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app.py
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import os
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import torch
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import torchvision.transforms as transforms
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import torch.nn.functional as F
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import gradio as gr
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from urllib.request import urlretrieve
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from model import Net
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# Loads latest model state from Github
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urlretrieve("https://github.com/equ1/mnist-interface/tree/main/saved_models")
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model_timestamps = [filename[10:-3]
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for filename in os.listdir("./saved_models")]
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latest_timestamp = max(model_timestamps)
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if torch.cuda.is_available():
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dev = "cuda:0"
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else:
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dev = "cpu"
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device = torch.device(dev)
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model = Net()
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model.load_state_dict(torch.load(f"./saved_models/mnist-cnn-{latest_timestamp}.pt", map_location=device))
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model.eval()
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# inference function
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def inference(img):
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transform = transforms.Compose([transforms.ToTensor(), transforms.Resize((28, 28))])
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img = transform(img).unsqueeze(0) # transforms ndarray and adds batch dimension
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with torch.no_grad():
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output_probabilities = F.softmax(model(img), dim=1)[0] # probability prediction for each label
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return {labels[i]: float(output_probabilities[i]) for i in range(len(labels))}
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# Creates and launches gradio interface
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labels = range(10) # 1-9 labels
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outputs = gr.outputs.Label(num_top_classes=5)
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gr.Interface(fn=inference, inputs='sketchpad', outputs=outputs, title="MNIST interface",
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description="Draw a number from 0-9 in the box and click submit to see the model's predictions.").launch()
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