delete initial app.py

app.py

@@ -1,24 +0,0 @@
-import torch
-import requests
-import gradio as gr
-from PIL import Image
-from torchvision import transforms
-
-
-model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
-
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
-
-def predict(inp):
-  inp = transforms.ToTensor()(inp).unsqueeze(0)
-  with torch.no_grad():
-    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
-  return confidences
-  
-gr.Interface(fn=predict, 
-             inputs=gr.inputs.Image(type="pil"),
-             outputs=gr.outputs.Label(num_top_classes=3),
-             examples=["lion.jpg", "cheetah.jpg"]).launch(share=True)