Update app.py

app.py

@@ -71,15 +71,91 @@ iface = gr.Interface(
     outputs=gr.Image(type="pil", label="Detection Results"),
     title="YOLOv8m Defence Object Detection",
     description="""
+import gradio as gr
+import PIL.Image as Image
+from ultralytics import YOLO
+import torch
+import os
+
+# Load model once at startup
+print("Loading YOLOv8m Defence model...")
+model = YOLO("yolov8m_defence.pt")
+
+# Set device and optimize for CPU inference
+if torch.cuda.is_available():
+    device = 'cuda'
+    print("Using GPU acceleration")
+else:
+    device = 'cpu'
+    print("Using CPU inference")
+    # Optimize for CPU
+    torch.set_num_threads(2)  # Limit threads for free tier
+
+model.to(device)
+
+def predict_image(img, conf_threshold, iou_threshold):
+    """Predicts objects in an image using YOLOv8m Defence model with adjustable confidence and IOU thresholds."""
+    try:
+        results = model.predict(
+            source=img,
+            conf=conf_threshold,
+            iou=iou_threshold,
+            show_labels=True,
+            show_conf=True,
+            imgsz=640,  # Keep original size for accuracy
+            verbose=False,  # Reduce console output
+            device=device
+        )
+        
+        for r in results:
+            im_array = r.plot()
+            im = Image.fromarray(im_array[..., ::-1])
+        
+        return im
+    
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+        return img  # Return original image if error occurs
+
+# Custom CSS for font styling
+css = """
+body, .gradio-container {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
+}
+.gr-button {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
+    font-weight: 500 !important;
+}
+.gr-box h1 {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
+    font-weight: 600 !important;
+}
+.gr-box p {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
+}
+"""
+
+# Create interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
+    ],
+    outputs=gr.Image(type="pil", label="Detection Results"),
+    title="🛡️ YOLOv8m Defence Object Detection",
+    description="""
     Upload images to detect military and civilian vehicles, aircraft, and ships using our fine-tuned YOLOv8m model.
+    Developed for DSTA Brainhack 2025 - TIL-AI Category (Semi-Finalist).
+    
+    **📋 Model Card:** [spencercdz/YOLOv8m_defence](https://huggingface.co/spencercdz/YOLOv8m_defence)
     
     **Detectable Objects (18 categories):** Aircraft (cargo, commercial, fighter, helicopter, etc.), 
     Vehicles (car, truck, tank, bus, van), Ships (cargo, yacht, cruise, warship, sailboat), 
     and specialized items (drone, missile).
     
-    **Note:** Running on the Free Tier - inference may take longer than expected.
-    
-    Developed for DSTA Brainhack 2025 - TIL-AI Category (Semi-Finalist)
+    **Note:** Running on CPU - inference takes 20-40 seconds per image for full accuracy.
     """,
     examples=[
         ["examples/test1.jpg", 0.25, 0.45],
@@ -94,6 +170,24 @@ iface = gr.Interface(
         ["examples/test10.jpg", 0.25, 0.45],
     ],
     css=css,
+    cache_examples=True  # Cache example results
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)    """,
+    examples=[
+        ["examples/test1.jpg", 0.25, 0.45],
+        ["examples/test2.jpg", 0.25, 0.45],
+        ["examples/test3.jpg", 0.25, 0.45],
+        ["examples/test4.jpg", 0.25, 0.45],
+        ["examples/test5.jpg", 0.25, 0.45],
+        ["examples/test6.jpg", 0.25, 0.45],
+        ["examples/test7.jpg", 0.25, 0.45],
+        ["examples/test8.jpg", 0.25, 0.45],
+        ["examples/test9.jpg", 0.25, 0.45],
+        ["examples/test10.jpg", 0.25, 0.45],
+    ],
+    css=css,
     cache_examples=True
 )