from huggingface_hub import hf_hub_download
config_path=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M-sen1floods11", filename="sen1floods11_Prithvi_100M.py", token=os.environ.get("token"))
ckpt=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M-sen1floods11", filename='sen1floods11_Prithvi_100M.pth', token=os.environ.get("token"))
import numpy as np
import gradio as gr
from PIL import Image
import rasterio

def cargar_imagen_tif(tifile):
    try:
        with rasterio.open(tifile, "r") as src:
            data = src.read()
        tuki = Image.fromarray(data[0])  # Convierte el arreglo raster a una imagen PIL
        return convertir_a_blanco_y_negro(tuki)  # Captura el valor de retorno de la función
    except Exception as e:
        return f"Error al cargar la imagen TIFF: {str(e)}"

def convertir_a_blanco_y_negro(input_img):
    try:
        img_array = np.array(input_img)

        binary_img = np.zeros_like(img_array)

        color_threshold = 50

        for i in range(img_array.shape[0]):
            for j in range(img_array.shape[1]):
                pixel_color = img_array[i, j]
                if np.all(pixel_color <= color_threshold):
                    binary_img[i, j] = 0
                else:
                    binary_img[i, j] = 255

        binary_img = Image.fromarray(np.uint8(binary_img))

        return binary_img, "Hecho"
    except Exception as e:
        return f"Error al convertir a blanco y negro: {str(e)}"

demo = gr.Interface(
    fn=cargar_imagen_tif,
    inputs="file",
    outputs=["image", "text"],
    title="Conversión a Blanco y Negro",
    description="Carga una imagen TIFF y conviértela a blanco y negro."
)

demo.launch()