Refactor: Integrate backend API and normalize data

This commit integrates the backend API for fetching and updating report data. It also includes a normalization function to handle data consistency between the API and local storage.

Co-authored-by: anthonymuncher <anthonymuncher@gmail.com>

backend/test/Machine_Learning/oraganize_path.py

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+import os
+import shutil
+import random
+from pathlib import Path
+
+# ---------- CONFIG ----------
+SRC_DIR = Path("dataset_downloads")  # where new images are
+DST_DIR = Path("dataset")            # your main dataset folder
+TRAIN_SPLIT = 0.8                    # 80% train, 20% val
+
+# Classes to process
+NEW_CLASSES = ["broken streetlight", "drainage"]
+
+for cls in NEW_CLASSES:
+    src_class_dir = SRC_DIR / cls
+    if not src_class_dir.exists():
+        print(f"⚠️ Source folder not found: {src_class_dir}")
+        continue
+
+    # Prepare destination folders
+    train_dest = DST_DIR / "train" / cls
+    val_dest = DST_DIR / "val" / cls
+    train_dest.mkdir(parents=True, exist_ok=True)
+    val_dest.mkdir(parents=True, exist_ok=True)
+
+    # List all images
+    images = list(src_class_dir.glob("*.*"))  # jpg, png, jpeg
+    random.shuffle(images)
+
+    # Split
+    split_idx = int(len(images) * TRAIN_SPLIT)
+    train_imgs = images[:split_idx]
+    val_imgs = images[split_idx:]
+
+    # Move images
+    for img in train_imgs:
+        shutil.move(str(img), train_dest / img.name)
+    for img in val_imgs:
+        shutil.move(str(img), val_dest / img.name)
+
+    print(f"✅ Class '{cls}' added: {len(train_imgs)} train, {len(val_imgs)} val")
+
+print("All new classes are organized and ready for training!")