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app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+
+# Model load karein (ensure karein best.pt upload ho chuki hai)
+model = YOLO("best.pt")
+
+def predict_image(img):
+    # Model prediction
+    results = model(img)
+    
+    # Annotated image hasil karein (boxes ke saath)
+    # results[0].plot() humein numpy array deta hai jis par boxes bane hote hain
+    res_plotted = results[0].plot()
+    
+    # RGB mein convert karein taake Gradio sahi dikhaye
+    output_img = Image.fromarray(res_plotted[:, :, ::-1]) 
+    
+    return output_img
+
+# Gradio interface setup
+demo = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Face Mask Detection (YOLOv8)",
+    description="Apni photo upload karein. Model 'with_mask', 'without_mask', ya 'incorrect' detect karega."
+)
+
+if __name__ == "__main__":
+    demo.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09289b1ccee1f7abd8e716976130685ef9ec8f2628a3518e364c0e3646616208
+size 6190122

data.yaml

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+train: ../train/images
+val: ../valid/images
+test: ../test/images
+
+nc: 3
+names: ['mask_weared_incorrect', 'with_mask', 'without_mask']
+
+roboflow:
+  workspace: saad-n
+  project: face-mask-y1vsd-mq8qr
+  version: 1
+  license: CC BY 4.0
+  url: https://universe.roboflow.com/saad-n/face-mask-y1vsd-mq8qr/dataset/1

requirements.txt

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+ultralytics
+gradio
+opencv-python
+PIL