Create app.py

app.py

@@ -0,0 +1,34 @@
+import os
+import cv2
+import numpy as np
+import gradio as gr
+from ultralytics import YOLO
+
+# Load model: fallback to YOLOv8n if custom weights are missing
+model_path = "best.pt"
+model = YOLO(model_path) if os.path.exists(model_path) else YOLO("yolov8n.pt")
+
+def detect_mask(img, conf_thresh, iou_thresh):
+    try:
+        img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        results = model.predict(img_bgr, conf=conf_thresh, iou=iou_thresh)[0]
+        annotated = results.plot()
+        return cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
+    except Exception as e:
+        print(f"Error: {e}")
+        return np.zeros_like(img)
+
+demo = gr.Interface(
+    fn=detect_mask,
+    inputs=[
+        gr.Image(type="numpy", label="📷 Upload Image"),
+        gr.Slider(0.3, 1.0, value=0.5, label="Confidence Threshold"),
+        gr.Slider(0.3, 1.0, value=0.5, label="IoU Threshold")
+    ],
+    outputs=gr.Image(label="🧠 Detection Result"),
+    title="🎯 Face Mask Detection (YOLOv8)",
+    description="Detects 'Mask' and 'No Mask' using YOLOv8. Upload an image and adjust thresholds. Automatically falls back to YOLOv8n if custom weights are missing."
+)
+
+if __name__ == "__main__":
+    demo.launch()