simplify imports

app.py

@@ -1,23 +1,9 @@
 import gradio as gr
-from glob import glob
-import os
-import time
-
 from PIL import Image
-import albumentations as A
 import numpy as np
-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(
@@ -27,18 +13,10 @@ model = smp.MAnet(
     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())

requirements.txt

@@ -1,11 +1,6 @@
 Pillow
-albumentations
 numpy
-pandas
-scipy
 segmentation_models_pytorch
-sklearn
 torch
 torchvision
-tqdm
 tensorflow