Update app.py

app.py

@@ -1,56 +1,85 @@
 import gradio as gr
+from dotenv import load_dotenv
 from roboflow import Roboflow
 import tempfile
 import os
+import requests
 
-# Inisialisasi Roboflow
-rf = Roboflow(api_key="Otg64Ra6wNOgDyjuhMYU")
-project = rf.workspace("alat-pelindung-diri").project("nescafe-4base")
-model = project.version(46).model
+# 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"))
+
+# 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
 
 # Fungsi untuk menangani input dan output gambar
 def detect_objects(image):
-    # Menyimpan gambar yang diupload sebagai file sementara
+    # 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
 
-    # Lakukan prediksi pada gambar
-    predictions = model.predict(temp_file_path, confidence=60, overlap=80).json()
-    
-    # Menghitung jumlah objek per kelas
-    class_count = {}
-    total_count = 0
-    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
-        total_count += 1
-
-    # 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} objek\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")
-    
+    try:
+        # Lakukan prediksi pada gambar
+        predictions = model.predict(temp_file_path, confidence=60, overlap=80).json()
+
+        # Menghitung jumlah objek per kelas
+        class_count = {}
+        total_count = 0  # Menyimpan total jumlah objek
+
+        for prediction in predictions['predictions']:
+            class_name = prediction['class']
+            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}"
+
+        # Menyimpan gambar dengan prediksi
+        output_image_path = "/tmp/prediction.jpg"
+        model.predict(temp_file_path, confidence=60, overlap=80).save(output_image_path)
+        
+    except requests.exceptions.HTTPError as http_err:
+        # Menangani kesalahan HTTP
+        result_text = f"HTTP error occurred: {http_err}"
+        output_image_path = temp_file_path  # Kembalikan gambar asli jika terjadi error
+    except Exception as err:
+        # Menangani kesalahan lain
+        result_text = f"An error occurred: {err}"
+        output_image_path = temp_file_path  # Kembalikan gambar asli jika terjadi error
+
     # 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()