Update app.py

app.py

@@ -12,8 +12,15 @@ def get_predictions(img, threshold, box_color, text_color):
     v8_frame = yolov8_detector.plot_bboxes(v8_results, img, float(threshold), box_color, text_color)
     return v8_frame
 
-with gr.Blocks(title="Leaf Detection and Classification", theme=gr.themes.Monochrome()) as interface:
-    gr.Markdown("# Leaf Detection and Classification")
+# Load the YOLOv8 model for plant leaf detection and classification
+yolov8_detector = ObjectDetection('Yolov8')
+
+with gr.Blocks(title="Plant Leaf Detection and Classification", theme=gr.themes.DarkMode()) as interface:
+    # Add a header with a description of the app and the model used
+    gr.Markdown("# Plant Leaf Detection and Classification")
+    gr.Markdown("This app uses YOLOv8, a state-of-the-art object detection model, to detect and classify plant leaves. "
+                "The model can detect leaves and classify them into up to 45 different plant categories.")
+
     with gr.Row():
         with gr.Column():
             image = gr.Image(shape=(824, 824), label="Input Image")
@@ -29,7 +36,7 @@ with gr.Blocks(title="Leaf Detection and Classification", theme=gr.themes.Monoch
     
     with gr.Row():        
         with gr.Box():
-            v8_prediction = gr.Image(shape=(1024, 1024), label="YOLOv8")  # Change the shape to make the output image larger
+            v8_prediction = gr.Image(label="YOLOv8 Prediction")  # Display the output image in its original size
 
         btn.click(
             get_predictions,
@@ -40,7 +47,4 @@ with gr.Blocks(title="Leaf Detection and Classification", theme=gr.themes.Monoch
     with gr.Row():
         gr.Examples(examples=examples, inputs=[image, confidence])
 
-
-yolov8_detector = ObjectDetection('Yolov8')
-
 interface.launch()