Update app.py

app.py

@@ -15,6 +15,8 @@ def predict(image):
     # Run YOLOv8 inference
     results = model(img)
 
+    detected_objects = []  # Store detected object names and confidence
+
     # Draw bounding boxes on the image
     for r in results:
         for box in r.boxes:
@@ -23,19 +25,20 @@ def predict(image):
             cls = int(box.cls[0])  # Class index
             
             label = f"{model.names[cls]} {conf:.2f}"
+            detected_objects.append(label)  # Save object name and confidence
             cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
             cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
 
     # Convert back to PIL format
-    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB), "\n".join(detected_objects) if detected_objects else "No objects detected."
 
 # Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
+    outputs=[gr.Image(type="pil"), gr.Textbox(label="Detected Objects")],
     title="YOLOv8 Fruit Detection",
-    description="Upload an image and the model will detect objects."
+    description="Upload an image and the model will detect objects and list them."
 )
 
 # Launch the Gradio app