| import streamlit as st |
| from PIL import Image |
| import torch |
| from ultralytics import YOLO |
| from huggingface_hub import hf_hub_download |
|
|
| |
| model_path = hf_hub_download( |
| repo_id="harpreetsahota/car-dd-segmentation-yolov11", |
| filename="best.pt" |
| ) |
|
|
| |
| model = YOLO(model_path) |
|
|
| st.set_page_config(page_title="Car Damage Detection", page_icon="🚗", layout="centered") |
| st.title("🚗 Машина зақымын анықтау (YOLOv11 Segmentation)") |
|
|
| uploaded_file = st.file_uploader("Машина суретін жүктеңіз", type=["jpg", "jpeg", "png"]) |
|
|
| if uploaded_file is not None: |
| image = Image.open(uploaded_file).convert("RGB") |
| st.image(image, caption="Жүктелген сурет", use_column_width=True) |
|
|
| st.write("🔎 Анализ жасалуда...") |
| results = model.predict(image) |
|
|
| st.subheader("📋 Нәтиже:") |
| for r in results: |
| boxes = r.boxes |
| names = r.names |
| for box in boxes: |
| cls = int(box.cls[0]) |
| label = names[cls] |
| conf = float(box.conf[0]) |
| st.success(f"{label} (сенімділік: {conf:.2f})") |
|
|
| |
| res_plotted = results[0].plot()[:, :, ::-1] |
| st.image(res_plotted, caption="Зақым табылған аймақтар", use_column_width=True) |
|
|
