create app.py

app.py

@@ -0,0 +1,24 @@
+import torch
+import requests
+from PIL import Image
+from torchvision import transforms
+import gradio as gr
+
+
+
+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)