akhaliq HF staff commited on
Commit
679a7db
1 Parent(s): e562ca7

Create app.py

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  1. app.py +30 -0
app.py ADDED
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+ import torch
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+ from PIL import Image
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+ import torchvision.transforms as transforms
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+ import numpy as np
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+ import json
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+ import requests
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+ import matplotlib.pyplot as plt
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+
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+ import gradio as gr
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+
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+ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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+
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+ efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)
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+ utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
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+
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+ efficientnet.eval().to(device)
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+
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+ def inference(img):
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+ batch = torch.cat(
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+ [utils.prepare_input_from_uri(img)]
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+ ).to(device)
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+ with torch.no_grad():
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+ output = torch.nn.functional.softmax(efficientnet(batch), dim=1)
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+
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+
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+ results = utils.pick_n_best(predictions=output, n=5)
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+
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+ return results
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+
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+ gr.Interface(inference,gr.inputs.Image(type="file"),"text").launch()