Create app.py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+from PIL import Image
+import numpy as np
+import cv2
+from ultralytics import YOLO
+
+# Title
+st.title("Suspicious Activity Detection")
+st.markdown("Upload an image to detect activity (Normal, Peaking, Sneaking, Stealing).")
+
+# Load model
+@st.cache_resource
+def load_model():
+    model = YOLO("yolo11l.pt")  # Make sure this file is uploaded to your Space
+    return model
+
+model = load_model()
+
+# File uploader
+uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Convert to numpy array for OpenCV/YOLO
+    img_array = np.array(image)
+
+    # Run prediction
+    st.info("Running detection...")
+    results = model.predict(source=img_array, conf=0.25, classes=None)
+
+    for r in results:
+        # Show annotated image
+        annotated_img = r.plot()
+        st.image(annotated_img, caption="Detection Result", use_column_width=True)
+
+        # Show detected objects with confidence
+        st.subheader("Detections:")
+        for box in r.boxes:
+            conf = float(box.conf[0])
+            cls_id = int(box.cls[0])
+            cls_name = model.names[cls_id]
+            st.write(f"- **{cls_name}** (Confidence: {conf:.2f})")