Update app.py

app.py

@@ -2,26 +2,48 @@ import gradio as gr
 from ultralytics import YOLO
 import numpy as np
 from PIL import Image
+import json
 
 # load model
 model = YOLO("best.pt")
 
 def predict(image):
     results = model(image)
-
     r = results[0]
-
-    output_image = r.plot()  # YOLO annotated image
-
-    return Image.fromarray(output_image)
+    
+    # Get detection results
+    detections = []
+    if len(r.boxes) > 0:
+        for box in r.boxes:
+            class_id = int(box.cls[0])
+            class_name = model.names[class_id]
+            confidence = float(box.conf[0])
+            detections.append({
+                "class": class_name,
+                "confidence": round(confidence * 100, 2)
+            })
+    
+    # Sort by confidence and get the top detection
+    if detections:
+        detections.sort(key=lambda x: x["confidence"], reverse=True)
+        top_detection = detections[0]
+        result_text = f"{top_detection['class']}: {top_detection['confidence']}%"
+    else:
+        result_text = "No detection"
+    
+    # Return annotated image AND the detection text
+    output_image = r.plot()
+    return Image.fromarray(output_image), result_text
 
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Image(type="pil", label="Detection"),
+        gr.Textbox(label="Result")
+    ],
     title="YOLOv8 Detection",
     description="Upload an image and detect objects"
 )
 
-demo.launch()
-print(model.names)
+demo.launch()