mav735/mri-assistent

1import gradio as gr2from model import get_results_model3from model import model_4import cv25 6IMAGES = 07 8 9def predict_image(image):10    global IMAGES11    paths = f'images/image_{IMAGES}.jpg'12    cv2.imwrite(paths, image)13    IMAGES += 114    result = get_results_model(paths, model_)15    if result[2] < 0.001:16        label_img = 'Unrecognised'17        pred_acc = ''18    else:19        label_img = result[1]20        pred_acc = f'Probability: &nbsp; **{(result[2] * 100):.2f} %**'21    return result[0], f'<font size="10"> Class: &nbsp; **{label_img}** &nbsp;&nbsp;&nbsp;&nbsp; {pred_acc}</font>'22 23 24with gr.Blocks() as demo:25    gr.Markdown('**<font size="10">MRI Assistant</font>**')26    with gr.Row():27        with gr.Column():28            image_input = gr.Image(label='MRI')29            label = gr.Markdown("")30        image_output = gr.Image(label='AI results')31 32    image_button = gr.Button("Predict results")33 34    gr.Markdown(r"""35                <font size="10">Social:</font>\36                &nbsp;&nbsp; <font size="7">*1.*</font>&nbsp;&nbsp; <font size="6"> [*Developers*](https://t.me/HenSolaris) </font>\37                &nbsp;&nbsp; <font size="7">*2.*</font>&nbsp;&nbsp; <font size="6"> [*Telegram bot*](https://t.me/Altsheimer_AI_bot) </font>38                """)39 40    image_button.click(predict_image, inputs=image_input, outputs=[image_output, label])41 42demo.launch()43 44print('launched!')45