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app.py

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+import gradio as gr
+from roboflow import Roboflow
+import tempfile
+import os
+
+# Inisialisasi Roboflow
+rf = Roboflow(api_key="Otg64Ra6wNOgDyjuhMYU")
+project = rf.workspace("alat-pelindung-diri").project("nescafe-4base")
+model = project.version(8).model
+
+# Fungsi untuk menangani input dan output gambar
+def detect_objects(image):
+    # 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
+
+    # Lakukan prediksi pada gambar
+    predictions = model.predict(temp_file_path, confidence=80, overlap=60).json()
+    
+    # Menghitung jumlah objek per kelas
+    class_count = {}
+    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
+
+    # Menyusun output berupa string hasil perhitungan
+    result_text = "Jumlah objek per kelas:\n"
+    for class_name, count in class_count.items():
+        result_text += f"{class_name}: {count} objek\n"
+    
+    # Menyimpan gambar dengan prediksi
+    output_image = model.predict(temp_file_path, confidence=40, overlap=30).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()

requirements.txt

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+gradio
+roboflow
+#opencv-python
+#numpy