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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")
# load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.jit.load("unet.pth")
model = model.cpu()
def transform_image(image):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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.Image.fromarray(img, "RGB")
image = transforms.Resize((480,640))(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[mask==2]=150
mask[mask==3]=76
mask[mask==4]=29
mask=np.reshape(mask,(480,640))
return Image.fromarray(mask.astype('uint8'))
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)
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