Create app.py

app.py

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+from huggingface_hub import from_pretrained_fastai
+import gradio as gr
+from fastai.vision.all import *
+import PIL
+import torchvision.transforms as transforms
+
+#repo_id = "Ignaciobfp/segmentacion-dron-marras"
+#learner = from_pretrained_fastai(repo_id)
+
+device = torch.device("cpu") 
+#model = learner.model
+model = torch.jit.load("pr3.pth")
+model = model.cpu()
+
+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_pil = PIL.Image.fromarray(img, 'RGB')
+    image = transforms.Resize((400,400))(img_pil)
+    tensor = transform_image(image=image)
+    model.to(device)
+    with torch.no_grad():
+        outputs = model(tensor)
+    outputs = torch.argmax(outputs,1)
+    mask = np.array(outputs.cpu())
+    mask[mask==1]=255
+    mask=np.reshape(mask,(400,400))
+    return Image.fromarray(mask.astype('uint8'))
+    
+# Creamos la interfaz y la lanzamos.
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(400, 400)), outputs=gr.outputs.Image(type="pil"), examples=['examples/1CA SUR_1200_800.png', 'examples/1CA SUR_4000_1200.png', 'examples/1CA SUR_4800_2000.png']).launch(share=False)
+                           
+