Update app.py

app.py

@@ -1,24 +1,16 @@
 import gradio as gr
-from dotenv import load_dotenv
 from roboflow import Roboflow
 import tempfile
 import os
 
-# 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
+# Inisialisasi Roboflow
+rf = Roboflow(api_key="Otg64Ra6wNOgDyjuhMYU")
+project = rf.workspace("alat-pelindung-diri").project("nescafe-4base")
+model = project.version(46).model
 
 # Fungsi untuk menangani input dan output gambar
 def detect_objects(image):
-    # Simpan gambar yang diupload sebagai file sementara
+    # Menyimpan 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
@@ -28,24 +20,22 @@ def detect_objects(image):
     
     # Menghitung jumlah objek per kelas
     class_count = {}
-    total_count = 0  # Menyimpan total jumlah objek
-
+    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  # Tambah jumlah objek untuk setiap prediksi
+        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} \n"
-    
+        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")