Delete app.py

app.py

@@ -1,35 +0,0 @@
-import torch
-import gradio as gr
-from torchvision import transforms
-from PIL import Image
-
-# Load model
-class MyModel(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        # Define layers here
-
-    def forward(self, x):
-        # Forward pass
-        return x
-
-model = MyModel()
-model.load_state_dict(torch.load("model.pth"))
-model.eval()
-
-# Define image preprocessing
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-])
-
-# Define prediction function
-def predict(image):
-    image = transform(image).unsqueeze(0)  # Add batch dimension
-    with torch.no_grad():
-        output = model(image)
-    return output.numpy().tolist()
-
-# Create Gradio interface
-iface = gr.Interface(fn=predict, inputs=gr.Image(), outputs="json")
-iface.launch()