bnsapa commited on
Commit
51c7034
1 Parent(s): 972e87d

add code to app.py

Browse files
Files changed (1) hide show
  1. app.py +51 -3
app.py CHANGED
@@ -1,7 +1,55 @@
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  import gradio as gr
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- def greet(name):
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- return "Hello " + name + "!!"
 
 
 
 
 
 
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- iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  iface.launch()
 
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  import gradio as gr
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+ from torchvision import models
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+ import torch.nn as nn
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+ import torch
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+ import os
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+ from PIL import Image
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+ from torchvision.transforms import transforms
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+ from dotenv import load_dotenv
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+ load_dotenv()
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+ share = os.getenv("SHARE", False)
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+ pretrained_model = models.vgg19(pretrained=True)
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+ class NeuralNet(nn.Module):
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+ def __init__(self):
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+ super().__init__()
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+ self.model = nn.Sequential(
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+ pretrained_model,
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+ nn.Flatten(),
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+ nn.Linear(1000, 1),
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+ nn.Sigmoid()
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+ )
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+
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+ def forward(self, x):
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+ return self.model(x)
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+
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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+ model = NeuralNet()
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+
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+ model.load_state_dict(torch.load("mask_detection.pth", map_location=device))
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+
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+ model = model.to(device)
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+
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+ transform=transforms.Compose([
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+ transforms.Resize((150,150)),
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+ transforms.RandomHorizontalFlip(),
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+ transforms.ToTensor(),
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+ transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])
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+ ])
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+
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+ def greet(image):
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+ image = Image.fromarray(image.astype('uint8'), 'RGB')
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+ image.save("input.png")
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+ image = Image.open("input.png")
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+ input = transform(image).unsqueeze(0)
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+ output = model(input.to(device))
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+ probability = output.item()
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+ if probability < 0.5:
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+ return "Person in the pic has mask"
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+ else:
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+ return "Person in the pic does not have mask"
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+
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+ iface = gr.Interface(fn=greet, inputs="image", outputs="text")
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  iface.launch()