Make gradio app

app.py

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+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()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2f7896ed91f6d20c6df42ddc5c846ca022837f65a7d0d4df713a8dea9a68261
+size 313745109

requirements.txt

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+glob
+os
+time
+PIL
+albumentations
+cv2
+numpy
+matplotlib
+pandas
+scipy
+segmentation_models_pytorch
+sklearn
+torch
+torchvision
+tqdm
+tensorflow

weights.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2f7896ed91f6d20c6df42ddc5c846ca022837f65a7d0d4df713a8dea9a68261
+size 313745109