Test1

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
+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)
+
+repo_id = "Ignaciobfp/segmentacion-dron-marras"
+
+learner = from_pretrained_fastai(repo_id)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
+model = learner.model
+model = model.cpu()
+
+
+
+# Definimos una función que se encarga de llevar a cabo las predicciones
+def predict(img):
+    #img = PILImage.create(img)
+    image = transforms.Resize((400,400))(img)
+    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 mask
+    
+# Creamos la interfaz y la lanzamos. 
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(400, 400)), outputs="image").launch(share=False)
+