Create app.py

app.py

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+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
+
+# Fungsi untuk menangani input dan output gambar
+def detect_objects(image):
+    # 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  # Menyimpan total jumlah objek
+
+    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
+
+    # Menyusun output berupa string hasil perhitungan
+    result_text = "Product Nestle \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 = model.predict(temp_file_path, confidence=60, overlap=80).save("/tmp/prediction.jpg")
+    
+    # Hapus file sementara setelah prediksi
+    os.remove(temp_file_path)
+    
+    return "/tmp/prediction.jpg", 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
+)
+
+# Menjalankan antarmuka
+iface.launch()