Update app.py

app.py

@@ -4,31 +4,31 @@ from roboflow import Roboflow
 import tempfile
 import os
 
-# Load environment variables from .env file
+# Muat variabel lingkungan dari file .env
 load_dotenv()
 api_key = os.getenv("ROBOFLOW_API_KEY")
 workspace = os.getenv("ROBOFLOW_WORKSPACE")
 project_name = os.getenv("ROBOFLOW_PROJECT")
 model_version = int(os.getenv("ROBOFLOW_MODEL_VERSION"))
 
-# Initialize Roboflow using the loaded environment variables
+# Inisialisasi Roboflow menggunakan data yang diambil dari secrets
 rf = Roboflow(api_key=api_key)
 project = rf.workspace(workspace).project(project_name)
 model = project.version(model_version).model
 
-# Function to handle image input and output
+# Fungsi untuk menangani input dan output gambar
 def detect_objects(image):
-    # Save the uploaded image as a temporary file
+    # Simpan gambar yang diupload sebagai file sementara
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
         image.save(temp_file, format="JPEG")
         temp_file_path = temp_file.name
 
-    # Perform prediction on the image
+    # Lakukan prediksi pada gambar
     predictions = model.predict(temp_file_path, confidence=60, overlap=80).json()
     
-    # Count the number of objects per class
+    # Menghitung jumlah objek per kelas
     class_count = {}
-    total_count = 0  # Store the total number of objects
+    total_count = 0  # Menyimpan total jumlah objek
 
     for prediction in predictions['predictions']:
         class_name = prediction['class']
@@ -36,38 +36,31 @@ def detect_objects(image):
             class_count[class_name] += 1
         else:
             class_count[class_name] = 1
-        total_count += 1  # Increment total object count for each prediction
+        total_count += 1  # Tambah jumlah objek untuk setiap prediksi
 
-    # Prepare the result text
+    # 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"\nTotal Product Nestle: {total_count}" 
     
-    # Save the image with predictions
-    output_image_path = "/tmp/prediction.jpg"
-    model.predict(temp_file_path, confidence=60, overlap=80).save(output_image_path)
+    # Menyimpan gambar dengan prediksi
+    output_image = model.predict(temp_file_path, confidence=60, overlap=80).save("/tmp/prediction.jpg")
     
-    # Remove the temporary file after prediction
+    # Hapus file sementara setelah prediksi
     os.remove(temp_file_path)
     
-    return output_image_path, result_text
+    return "/tmp/prediction.jpg", result_text
 
-# Create the Gradio interface
-with gr.Blocks() as iface:
-    with gr.Row():
-        image_input = gr.Image(type="pil", label="Input Image")
-    with gr.Row():
-        image_output = gr.Image(label="Detect Object")
-    with gr.Row():
-        text_output = gr.Textbox(label="Counting Object")
-    gr.Interface(
-        fn=detect_objects,
-        inputs=image_input,
-        outputs=[image_output, text_output],
-    )
+# 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
+)
 
-# Launch the interface
-iface.launch()
+# Menjalankan antarmuka
+iface.launch()