import gradio as gr

from detection_pipeline import DetectionModel

if gr.NO_RELOAD:
    model = DetectionModel()
    preds = []

def predict(image, threshold):
    global preds
    preds = model(image)
    return filter_preds(image, threshold)

def filter_preds(image, threshold):
    preds_ = list(filter(lambda x: x[4] > threshold/100, preds))
    output = model.visualize(image, preds_)
    return output


with gr.Blocks() as interface:
    with gr.Row():
        with gr.Column():
            image = gr.Image(label="Input", value="sample/1.jpg")

        with gr.Column():
            output = gr.Image(label="Output")

    with gr.Row():
        with gr.Column():        
            threshold = gr.Slider(0, 100, 30, step=5, label="Threshold")
            threshold.release(filter_preds, inputs=[image, threshold], outputs=output)
        with gr.Column():
            button = gr.Button(value="Detect")
            button.click(predict, [image, threshold], output)

if __name__ == "__main__":
    interface.launch()