/
app.py
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/
app.py
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import os
import torch
def prepare_model():
model = torch.nn.Sequential(
torch.nn.Linear(1, 100),
torch.nn.Dropout(0.01),
torch.nn.ReLU(),
torch.nn.Linear(100, 100),
torch.nn.Dropout(0.01),
torch.nn.ReLU(),
torch.nn.Linear(100, 100),
torch.nn.Dropout(0.01),
torch.nn.ReLU(),
torch.nn.Linear(100, 1),
)
return model
def load_model(model_dir):
model = prepare_model()
path = os.path.join(model_dir, 'model.pth')
model.load_state_dict(torch.load(path))
model.eval()
return model
def predict(model, x):
output = None
with torch.no_grad():
output = model(torch.Tensor([x]))
return output.numpy()[0]
def handler(event, context):
model = load_model("model")
x = event.get('queryStringParameters',{}).get('x', 0)
y = predict(model, float(x))
return str(y)
"""
if __name__ == "__main__":
event = {
'queryStringParameters': {
'x': 42
}
}
output = handler(event, context={})
print(output)
"""