Update app.py

app.py

@@ -32,35 +32,43 @@ def detect_objects(image):
 
     for prediction in predictions['predictions']:
         class_name = prediction['class']
-        if class_name in class_count:
-            class_count[class_name] += 1
-        else:
-            class_count[class_name] = 1
+        class_count[class_name] = class_count.get(class_name, 0) + 1
         total_count += 1  # Tambah jumlah objek untuk setiap prediksi
 
     # Menyusun output berupa string hasil perhitungan
     result_text = "Product Nestle\n\n"
-    
     for class_name, count in class_count.items():
-        result_text += f"{class_name}: {count} \n"
-    
-    result_text += f"\nTotal Product Nestle: {total_count}" 
+        result_text += f"{class_name}: {count}\n"
+    result_text += f"\nTotal Product Nestle: {total_count}"
     
     # Menyimpan gambar dengan prediksi
-    output_image = model.predict(temp_file_path, confidence=60, overlap=80).save("/tmp/prediction.jpg")
+    output_image_path = "/tmp/prediction.jpg"
+    model.predict(temp_file_path, confidence=60, overlap=80).save(output_image_path)
     
     # Hapus file sementara setelah prediksi
     os.remove(temp_file_path)
     
-    return "/tmp/prediction.jpg", result_text
+    return output_image_path, result_text
 
-# Membuat antarmuka Gradio
-iface = gr.Interface(
-    fn=detect_objects,                         # Fungsi yang dipanggil saat gambar diupload
-    inputs=gr.Image(type="pil"),               # Input berupa gambar
-    outputs=[gr.Image(), gr.Textbox()],        # Output gambar dan teks
-    live=True                                    # Menampilkan hasil secara langsung
-)
+# Membuat antarmuka Gradio dengan tata letak fleksibel
+with gr.Blocks() as iface:
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Input Image")
+        with gr.Column():
+            output_image = gr.Image(label="Detect Object")
+        with gr.Column():
+            output_text = gr.Textbox(label="Counting Object")
+    
+    # Tombol untuk memproses input
+    detect_button = gr.Button("Detect")
+    
+    # Hubungkan tombol dengan fungsi deteksi
+    detect_button.click(
+        fn=detect_objects, 
+        inputs=input_image, 
+        outputs=[output_image, output_text]
+    )
 
 # Menjalankan antarmuka
-iface.launch()
+iface.launch()