Update app.py

app.py

@@ -1,36 +1,34 @@
 import gradio as gr
-import os
-import tempfile
-from roboflow import Roboflow
 from dotenv import load_dotenv
+from roboflow import Roboflow
+import tempfile
+import os
 
-# Muat variabel lingkungan dari file .env
+# Load environment variables from .env file
 load_dotenv()
-
-# Ambil nilai dari environment variables
 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"))
 
-# Inisialisasi Roboflow menggunakan data yang diambil dari .env
+# Initialize Roboflow using the loaded environment variables
 rf = Roboflow(api_key=api_key)
 project = rf.workspace(workspace).project(project_name)
 model = project.version(model_version).model
 
-# Fungsi untuk menangani input dan output gambar
+# Function to handle image input and output
 def detect_objects(image):
-    # Simpan gambar yang diupload sebagai file sementara
+    # Save the uploaded image as a temporary file
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
         image.save(temp_file, format="JPEG")
         temp_file_path = temp_file.name
 
-    # Lakukan prediksi pada gambar
+    # Perform prediction on the image
     predictions = model.predict(temp_file_path, confidence=60, overlap=80).json()
     
-    # Menghitung jumlah objek per kelas
+    # Count the number of objects per class
     class_count = {}
-    total_count = 0  # Menyimpan total jumlah objek
+    total_count = 0  # Store the total number of objects
 
     for prediction in predictions['predictions']:
         class_name = prediction['class']
@@ -38,40 +36,39 @@ def detect_objects(image):
             class_count[class_name] += 1
         else:
             class_count[class_name] = 1
-        total_count += 1  # Tambah jumlah objek untuk setiap prediksi
+        total_count += 1  # Increment total object count for each prediction
 
-    # Menyusun output berupa string hasil perhitungan
-    result_text = "Product Nestle\n\n"  # Tambahkan baris kosong setelah judul
+    # Prepare the result text
+    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}"  # Tambahkan baris kosong antara kategori dan total
+    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")
+    # Save the image with predictions
+    output_image_path = "/tmp/prediction.jpg"
+    model.predict(temp_file_path, confidence=60, overlap=80).save(output_image_path)
     
-    # Hapus file sementara setelah prediksi
+    # Remove the temporary file after prediction
     os.remove(temp_file_path)
     
-    return "/tmp/prediction.jpg", result_text
-
-# Membuat antarmuka Gradio dengan label yang telah diganti
-inputs = gr.Image(type="pil", label="Input Image")  # Label input
-outputs = [gr.Image(label="Detect Object"), gr.Textbox(label="Counting Object")]  # Label output
-
-# Membuat layout dengan gr.Row() untuk menampilkan input dan output berdampingan
-iface = gr.Interface(
-    fn=detect_objects,
-    inputs=inputs,
-    outputs=outputs,
-    live=True
-)
+    return output_image_path, result_text
 
-# Menyusun antarmuka dengan layout horizontal
-iface.layout = gr.Row(
-    inputs,  # Input image component
-    outputs  # Output components (image and 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():
+        with gr.Column():
+            image_output = gr.Image(label="Detect Object")
+        with gr.Column():
+            text_output = gr.Textbox(label="Counting Object")
+    gr.Interface(
+        fn=detect_objects,
+        inputs=image_input,
+        outputs=[image_output, text_output],
+    )
 
-# Menjalankan antarmuka
+# Launch the interface
 iface.launch()