change model

app.py

@@ -11,7 +11,7 @@ rf = Roboflow(api_key="Otg64Ra6wNOgDyjuhMYU")
 project = rf.workspace("alat-pelindung-diri").project("nescafe-4base")
 model = project.version(16).model
 
-# Fungsi untuk deteksi objek menggunakan SAHI
+# Fungsi untuk deteksi objek menggunakan SAHI dan Roboflow Model
 def detect_objects(image):
     # Menyimpan gambar sementara
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
@@ -29,21 +29,12 @@ def detect_objects(image):
         overlap_width_ratio=0.1
     )
 
-    # Jalankan prediksi pada setiap potongan gambar
-    predictions = get_sliced_prediction(
-        image_path=temp_file_path,
-        model_path="path_to_your_model",
-        model_type="yolov8",  # Adjust based on your model
-        slice_height=256,
-        slice_width=256,
-        overlap_height_ratio=0.1,
-        overlap_width_ratio=0.1,
-        model_confidence_threshold=0.25
-    )
+    # Prediksi menggunakan model Roboflow
+    predictions = model.predict(image_path=temp_file_path).json()
 
     # Postprocess dan gabungkan hasil prediksi
     postprocessed_predictions = postprocess_predictions(
-        predictions=predictions,
+        predictions=predictions['predictions'],
         postprocess_type='NMS',
         iou_threshold=0.5
     )
@@ -58,7 +49,7 @@ def detect_objects(image):
     # Menghitung jumlah objek per kelas
     class_count = {}
     for detection in postprocessed_predictions:
-        class_name = detection.class_name
+        class_name = detection['class']
         if class_name in class_count:
             class_count[class_name] += 1
         else: