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Dockerfile

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+FROM python:3.10.13-slim
+
+# DEMO
+WORKDIR /app
+
+COPY ./requirements.txt /app/requirements.txt
+RUN apt-get update && apt-get install -y && rm -rf /var/lib/apt/lists/*
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+RUN pip install -r requirements.txt
+
+#USER
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME /home/user
+ENV PATH $HOME/.local/bin:$PATH
+
+WORKDIR $HOME
+RUN mkdir app
+WORKDIR $HOME/app
+COPY . $HOME/app
+
+EXPOSE 7860
+CMD streamlit run yolov10streamlit.py \
+    --server.headless true \
+    --server.port 7860 \
+    --server.enableCORS false \
+    --server.enableXsrfProtection false \
+    --server.fileWatcherType none
+
+#CMD python yolov10gradio.py

helloworld.py

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+print("Hello, World fro VAL-OD!")

requirements.txt

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+streamlit
+gradio
+ultralytics

yolov10_predict.py

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+from ultralytics import YOLO
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+
+model=YOLO('yolov10n.pt')
+
+file="su57.jpg"
+image=Image.open(file)
+
+results=model.predict(source=np.array(image))
+result_images=results[0].plot()
+
+plt.imshow(result_images)
+plt.axis('off')
+plt.show()

yolov10gradio.py

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+from ultralytics import YOLO
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+import gradio as gr
+
+model=YOLO('yolov10n.pt')
+def predict_image(image):
+    np_image=np.array(image)
+    results=model.predict(source=np_image)
+    result_images=results[0].plot()
+    return result_images
+
+iface=gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type='pil', label="Chon mot tep anh? "),
+    outputs=gr.Image(type='pil', label="Anh dau ra"),
+    title="YOLOv10 Object Detection",
+    description="Chon mot tep anh de nhan dien doi tuong"
+)
+iface.launch()

yolov10n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11287ed0735678e7ba1ac2a9b3098c049155b3fde123992e724c1264bcc16b6f
+size 5860383

yolov10streamlit.py

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+from ultralytics import YOLO
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+import streamlit as st
+
+model=YOLO('yolov10n.pt')
+st.title("YOLOv10 Object Detection")
+
+uploaded_file=st.file_uploader("Chon mot tep anh? ", type=['jpg','jpeg','png'])
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Anh dau vao", use_column_width=True)
+
+    if st.button("Predict"):
+        results=model.predict(source=np.array(image))
+        result_images = results[0].plot()
+        st.image(result_images, caption="Anh dau ra", use_column_width=True)