Intial

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+data

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+import torch
+import torch.nn as nn
+import model
+
+net = torch.load('mnist.pth')
+net.eval()
+
+def predict(img):
+    arr = np.array(img) / 255  # Assuming img is in the range [0, 255]
+    arr = np.expand_dims(arr, axis=0)  # Add batch dimension
+    arr = torch.from_numpy(arr).float()  # Convert to PyTorch tensor
+    output = net(arr)
+    topk_values, topk_indices = torch.topk(output, 2)  # Get the top 2 classes
+    return [str(k) for k in topk_indices[0].tolist()]
+
+
+sp = gr.Sketchpad(shape=(28, 28))
+
+gr.Interface(fn=predict,
+             inputs=sp,
+             outputs=['label','label']).launch()

model.py

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+import torch
+import torch.nn as nn
+# Define the model
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = nn.Linear(28*28, 128)  # MNIST images are 28x28
+        self.fc2 = nn.Linear(128, 64)
+        self.fc3 = nn.Linear(64, 10)  # There are 10 classes (0 through 9)
+
+    def forward(self, x):
+        x = x.view(x.shape[0], -1)  # Flatten the input
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        return self.fc3(x)

train.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+import model
+# Load the MNIST dataset
+train_set = torchvision.datasets.MNIST(root='./data', train=True,
+                                       download=True, transform=transforms.ToTensor())
+test_set = torchvision.datasets.MNIST(root='./data', train=False,
+                                      download=True, transform=transforms.ToTensor())
+
+train_loader = torch.utils.data.DataLoader(train_set, batch_size=32,
+                                           shuffle=True)
+test_loader = torch.utils.data.DataLoader(test_set, batch_size=32,
+                                          shuffle=False)
+
+
+
+net = model.Net()
+
+# Use CrossEntropyLoss for multi-class classification
+criterion = nn.CrossEntropyLoss()
+
+optimizer = optim.SGD(net.parameters(), lr=0.01)
+
+# Train the model
+for epoch in range(50):  # Loop over the dataset multiple times
+    for i, data in enumerate(train_loader, 0):
+        inputs, labels = data
+
+        optimizer.zero_grad()  
+        outputs = net(inputs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()  
+
+print('Finished Training')
+
+# Test the model
+correct = 0
+total = 0
+with torch.no_grad():
+    for data in test_loader:
+        images, labels = data
+        outputs = net(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+print(f'Accuracy of the network on test images: {100 * correct / total}%')
+
+torch.save(net,'mnist.pth')