test gradio

app.py

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+import gradio as gr
+import torch
+import requests
+from torchvision import transforms
+
+model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
+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
+
+demo = gr.Interface(fn=predict, 
+             inputs=gr.inputs.Image(type="pil"),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             examples=[["cheetah.jpg"]],
+             )
+             
+demo.launch()

requirements.txt

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+torch 
+Pillow
+torchvision