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Diego Carpintero
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Commit
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2fb361b
1
Parent(s):
31cd6a1
implement gradio app.py
Browse files
app.py
CHANGED
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import gradio as gr
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import gradio as gr
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import torch
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from model import *
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from PIL import Image
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import torchvision.transforms as transforms
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title = "Digit Classifier"
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description = (
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"Multilayer-Perceptron built for the fast.ai 'Deep Learning' course "
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"to classify handwritten digits from the MNIST dataset. "
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)
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inputs = gr.components.Image()
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outputs = gr.components.Label()
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examples = "examples"
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model = torch.load("model/digit_classifier.pt", map_location=torch.device("cpu"))
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labels = [str(i) for i in range(10)]
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transform = transforms.Compose(
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[
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transforms.Resize((28, 28)),
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transforms.Grayscale(),
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transforms.ToTensor(),
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transforms.Lambda(lambda x: x[0]),
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transforms.Lambda(lambda x: x.unsqueeze(0)),
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]
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)
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def predict_digit(img):
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img = transform(Image.fromarray(img))
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output = model(img)
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probs = torch.nn.functional.softmax(output, dim=1)
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return dict(zip(labels, map(float, probs.flatten()[:10])))
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with gr.Blocks() as demo:
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with gr.Tab("Digit Prediction"):
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gr.Interface(
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fn=predict_digit,
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inputs=inputs,
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outputs=outputs,
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examples=examples,
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title=title,
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description=description,
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).queue(default_concurrency_limit=5)
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demo.launch()
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