app.py

import gradio as gr
import segmentation_models_pytorch as smp
import torch
import PIL as Image

#load our pytorch model:
model = smp.Unet(
    encoder_name="resnet34",        # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
    encoder_weights=None,     # use `imagenet` pre-trained weights for encoder initialization
    in_channels=3,                  # model input channels (1 for gray-scale images, 3 for RGB, etc.)
    classes=10,                      # model output channels (number of classes in your dataset)
)
model.load_state_dict(torch.load('Floodnet_model_e5.pt', map_location=torch.device('cpu')))
model.eval()

#handle input:
# output = lbm(sample.unsqueeze(dim=0).float()).detach().type(torch.int64)
# show(output.argmax(dim=1).squeeze())

def predict_segmentation(image: Image.Image):
    image = image.resize((256, 256))
    input_data = np.asarray(image)

    # Assuming the model expects a 4D input array
    input_data = input_data[np.newaxis, ...]

    # Get the prediction from the model
    output_data = model.predict(torch.from_numpy(input_data).float())

    # Assuming the output is a 3D array
    output_mask = output.argmax(dim=1).squeeze()

    # Convert the output_mask to an Image object
    output_image = output_mask#Image.fromarray(np.uint8(output_mask.numpy()))

    return output_image

image_input = gr.components.Image(shape=(256, 256), source="upload")
image_output = gr.components.Image(type="pil")

iface = gr.Interface(predict_segmentation, 'image', 'image')
iface.launch()