.click(fn=main, inputs=[inp, epochs_sldr, train_sldr, test_sldr, learning_rate_sldr, optimizer, sigma_sldr, adv_attack, username, scheduler], outputs=[accuracy, pics, allpics, gaussian_pics, attack_pics])
+        btn.click(fn=main, inputs=[inp, epochs_sldr, train_sldr, test_sldr, learning_rate_sldr, optimizer, sigma_sldr, adv_attack, scheduler], outputs=[accuracy, pics, allpics, gaussian_pics, attack_pics])
 
 ## Documentation app (implemented as second tab)