Update app.py

app.py

@@ -14,59 +14,44 @@ from sklearn.metrics import classification_report, confusion_matrix
 import torchvision
 
 
-# Paths
+
 zip_path = "Fruits_dataset.zip"
-dataset_root = "./Fruits_dataset"
-dataset_folder = "./Fruits_dataset/Data"
+extract_root = "./unzipped"
+dataset_folder = None
 
-# Extract dataset if not already extracted
-if not os.path.exists(dataset_folder):
+# Extract ZIP
+if not os.path.exists(extract_root):
     print("Extracting dataset from uploaded zip...")
     with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-        zip_ref.extractall(dataset_root)
+        zip_ref.extractall(extract_root)
 else:
     print("Dataset already extracted.")
 
-# 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(dataset_folder))
-
-# 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)
-            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})")
+# Try to auto-detect the actual dataset folder
+for root, dirs, files in os.walk(extract_root):
+    if any(os.path.isdir(os.path.join(root, d)) for d in dirs):
+        # Pick folder that has class folders inside
+        if all(os.path.isdir(os.path.join(root, d)) for d in dirs):
+            dataset_folder = root
+            break
 
+if dataset_folder is None:
+    raise RuntimeError("Could not find dataset folder with class directories.")
 
+print(f"Detected dataset folder: {dataset_folder}")
+print("Classes:", os.listdir(dataset_folder))
 
-# Preview image dimensions
-image_shapes = []
+# Show image 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)
+    cls_path = os.path.join(dataset_folder, cls)
+    if os.path.isdir(cls_path):
+        for img_file in os.listdir(cls_path):
+            img_path = os.path.join(cls_path, img_file)
             try:
                 img = Image.open(img_path)
-                image_shapes.append(img.size)
-            except:
-                continue
+                print(f"{img_path}: {img.size}")
+            except Exception as e:
+                print(f"Failed to open {img_path}: {e}")
 
 # -------------------------------
 # 2. Load Data