app.py

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(16).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=50, overlap=80).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=50, 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()