Create app.py

app.py

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+from fastai.vision.all import *
+import gradio as gr
+import cloudpickle
+import numpy as np
+device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+# Cargamos el learner
+def load(f, map_location='cpu', pickle_module=pickle, **pickle_load_args):
+    with open ('modelomemes.pkl',mode='rb') as file:
+        learn=cloudpickle.load(file)
+    
+
+
+
+# Definimos una función que se encarga de llevar a cabo las predicciones
+def predict(img):
+    imgLAB = cv2.cvtColor(img.astype('uint8'), cv2.COLOR_BGR2LAB)
+    img_pred,a,b = learn.predict(imgLAB[:,:,0])
+    arrL = np.array(img_pred)[0,:,:]
+    arrA = np.array(img_pred)[1,:,:]
+    arrB = np.array(img_pred)[2,:,:]
+    imgP = np.stack((arrB,arrA,arrL),axis=2)
+    imgColorRGB = cv2.cvtColor(imgP.astype('uint8'), cv2.COLOR_LAB2BGR)
+    return(imgColorRGB)
+    
+# Creamos la interfaz y la lanzamos. 
+gr.Interface(fn=predict, inputs=gr.inputs.Image(), outputs=gr.outputs.Image()).launch(share=False)