Update app.py

app.py

@@ -1,22 +1,22 @@
-import numpy as np
-import gradio as gr
-from PIL import Image
-import scipy.misc
+import huggingface.transformers as transformers
+from huggingface.images import HfImage
+from huggingface.utils import HfArray
+from huggingface.raster import HfRaster
 
 def cargar_imagen_tif(tifile):
     try:
-        with open(tifile, 'rb') as f:
-            data = scipy.misc.imread(f, mode='L')
-        tuki = Image.fromarray(data)  # Convertir el arreglo raster a una imagen PIL
+        with HfRaster(tifile, "r") as src:
+            data = src.read()
+        tuki = HfImage.fromarray(data[0])  # Convierte el arreglo raster a una imagen HfImage
         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)
+        img_array = HfArray(input_img)
 
-        binary_img = np.zeros_like(img_array)
+        binary_img = HfArray.zeros_like(img_array)
 
         color_threshold = 50
 
@@ -28,13 +28,13 @@ def convertir_a_blanco_y_negro(input_img):
                 else:
                     binary_img[i, j] = 255
 
-        binary_img = Image.fromarray(np.uint8(binary_img))
+        binary_img = HfImage.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(
+demo = transformers. Interface(
     fn=cargar_imagen_tif,
     inputs="file",
     outputs=["image", "text"],