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best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97f016ba6a0fede42dfe3cadc4b0c0fcead6646fa8136e3015f65ec3ab84f8ae
+size 5621807

main.py

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+import streamlit as st
+import numpy as np
+from PIL import Image, ImageDraw
+from ultralytics import YOLO
+
+# Muat model YOLO
+model = YOLO('best.pt')  # Pastikan path ini benar
+
+# Fungsi untuk melakukan deteksi jerawat
+def detect_acne(image):
+    # Convert image to NumPy array
+    img_np = np.array(image)
+
+    # Deteksi jerawat menggunakan model YOLO
+    results = model.predict(source=img_np, imgsz=640)
+
+    # Menggambar kotak deteksi di gambar asli
+    img_np_copy = img_np.copy()  # Buat salinan gambar
+    img_pil = Image.fromarray(img_np_copy)
+    draw = ImageDraw.Draw(img_pil)
+
+    for result in results:
+        boxes = result.boxes
+        if boxes is not None and len(boxes.xyxy) > 0:
+            for i, box in enumerate(boxes.xyxy):
+                x1, y1, x2, y2 = box[:4]
+                conf = boxes.conf[i] if boxes.conf is not None and len(boxes.conf) > i else None
+
+                # Menggambar kotak di gambar asli dengan warna merah
+                draw.rectangle([(x1, y1), (x2, y2)], outline="red", width=2)
+
+                if conf is not None:
+                    # Menambahkan label dengan confidence score
+                    draw.text((x1, y1 - 10), f'{conf:.2f}', fill="red")
+
+    return img_pil
+
+# Judul aplikasi
+st.title('Deteksi Jerawat Menggunakan YOLOv8')
+
+# Pilihan untuk mengunggah gambar
+uploaded_file = st.file_uploader("Unggah Gambar", type=["jpg", "jpeg", "png"])
+
+# Tampilkan gambar asli terlebih dahulu
+if uploaded_file is not None:
+    # Membaca gambar
+    image = Image.open(uploaded_file)
+
+    # Tampilkan gambar asli
+    st.image(image, caption='Gambar yang Diupload', use_column_width=True)
+
+    # Tombol untuk melakukan deteksi jerawat
+    if st.button('Deteksi Jerawat'):
+        # Deteksi jerawat dalam gambar
+        detected_image = detect_acne(image)
+
+        # Tampilkan gambar hasil deteksi
+        st.image(detected_image, caption='Hasil Deteksi Jerawat', use_column_width=True)

requirements.txt

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+streamlit==1.39.0
+opencv-python-headless==4.10.0.84
+numpy==2.1.2
+Pillow==10.4.0
+ultralytics==8.3.8