Update app.py

app.py

@@ -13,13 +13,12 @@ import zipfile
 from PIL import Image
 import gradio as gr
 
-# Step 1: Setup Kaggle API
-# Ensure the .kaggle directory exists
+# Setup Kaggle API
 kaggle_dir = os.path.expanduser("~/.kaggle")
 if not os.path.exists(kaggle_dir):
     os.makedirs(kaggle_dir)
 
-# Step 2: Copy the kaggle.json file to the ~/.kaggle directory
+Copy the kaggle.json file to the ~/.kaggle directory
 kaggle_json_path = "kaggle.json"
 kaggle_dest_path = os.path.join(kaggle_dir, "kaggle.json")
 
@@ -30,15 +29,14 @@ if not os.path.exists(kaggle_dest_path):
 else:
     print("Kaggle API key already exists.")
     
-# Step 3: Download the dataset from Kaggle using Kaggle CLI
+Download the dataset from Kaggle using Kaggle CLI
 dataset_name = "mostafaabla/garbage-classification"
 print(f"Downloading the dataset: {dataset_name}")
 download_command = f"kaggle datasets download -d {dataset_name}"
 
 # Run the download command
 subprocess.run(download_command, shell=True)
-
-# Step 4: Unzip the downloaded dataset
+Unzip the downloaded dataset
 dataset_zip = "garbage-classification.zip"
 extracted_folder = "./garbage-classification"
 
@@ -54,12 +52,12 @@ else:
     print(f"Dataset zip file '{dataset_zip}' not found.")
 
 
-
+# Model training and testing in separate directory at ipynb file (Copy of ai-portfolio Kendrick.ipynb)
 
 from PIL import Image
 import gradio as gr
 
-# Load your model
+# Load model
 def load_model():
     model = models.resnet50(weights='DEFAULT')  # Using default weights for initialization
     num_ftrs = model.fc.in_features
@@ -116,7 +114,7 @@ def predict(image):
     bin_color = bin_colors[class_name]  # Get the corresponding bin color
     return class_name, bin_color  # Return both class name and bin color
 
-# Buat antarmuka Gradio dengan deskripsi
+# Make Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy", label="Unggah Gambar"),