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import gradio as gr
import torch
from torch import nn
from PIL import Image
from torchvision import transforms
import numpy as np
class CustomModel(nn.Module):
def __init__(self, input_shape, num_classes):
super(CustomModel, self).__init__()
self.conv_layers = nn.Sequential(
nn.Conv2d(in_channels=input_shape[0], out_channels=32, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(kernel_size=2)
)
self.fc_layers = nn.Sequential(
nn.Flatten(),
nn.Dropout(0.5),
nn.Linear(128 * (input_shape[1] // 16) * (input_shape[2] // 16), 512),
nn.ReLU(),
nn.BatchNorm1d(512),
nn.Dropout(0.5),
nn.Linear(512, num_classes)
)
def forward(self, x):
x = self.conv_layers(x)
x = self.fc_layers(x)
return x
model = CustomModel(input_shape=(3,128,128), num_classes=2)
model.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
def predict(image):
preprocess = transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Ensure the image is a PIL Image
image = Image.fromarray(image.astype('uint8'), 'RGB')
x = preprocess(image).unsqueeze(0)
# Set model to evaluation mode
model.eval()
with torch.no_grad(): # Use no_grad context for inference to save memory and computations
x = model(x)
probabilities = torch.nn.functional.softmax(x, dim=1)[0]
#class_id = probabilities.argmax(dim=1).item()
cat_prob = probabilities[0]
dog_prob = probabilities[1]
return {
'cat': cat_prob.item(),
'dog': dog_prob.item()
}
#classes = ['cat', 'dog']
#return classes[class_id]
# Update Gradio interface
demo = gr.Interface(fn=predict, inputs="image", outputs="label")
demo.launch()
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