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/test_trained_ml.py

@@ -0,0 +1,40 @@
+import torch
+from torchvision import transforms, models
+from PIL import Image
+import os
+
+# ---------- CONFIG ----------
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+NUM_CLASSES = 6
+CLASS_NAMES = ["broken_streetlight","drainage","garbage", "pothole","signage", "streetlight"]
+MODEL_PATH = "best_model.pth"
+TEST_IMAGES_DIR = "images"  # folder containing test images
+
+# ---------- MODEL ----------
+model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
+model.fc = torch.nn.Linear(model.fc.in_features, NUM_CLASSES)
+model.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE))
+model = model.to(DEVICE)
+model.eval()
+
+# ---------- IMAGE PREPROCESS ----------
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+# ---------- INFERENCE ----------
+for image_name in os.listdir(TEST_IMAGES_DIR):
+    image_path = os.path.join(TEST_IMAGES_DIR, image_name)
+    if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
+        continue
+
+    image = Image.open(image_path).convert("RGB")
+    input_tensor = preprocess(image).unsqueeze(0).to(DEVICE)  # add batch dimension
+
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        _, predicted = torch.max(outputs, 1)
+        predicted_class = CLASS_NAMES[predicted.item()]
+
+    print(f"{image_name} --> Predicted class: {predicted_class}")