app.py

import gradio as gr
from glob import glob
import os
import time

from PIL import Image
import albumentations as A
import cv2
import numpy as np
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import pandas as pd
from scipy.ndimage.morphology import binary_dilation
import segmentation_models_pytorch as smp
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms as T
from tqdm import tqdm
from tensorflow.keras.models import load_model

model = smp.MAnet(
    encoder_name="efficientnet-b7",
    encoder_weights="imagenet",
    in_channels=3,
    classes=1,
    activation='sigmoid',)

transform = A.Compose([
    A.ChannelDropout(p=0.3),
    A.RandomBrightnessContrast(p=0.3),
    A.ColorJitter(p=0.3),
])

model.load_state_dict(torch.load("weights.pt", map_location=torch.device('cpu')))
model.eval()

def segment(image):
    image = transform(image=image)
    image = image.get("image")
    image = T.functional.to_tensor(image)
    prediction = model(image[None, ...])
    prediction = np.squeeze(prediction.detach().numpy())
    return Image.fromarray(prediction)
iface = gr.Interface(fn=segment, inputs="image", outputs="image").launch()