app.py

import os
import torch
import gradio as gr
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

# This is just to show an interface where one draws a number and gets prediction. 

n_epochs = 10
batch_size_train = 128
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 10
random_seed = 1
TRAIN_CUTOFF = 10
MODEL_PATH = 'weights' 
os.makedirs(MODEL_PATH,exist_ok=True)
METRIC_PATH = os.path.join(MODEL_PATH,'metrics.json')
MODEL_WEIGHTS_PATH = os.path.join(MODEL_PATH,'mnist_model.pth')
OPTIMIZER_PATH = os.path.join(MODEL_PATH,'optimizer.pth')
REPOSITORY_DIR = "data"
LOCAL_DIR = 'data_local'




HF_TOKEN = os.getenv("HF_TOKEN")
MODEL_REPO = 'mnist-adversarial-model'
HF_DATASET ="mnist-adversarial-dataset"
DATASET_REPO_URL = f"https://huggingface.co/datasets/chrisjay/{HF_DATASET}"
MODEL_REPO_URL = f"https://huggingface.co/model/chrisjay/{MODEL_REPO}"


torch.backends.cudnn.enabled = False
torch.manual_seed(random_seed)



TRAIN_TRANSFORM = torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.1307,), (0.3081,))
                             ])



# Source: https://nextjournal.com/gkoehler/pytorch-mnist
class MNIST_Model(nn.Module):
    def __init__(self):
        super(MNIST_Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)

train_loader = torch.utils.data.DataLoader(
  torchvision.datasets.MNIST('files/', train=True, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 mean=(0.1307,), std=(0.3081,))
                             ])),
  batch_size=batch_size_train, shuffle=True)

test_loader = torch.utils.data.DataLoader(
  torchvision.datasets.MNIST('files/', train=False, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.1307,), (0.3081,))
                             ])),
  batch_size=batch_size_test, shuffle=True)

def train(epoch,network,optimizer,train_loader):
    
    train_losses=[]
    network.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = network(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
            epoch, batch_idx * len(data), len(train_loader.dataset),
            100. * batch_idx / len(train_loader), loss.item()))
            train_losses.append(loss.item())
    
            torch.save(network.state_dict(), MODEL_WEIGHTS_PATH)
            torch.save(optimizer.state_dict(), OPTIMIZER_PATH)

def test():
    test_losses=[]
    network.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = network(data)
            test_loss += F.nll_loss(output, target, size_average=False).item()
            pred = output.data.max(1, keepdim=True)[1]
            correct += pred.eq(target.data.view_as(pred)).sum()
            test_loss /= len(test_loader.dataset)
        test_losses.append(test_loss)
        acc = 100. * correct / len(test_loader.dataset)
        acc = acc.item()
        test_metric = '〽Current test metric -> Avg. loss: `{:.4f}`, Accuracy: `{:.0f}%`\n'.format(
        test_loss,acc)
        print(test_metric)
        return test_metric,acc



random_seed = 1
torch.backends.cudnn.enabled = False
torch.manual_seed(random_seed)

network = MNIST_Model() #Initialize the model with random weights
optimizer = optim.SGD(network.parameters(), lr=learning_rate,
                      momentum=momentum)        


model_state_dict = MODEL_WEIGHTS_PATH
optimizer_state_dict = OPTIMIZER_PATH
if os.path.exists(model_state_dict) and os.path.exists(optimizer_state_dict):
    network_state_dict = torch.load(model_state_dict)
    network.load_state_dict(network_state_dict)

    optimizer_state_dict = torch.load(optimizer_state_dict)
    optimizer.load_state_dict(optimizer_state_dict)   
# Train

#for epoch in range(n_epochs):

#  train(epoch,network,optimizer,train_loader)
#  test()


def image_classifier(inp):
    """
    It takes an image as input and returns a dictionary of class labels and their corresponding
    confidence scores.
    
    :param inp: the image to be classified
    :return: A dictionary of the class index and the confidence value.
    """
    input_image = torchvision.transforms.ToTensor()(inp).unsqueeze(0)
    with torch.no_grad():

        prediction = torch.nn.functional.softmax(network(input_image)[0], dim=0)
        #pred_number = prediction.data.max(1, keepdim=True)[1]
        sorted_prediction = torch.sort(prediction,descending=True)
        confidences={}
        for s,v in zip(sorted_prediction.indices.numpy().tolist(),sorted_prediction.values.numpy().tolist()):
            confidences.update({s:v})
        return confidences




def main():
    block = gr.Blocks()

    with block:

        with gr.Row():     
    

            image_input =gr.inputs.Image(source="canvas",shape=(28,28),invert_colors=True,image_mode="L",type="pil")
            label_output = gr.outputs.Label(num_top_classes=10)
        
        image_input.change(image_classifier,inputs = [image_input],outputs=[label_output])
        


    block.launch()  
        
     


if __name__ == "__main__":
    main()