Upload app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from detection import ObjectDetection
+
+examples = [
+    ['test-images/plant1.jpeg', 0.31],
+    ['test-images/plant2.jpeg', 0.51],
+    ['test-images/plant3.webp', 0.39],
+]
+
+def get_predictions(img, threshold, box_color, text_color):
+    v8_results = yolov8_detector.v8_score_frame(img)
+    v8_frame = yolov8_detector.plot_bboxes(v8_results, img, float(threshold), box_color, text_color)
+    return v8_frame
+
+with gr.Blocks(title="Leaf Disease Detection", theme=gr.themes.Monochrome()) as interface:
+    gr.Markdown("# Leaf Disease Detection")
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(shape=(416,416), label="Input Image")
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    box_color = gr.ColorPicker(label="Box Color", value="#0000ff")
+                with gr.Column():
+                    text_color = gr.ColorPicker(label="Prediction Color", value="#ff0000")
+
+            confidence = gr.Slider(maximum=1, step=0.01, value=0.4, label="Confidence Threshold", interactive=True)
+            btn = gr.Button("Detect")
+    
+    with gr.Row():        
+        with gr.Box():
+            v8_prediction = gr.Image(shape=(416,416), label="YOLOv8")
+
+        btn.click(
+            get_predictions,
+            [image, confidence, box_color, text_color],
+            [v8_prediction]
+        )
+    
+    with gr.Row():
+        gr.Examples(examples=examples, inputs=[image, confidence])
+
+
+yolov8_detector = ObjectDetection('yolov8')
+
+interface.launch()