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app.py

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+import gradio as gr
+import torch
+import requests
+import os
+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=[],
+)
+
+basic_auth = (
+    "lenox",
+    os.environ["PROTOTYPE_PASSWORD"],
+)  # gets os.environ["PROTOTYPE_PASSWORD"] from hg repo secrets
+demo.launch(auth=basic_auth)

requirements.txt

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+gradio==3.40.1
+torch==2.0.1
+torchvision==0.15.2
+huggingface-hub==0.16.4