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import torch
from torch import nn
from torchvision import transforms
from torchvision.models import resnet50, ResNet50_Weights
import gradio as gr
title = "Cancer Detection"
description = "Image classification with histopathologic images"
article = "<p style='text-align: center'><a href='https://github.com/TirendazAcademy'>Github Repo</a></p>"
# The model architecture
class ImageClassifier(nn.Module):
def __init__(self):
super().__init__()
self.pretrain_model = resnet50(weights=ResNet50_Weights.DEFAULT)
self.pretrain_model.eval()
for param in self.pretrain_model.parameters():
param.requires_grad = False
self.pretrain_model.fc = nn.Sequential(
nn.Linear(self.pretrain_model.fc.in_features, 1024),
nn.ReLU(),
nn.Dropout(),
nn.Linear(1024,2)
)
def forward(self, input):
output=self.pretrain_model(input)
return output
model = ImageClassifier()
model.load_state_dict(torch.load('comet-torch-model.pth'))
def predict(inp):
image_transform = transforms.Compose([ transforms.Resize(size=(224,224)), transforms.ToTensor()])
labels = ['normal', 'cancer']
inp = image_transform(inp).unsqueeze(dim=0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp))
confidences = {labels[i]: float(prediction.squeeze()[i]) for i in range(len(labels))}
return confidences
gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=2),
title=title,
description=description,
article=article,
examples=['image-1.jpg', 'image-2.jpg']).launch() |