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import os
import albumentations as A
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from albumentations.pytorch import ToTensorV2
from efficientnet_pytorch import EfficientNet
from PIL import Image
from sklearn import metrics
from torch import nn, optim
from torch.utils.data import DataLoader, Dataset
from torchvision import models
from tqdm import tqdm
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Dataset(Dataset):
def __init__(self, root_images, root_file, transform=None):
self.root_images = root_images
self.root_file = root_file
self.transform = transform
self.file = pd.read_csv(root_file)
def __len__(self):
return self.file.shape[0]
def __getitem__(self, index):
img_path = os.path.join(self.root_images, self.file["id"][index])
image = np.array(Image.open(img_path).convert("RGB"))
if self.transform is not None:
augmentations = self.transform(image=image)
image = augmentations["image"]
return image
learning_rate = 0.0001
batch_size = 32
epochs = 10
height = 224
width = 224
IMG = "AI images or Not/test"
FILE = "Data/sample_submission.csv"
def get_loader(image, file, batch_size, test_transform):
test_ds = Dataset(image, file, test_transform)
test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)
return test_loader
normalize = A.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.255], max_pixel_value=255.0
)
test_transform = A.Compose(
[A.Resize(width=width, height=height), normalize, ToTensorV2()]
)
class Net(nn.Module):
def __init__(self):
super().__init__()
self.model = EfficientNet.from_pretrained("efficientnet-b4")
self.fct = nn.Linear(1000, 1)
def forward(self, img):
x = self.model(img)
# print(x.shape)
x = self.fct(x)
return x
def load_checkpoint(checkpoint, model, optimizer):
print("====> Loading...")
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
# test = pd.read_csv(FILE)
# test
model = Net().to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
checkpoint_file = "Checkpoint/baseline_V0.pth.tar"
test_loader = get_loader(IMG, FILE, batch_size, test_transform)
checkpoint = torch.load(checkpoint_file, map_location=torch.device("cpu"))
load_checkpoint(checkpoint, model, optimizer)
model.eval()
# define the predict function
def predict(image):
# preprocess the image
image = np.array(image)
image = test_transform(image=image)["image"]
image = image.unsqueeze(0).to(device)
# get the model prediction
with torch.no_grad():
output = model(image)
pred = torch.sigmoid(output).cpu().numpy().squeeze()
# check if prediction is AI generated, not AI generated, or uncertain
if pred >= 0.6:
prediction = "AI generated"
confidence = pred
elif pred <= 0.4:
prediction = "NOT AI generated"
confidence = 1 - pred
else:
prediction = "uncertain"
confidence = abs(0.5 - pred) * 2
# return the prediction and confidence as a string
return f"This image is {prediction} with {confidence:.2%} confidence."
# define the input interface with examples
inputs = gr.inputs.Image(shape=(224, 224))
outputs = gr.outputs.Textbox()
examples = [
["Data/train/3.jpg"],
["Data/train/10.jpg"],
["Data/train/14.jpg"],
["Data/train/4515.jpg"],
["Data/train/4518.jpg"],
["Data/train/6122.jpg"],
["Data/train/6123.jpg"],
["Data/train/6124.jpg"],
["Data/train/6125.jpg"],
["Data/train/7461.jpg"],
["Data/train/7462.jpg"],
["Data/train/7463.jpg"],
["Data/train/7464.jpg"],
["Data/train/7465.jpg"],
["Data/train/8546.jpg"],
["Data/train/8543.jpg"],
["Data/train/9120.jpg"],
["Data/train/10120.jpg"],
]
iface = gr.Interface(
fn=predict,
inputs=inputs,
outputs=outputs,
title="AI image detector 🔎",
description="Check if an image is AI generated or real.",
examples=examples,
)
# launch the gradio app
iface.launch()
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