Create app.py

app.py

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+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)