Create app.py

app.py

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