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import gradio as gr
import torch
import torch.nn as nn
from torch.utils.data import Dataset
import torchvision
from torchvision import transforms
#from torchvision import transforms
from PIL import Image
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
self.relu2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
self.relu3 = nn.ReLU()
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
#self.fc1 = nn.Linear(in_features=262144, out_features=512)
#self.fc1 = nn.Linear(in_features=4096, out_features=512) # hr_pytorch_model.py
self.fc1 = nn.Linear(in_features=784, out_features=512)
self.relu4 = nn.ReLU()
self.fc2 = nn.Linear(in_features=512, out_features=2)
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.relu3(x)
x = self.pool3(x)
# Flatten
x = x.reshape(x.shape[0], -1) #this work
x = self.fc1(x)
x = self.relu4(x)
x = self.fc2(x)
return x
"""
transform = transforms.Compose(
[transforms.Pad(2),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
"""
# other transform
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
model = CNN()
#model.load_state_dict(torch.load('./best_model.nn'))
state_dict = torch.load('./pytorch_model.bin', map_location=torch.device('cpu'))
model.load_state_dict(state_dict, strict=False)
model.eval()
def predict(image):
img = Image.open(image)
img = transform(img)
print("===============", img.shape)
with torch.no_grad():
pred = model(img)
#is_ai = torch.max(pred.data, 0)[1]
#print("===============", is_ai)
probabilities = model(img).softmax(-1)[0,1].item()
print("=============== prob", probabilities)
return "AI" if probabilities > 0.3 else "Not AI"
"""
gr.Interface.load(
"huggingface/diallomama/AiorNot/blob/main/bes_model.nn",
inputs=gr.Textbox(lines=5, label="Input Text"),
outputs = "text"
).launch()
"""
gr.Interface(
predict,
inputs = gr.Image(label="Uploat an image", type="filepath"),
#outputs = gr.outputs.Label(num_top_classes=2)
outputs = "text"
).launch()
"""
gr.Interface(predict, inputs=gr.inputs.Image(shape=(512,512,3)), outputs="text").launch()
""" |