app.py

import torch
from torch import nn
from torchvision import transforms
import gradio as gr

title = "PyTorch Cat vs Dog"
description = "Classifying cats and dogs with Pytorch"
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.conv_layer_1 = nn.Sequential(
          nn.Conv2d(3, 64, 3, padding=1),
          nn.ReLU(),
          nn.BatchNorm2d(64),
          nn.MaxPool2d(2))
        self.conv_layer_2 = nn.Sequential(
          nn.Conv2d(64, 512, 3, padding=1),
          nn.ReLU(),
          nn.BatchNorm2d(512),
          nn.MaxPool2d(2))
        self.conv_layer_3 = nn.Sequential(
          nn.Conv2d(512, 512, kernel_size=3, padding=1),
          nn.ReLU(),
          nn.BatchNorm2d(512),
          nn.MaxPool2d(2)) 
        self.classifier = nn.Sequential(
          nn.Flatten(),
           nn.Linear(in_features=512*3*3, out_features=2)
        )
    def forward(self, x: torch.Tensor):
        x = self.conv_layer_1(x)
        x = self.conv_layer_2(x)
        x = self.conv_layer_3(x)
        x = self.conv_layer_3(x)
        x = self.conv_layer_3(x)
        x = self.conv_layer_3(x)
        x = self.classifier(x)
        return x

model = ImageClassifier()
model.load_state_dict(torch.load('image_classifier.pth'))

def predict(inp):
    image_transform = transforms.Compose([ transforms.Resize(size=(224,224)), transforms.ToTensor()])
    labels = ['cat', 'dog']
    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=['cat.jpg', 'dog.jpg']).launch()