Update app.py

app.py

@@ -1,26 +1,40 @@
-import gradio as gr
-from ultralytics import YOLO
-import cv2
 import torch
+from ultralytics import YOLO
+from ultralytics.nn.tasks import DetectionModel  # ✅ Correct import for PyTorch 2.6 fix
+import gradio as gr
 
+# Fix for PyTorch 2.6 safe loading
 torch.serialization.add_safe_globals([DetectionModel])
 
-# Load your YOLOv8 model
+# Load YOLOv8 model
 model = YOLO("best.pt")
 
-# Prediction function
+# Detection function
 def detect_fracture(image):
-    results = model.predict(source=image, save=False)  # Run inference
-    annotated_img = results[0].plot()  # Draw detections
-    return annotated_img
+    # Run detection
+    results = model.predict(source=image, save=False)
+    annotated_img = results[0].plot()  # Annotated image with bounding boxes
+
+    # Extract labels & confidence scores
+    detections = []
+    for box in results[0].boxes:
+        cls_id = int(box.cls[0])
+        label = model.names[cls_id]
+        conf = float(box.conf[0])
+        detections.append(f"{label}: {conf:.2f}")
+
+    return annotated_img, "\n".join(detections) if detections else "No fracture detected"
 
-# Gradio interface
+# Gradio app
 demo = gr.Interface(
     fn=detect_fracture,
     inputs=gr.Image(type="numpy", label="Upload Bone X-ray"),
-    outputs=gr.Image(type="numpy", label="Detection Result"),
+    outputs=[
+        gr.Image(type="numpy", label="Detection Result"),
+        gr.Textbox(label="Detected Fractures & Confidence Scores")
+    ],
     title="Human Bone Fracture Detection",
-    description="Upload an X-ray image to detect types of human bone fractures using YOLOv8."
+    description="Upload an X-ray image to detect types of human bone fractures using a YOLOv8 model."
 )
 
 if __name__ == "__main__":