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| import gradio as gr | |
| import torch | |
| import numpy as np | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| with open('cnn_model.bin', 'rb') as f: | |
| nn = torch.load(f, map_location=torch.device('cpu')) | |
| nn.to(device) | |
| def predict(input): | |
| if input is None: | |
| return 'None' | |
| x = np.array([[input]]) | |
| x = torch.tensor(x).to(device) | |
| p = nn(x) | |
| p = p[0].cpu().detach().numpy() | |
| return dict(enumerate(p.tolist())) | |
| desc = """\ | |
| This project uses a Convolutional Neural Network to classify handwritten digits. | |
| Trained on the MNIST dataset. | |
| Use most of the drawing area for better results. | |
| """ | |
| demo = gr.Interface( | |
| fn=predict, | |
| title='ConvNet for handwritten digits classification', | |
| description=desc, | |
| inputs=[ | |
| gr.Sketchpad( | |
| shape=(28, 28), | |
| brush_radius=1.2, | |
| ) | |
| ], | |
| outputs=[ | |
| gr.Label( | |
| num_top_classes=3, | |
| scale=3, | |
| ) | |
| ], | |
| live=True, | |
| allow_flagging='never', | |
| ).launch() | |