Update app.py

app.py

@@ -10,7 +10,7 @@ from PIL import Image, ImageDraw
 # Initialize Roboflow
 rf = Roboflow(api_key="Otg64Ra6wNOgDyjuhMYU")
 project = rf.workspace("alat-pelindung-diri").project("nescafe-4base")
-model = project.version(6).model
+model = project.version(16).model
 
 # Apply NMS (Non-Maximum Suppression)
 def apply_nms(predictions, iou_threshold=0.5):
@@ -85,17 +85,27 @@ def detect_objects(image):
         print("Failed to access sliced_image_paths attribute.")
         sliced_image_paths = []
 
-    # Save all predictions for each sliced image
-    all_predictions = []
+    # Check predictions for the whole image first
+    print("Predicting on the whole image (without slicing)...")
+    whole_image_predictions = model.predict(image_path=temp_file_path).json()
+    print(f"Whole image predictions: {whole_image_predictions}")
+
+    # If there are predictions, return them
+    if whole_image_predictions['predictions']:
+        print("Using predictions from the whole image.")
+        all_predictions = whole_image_predictions['predictions']
+    else:
+        print("No predictions found for the whole image. Predicting on slices...")
+        # If no predictions for the whole image, predict on slices
+        all_predictions = []
+
+        for sliced_image_path in sliced_image_paths:
+            if isinstance(sliced_image_path, str):
+                predictions = model.predict(image_path=sliced_image_path).json()
+                all_predictions.extend(predictions['predictions'])
+            else:
+                print(f"Skipping invalid image path: {sliced_image_path}")
 
-    # Predict on each sliced image
-    for sliced_image_path in sliced_image_paths:
-        if isinstance(sliced_image_path, str):
-            predictions = model.predict(image_path=sliced_image_path).json()
-            all_predictions.extend(predictions['predictions'])
-        else:
-            print(f"Skipping invalid image path: {sliced_image_path}")
-    
     # Apply NMS to remove duplicate detections
     postprocessed_predictions = apply_nms(all_predictions, iou_threshold=0.5)