Update app.py

app.py

@@ -14,41 +14,45 @@ from sklearn.metrics import classification_report, confusion_matrix
 import torchvision
 
 
-# Define dataset paths
+# Paths
 zip_path = "Fruits_dataset.zip"
-dataset_folder = "./Fruits_dataset"
+dataset_root = "./Fruits_dataset"
+dataset_folder = os.path.join(dataset_root, "Data")
 
-# Check and extract dataset
+# Extract dataset if not already extracted
 if not os.path.exists(dataset_folder):
     print("Extracting dataset from uploaded zip...")
     with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-        zip_ref.extractall("./")
+        zip_ref.extractall(dataset_root)
 else:
     print("Dataset already extracted.")
 
-# Normalize folder structure by moving class folders
-for folder in ["apples", "pears", "Pears"]:
-    src = f"./{folder}"
-    dst = f"./Fruits_dataset/{folder.lower()}"
-    if os.path.exists(src) and not os.path.exists(dst):
-        os.makedirs("./Fruits_dataset", exist_ok=True)
-        os.rename(src, dst)
+# Normalize folder structure: make all class folder names lowercase
+for folder in os.listdir(dataset_folder):
+    folder_path = os.path.join(dataset_folder, folder)
+    if os.path.isdir(folder_path):
+        new_name = folder.lower()
+        new_path = os.path.join(dataset_folder, new_name)
+        if folder != new_name and not os.path.exists(new_path):
+            os.rename(folder_path, new_path)
 
 # Confirm class folders
-print("Available classes:", os.listdir("./Fruits_dataset"))
+print("Available classes:", os.listdir(dataset_folder))
 
-# Preview all image sizes
+# Preview image sizes in each class
 print("\nImage sizes:")
 for cls in os.listdir(dataset_folder):
     cls_folder = os.path.join(dataset_folder, cls)
     if os.path.isdir(cls_folder):
         for img_file in os.listdir(cls_folder):
             img_path = os.path.join(cls_folder, img_file)
-            try:
-                img = Image.open(img_path)
-                print(f"{img_path}: {img.size}")
-            except:
-                print(f"Failed to read image: {img_path}")
+            if os.path.isfile(img_path):
+                try:
+                    img = Image.open(img_path)
+                    print(f"{img_path}: {img.size}")
+                except Exception as e:
+                    print(f"Failed to read image: {img_path} ({e})")
+
 
 
 # Preview image dimensions