Delete app.py

app.py

@@ -1,38 +0,0 @@
-import gradio as gr
-import torchvision.transforms as transforms
-from PIL import Image
-import torch
-from timm.models import create_model
-def predict(input_img):
-    input_img =  Image.fromarray(np.uint8(input_img))
-    model1 = create_model(
-        'resnet50',
-        drop_rate=0.5,
-        num_classes=1,)
-    model2 = create_model(
-        'resnet50',
-        drop_rate=0.5,
-        num_classes=1,)
-    
-    loc = 'cuda:{}'.format(0)
-    checkpoint1 = torch.load("./machine_full_best.tar", map_location=loc)
-    model1.load_state_dict(checkpoint1['state_dict'])
-    checkpoint2 = torch.load("./human_full_best.tar", map_location=loc)
-    model2.load_state_dict(checkpoint2['state_dict'])
-    
-    my_transform = transforms.Compose([
-        transforms.RandomResizedCrop(224, (1, 1)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                            std=[0.229, 0.224, 0.225]),])
-    
-    input_img = my_transform(input_img).view(1,3,224,224)
-    model1.eval()
-    model2.eval()
-    result1 = round(model1(input_img).item(), 3)
-    result2 = round(model2(input_img).item(), 3)
-    result = 'MachineMem score = ' + str(result1) + ', HumanMem score = ' + str(result2) +'.'
-    return result
-
-demo = gr.Interface(predict, gr.Image(), "text")
-demo.launch(debug = True)