import cv2
import numpy as np
import gradio as gr

from ultralytics import YOLO

def predict(path:str):
    model = YOLO("yolov8s.yaml")
    model = YOLO("best-underwater.pt")
    imagen = cv2.imread(path)
    results = model.predict(source=path)

    for r in results:
        return r.plot()

image_url = "https://huggingface.co/spaces/Jass0117/test/blob/main/diente.jpg"

css = """
.gradio-container {
    background-color: #ADD8E6; /* Fondo azul claro */
    background-image: url('{image_url}'), 
                      url('{image_url}'), 
                      url('{image_url}'), 
                      url('{image_url}');
    background-position: top left, top right, bottom left, bottom right; 
    background-repeat: no-repeat; 
}
"""

with gr.Blocks(css=css) as demo:
    # Encabezado
    gr.Markdown("<h1 style='text-align: center;'>Bienvenido al Identificador de Piezas Dentales</h1>"
                "<p style='text-align: center;'>Por favor, cargue la radiografía que desea analizar.</p>")

    # Contenedor para la carga de imágenes y resultados
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="filepath", label="Cargue su radiografía")
        with gr.Column():
            output_image = gr.Image(type="numpy", label="Análisis")

    analyze_button = gr.Button("Analizar")
    analyze_button.click(fn=predict, inputs=input_image, outputs=output_image)

demo.launch()