Update app.py
Browse files
app.py
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@@ -1,3 +1,4 @@
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import torch
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import gradio as gr
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from PIL import Image
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@@ -8,6 +9,9 @@ torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images
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model = torch.hub.load('pytorch/vision:v0.9.0', 'alexnet', pretrained=True)
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model.eval()
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def inference(input_image):
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preprocess = transforms.Compose([
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@@ -28,8 +32,6 @@ def inference(input_image):
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output = model(input_batch)
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# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
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probabilities = torch.nn.functional.softmax(output[0], dim=0)
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# Download ImageNet labels
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!wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
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# Read the categories
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with open("imagenet_classes.txt", "r") as f:
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categories = [s.strip() for s in f.readlines()]
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import os
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import torch
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import gradio as gr
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from PIL import Image
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model = torch.hub.load('pytorch/vision:v0.9.0', 'alexnet', pretrained=True)
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model.eval()
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# Download ImageNet labels
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os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
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def inference(input_image):
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preprocess = transforms.Compose([
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output = model(input_batch)
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# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
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probabilities = torch.nn.functional.softmax(output[0], dim=0)
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# Read the categories
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with open("imagenet_classes.txt", "r") as f:
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categories = [s.strip() for s in f.readlines()]
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