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()