Update app.py

app.py

@@ -5,22 +5,33 @@ from fastai.vision.all import *
 from fastai.learner import load_learner
 from PIL import Image
 
-# Load the learner
-repo_id ="LuisCe/Practica03"
+# Define la clase TargetMaskConvertTransform
+class TargetMaskConvertTransform(ItemTransform):
+    def __init__(self):
+        pass
+    def encodes(self, x):
+        img, mask = x
+        mask = np.array(mask)
+        mask[mask == 255] = 1
+        mask[mask == 150] = 2
+        mask[mask == 74] = 3
+        mask[mask == 76] = 3
+        mask[mask == 29] = 4
+        mask[mask == 25] = 4
+        mask = PILMask.create(mask)
+        return img, mask
 
+# Carga el modelo después de definir la clase
+repo_id = "LuisCe/Practica03"
 learner = from_pretrained_fastai(repo_id)
-# learn_inf = load_learner("LuisCe/Practica03")
 
-# Define the prediction function
+# Define la función de predicción
 def predict_image(img):
-    # Convert the PIL image to a format that fastai expects
     img_fastai = Image.fromarray(img.astype('uint8'), 'RGB')
-    # Make prediction
     pred, _, _ = learner.predict(img_fastai)
-    # Return prediction
     return pred
 
-# Create Gradio interface
+# Crea la interfaz Gradio
 gr.Interface(predict_image, 
               inputs="image", 
               outputs="text",