Update app.py

app.py

@@ -1,77 +1,32 @@
-import streamlit as st
-import torch
-import cv2
-import tempfile
-import os
-from PIL import Image
-import numpy as np
-
-# Muat model YOLOv5 yang sudah dilatih
-@st.cache_resource
-def load_model():
-    model = torch.hub.load('ultralytics/yolov11', 'custom', path='best.pt', force_reload=True)
-    return model
-
-model = load_model()
-
-# Fungsi untuk deteksi pada gambar
-def detect_image(image):
-    results = model(image)
-    results.render()  # Tambahkan bounding box ke gambar
-    annotated_image = results.imgs[0]  # Ambil gambar yang sudah dianotasi
-    return annotated_image
-
-# Fungsi untuk deteksi pada video
-def detect_video(video_path, output_path):
-    cap = cv2.VideoCapture(video_path)
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
-    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        results = model(frame)
-        results.render()
-        annotated_frame = np.squeeze(results.render())
-        out.write(annotated_frame)
-
-    cap.release()
-    out.release()
-
-# Streamlit Interface
-st.title("YOLO Object Detection")
-
-# Opsi untuk memilih gambar atau video
-option = st.radio("Pilih jenis input:", ("Gambar", "Video"))
-
-if option == "Gambar":
-    uploaded_image = st.file_uploader("Unggah gambar", type=["jpg", "jpeg", "png"])
-    if uploaded_image is not None:
-        image = Image.open(uploaded_image)
-        st.image(image, caption="Gambar asli", use_column_width=True)
-        
-        # Deteksi objek
-        annotated_image = detect_image(np.array(image))
-        st.image(annotated_image, caption="Hasil deteksi", use_column_width=True)
-
-elif option == "Video":
-    uploaded_video = st.file_uploader("Unggah video", type=["mp4", "avi", "mov"])
-    if uploaded_video is not None:
-        # Simpan video sementara
-        temp_video_path = tempfile.NamedTemporaryFile(delete=False).name
-        with open(temp_video_path, "wb") as f:
-            f.write(uploaded_video.read())
-        
-        # Proses video
-        output_video_path = "output_video.mp4"
-        st.text("Sedang memproses video...")
-        detect_video(temp_video_path, output_video_path)
-        st.text("Proses selesai!")
-
-        # Tampilkan hasil video
-        st.video(output_video_path)
+import gradio as gr
+from ultralytics import YOLO
+
+# Muat model custom yang telah dilatih
+model = YOLO("best.pt")  # Pastikan file best.pt ada di direktori yang sama
+
+# Fungsi untuk melakukan prediksi
+def predict_image(img, conf_threshold, iou_threshold):
+    results = model.predict(
+        source=img,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+    )
+    return results[0].plot() if results else None
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),  # Input gambar
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),  # Confidence threshold
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),  # IoU threshold
+    ],
+    outputs=gr.Image(type="pil", label="Result"),  # Output gambar dengan bounding box
+    title="Ultralytics Gradio YOLO11 - Custom Model",  # Judul aplikasi
+    description="Upload images for custom YOLO11 object detection.",  # Deskripsi aplikasi
+)
+
+# Luncurkan aplikasi
+iface.launch()