Delete app.py

app.py

@@ -1,46 +0,0 @@
-import gradio as gr
-from detection import ObjectDetection
-
-examples = [
-    ['test-images/plant1.jpeg', 0.31],
-    ['test-images/plant1.jpeg', 0.51],
-    ['test-images/plant1.jpeg', 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()