Update app.py

app.py

@@ -1,14 +1,18 @@
-from huggingface_hub import from_pretrained_fastai
+
 import gradio as gr
+
 from fastai.basics import *
 from fastai.vision import models
 from fastai.vision.all import *
 from fastai.metrics import *
 from fastai.data.all import *
 from fastai.callback import *
+
 import PIL
 import torchvision.transforms as transforms
 
+# direct download
+
 from huggingface_hub import hf_hub_download
 hf_hub_download(repo_id="Alesteba/deep_model_03", filename="unet.pth")
 
@@ -30,6 +34,8 @@ def transform_image(image):
     
     return my_transforms(image_aux).unsqueeze(0).to(device)
 
+# Definimos una función que se encarga de llevar a cabo las predicciones
+
 def predict(img):
     img = PIL.Image.fromarray(img, "RGB")
     image = transforms.Resize((480,640))(img)
@@ -48,21 +54,10 @@ def predict(img):
     mask=np.reshape(mask,(480,640))
     return Image.fromarray(mask.astype('uint8'))
 
-# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
-# repo_id = "Alesteba/deep_model_03"
-
-# learner = from_pretrained_fastai(repo_id)
-# labels = learner.dls.vocab
-
-
-
-# # Definimos una función que se encarga de llevar a cabo las predicciones
-# def predict(img):
-#     #img = PILImage.create(img)
-#     pred,pred_idx,probs = learner.predict(img)
-#     return {labels[i]: float(probs[i]) for i in range(len(labels))}
-    
-# Creamos la interfaz y la lanzamos. 
-
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=[gr.outputs.Image(type="pil", label="Predicción")], examples=['color_154.jpg','color_155.jpg']).launch(share=False)
+gr.Interface(
+    fn=predict, 
+    inputs=gr.inputs.Image(shape=(128, 128)), 
+    outputs=[gr.outputs.Image(type="pil", label="Prediction")], 
+    examples=['color_154.jpg','color_155.jpg']
+).launch(share=False)