Update app.py

app.py

@@ -4,6 +4,7 @@ from roboflow import Roboflow
 import tempfile
 import os
 import requests
+from sahi.predict import get_sliced_prediction  # SAHI slicing inference
 
 # Muat variabel lingkungan dari file .env
 load_dotenv()
@@ -25,15 +26,22 @@ def detect_objects(image):
         temp_file_path = temp_file.name
 
     try:
-        # Lakukan prediksi pada gambar
-        predictions = model.predict(temp_file_path, confidence=60, overlap=80).json()
+        # Perform sliced inference with SAHI
+        result = get_sliced_prediction(
+            temp_file_path,
+            model,
+            slice_height=256,  # Adjust as needed
+            slice_width=256,   # Adjust as needed
+            overlap_height_ratio=0.2,  # Adjust as needed
+            overlap_width_ratio=0.2   # Adjust as needed
+        )
 
         # Menghitung jumlah objek per kelas
         class_count = {}
         total_count = 0  # Menyimpan total jumlah objek
 
-        for prediction in predictions['predictions']:
-            class_name = prediction['class']
+        for prediction in result.object_prediction_list:
+            class_name = prediction.class_id  # or prediction.class_name if available
             class_count[class_name] = class_count.get(class_name, 0) + 1
             total_count += 1  # Tambah jumlah objek untuk setiap prediksi
 
@@ -45,8 +53,9 @@ def detect_objects(image):
 
         # Menyimpan gambar dengan prediksi
         output_image_path = "/tmp/prediction.jpg"
-        model.predict(temp_file_path, confidence=60, overlap=80).save(output_image_path)
-        
+        result.export_visuals(export_dir="/tmp/")  # Export visuals for display
+        output_image_path = "/tmp/prediction_visual.png"  # Assuming the visual output is saved here
+
     except requests.exceptions.HTTPError as http_err:
         # Menangani kesalahan HTTP
         result_text = f"HTTP error occurred: {http_err}"