Create main.py

main.py

@@ -0,0 +1,30 @@
+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
+
+
+def run():
+    demo = gr.Interface(
+        fn=predict,
+        inputs=gr.inputs.Image(type="pil"),
+        outputs=gr.outputs.Label(num_top_classes=3),
+    )
+
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+
+
+if __name__ == "__main__":
+    run()