Delete aap.py

aap.py

@@ -1,66 +0,0 @@
-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 *
-
-
-from pathlib import Path
-import random
-
-import PIL
-import torchvision.transforms as transforms
-
-import gradio as gr
-
-
-# Cargamos el learner
-#learn = load_learner('export.pkl')
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
-model = torch.jit.load("unet.pth")
-model = model.cpu()
-model.eval()
-
-# Definimos las etiquetas de nuestro modelo
-#labels = learn.dls.vocab
-
-def transform_image(image):
-    my_transforms = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize(
-                                            [0.485, 0.456, 0.406],
-                                            [0.229, 0.224, 0.225])])
-    image_aux = 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 = PILImage.create(img)
-    
-    image = transforms.Resize((480,640))(img)
-    tensor = transform_image(image=image)
-    
-    with torch.no_grad():
-        outputs = model(tensor)
-
-    outputs = torch.argmax(outputs,1)
-    
-    mask = np.array(outputs.cpu())
-    mask[mask==0]=255 #grape
-    mask[mask==1]=150 #leaves
-    mask[mask==2]=76 #pole
-    mask[mask==2]=74 #pole
-    mask[mask==3]=29 #wood
-    mask[mask==3]=25 #wood
-
-    mask=np.reshape(mask,(480,640))
-    
-    return Image.fromarray(mask.astype('uint8'))
-    #pred,pred_idx,probs = learn.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(),examples=['color_154.jpg','color_155.jpg']).launch(share=False)