Delete app.py

app.py

@@ -1,88 +0,0 @@
-import gradio as gr
-import torch
-import torch.nn as nn
-
-class CNN(nn.Module):
-    def __init__(self):
-        super(CNN, self).__init__()
-        
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
-        self.relu1 = nn.ReLU()
-        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
-        
-        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
-        self.relu2 = nn.ReLU()
-        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
-        
-        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
-        self.relu3 = nn.ReLU()
-        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
-        
-        self.fc1 = nn.Linear(in_features=262144, out_features=512)
-        self.relu4 = nn.ReLU()
-        
-        self.fc2 = nn.Linear(in_features=512, out_features=2)
-    
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.relu1(x)
-        x = self.pool1(x)
-        
-        x = self.conv2(x)
-        x = self.relu2(x)
-        x = self.pool2(x)
-        
-        x = self.conv3(x)
-        x = self.relu3(x)
-        x = self.pool3(x)
-        
-        # Flatten
-        x = x.reshape(x.shape[0], -1) #this work
-        
-        x = self.fc1(x)
-        x = self.relu4(x)
-        
-        x = self.fc2(x)
-        
-        return x
-
-# Convert dataset to PyTorch dataset
-class AiornotDataset(Dataset):
-    def __init__(self, image, transform=None):
-        self.image = image
-        self.transform = transform
-
-    def __getitem__(self, idx):
-        # Load image
-        #img_byte = BytesIO(self.dataset[idx]['image'].tobytes())
-        #img = self.dataset[idx]['image']
-        # Apply transform
-        if self.transform:
-            img = self.transform(img)
-        
-        # Load label
-        #label = self.dataset[idx]['label']
-        
-        return img
-        
-def predict(image, model):
-    img = AiornotDataset(image)
-    model = CNN()
-    model.load_state_dict(torch.load('./best_model.nn'))
-    model.eval()
-
-    pred = model(img)
-    is_ai = torch.max(pred.data, 0)[1]
-    if is_ai == 1:
-        return "The input image is generated by an AI"
-    return "The input image is not generated by an AI"
-    
-    
-
-
-gr.Interface(
-    predict,
-    inputs = gr.inpust.Image(label="Uploat an image", type="filepath"),
-    #outputs = gr.outputs.Label(num_top_classes=2)
-    outputs = "text"
-).lunch()