diff --git a/demo.py b/demo.py
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+++ b/demo.py
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+import os
+import clip
+import torch
+from torchvision.datasets import CIFAR100
+import gradio as gr
+
+# Load the model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, transform = clip.load("ViT-B/32", device=device)
+
+# Download the dataset
+cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False)
+
+def classify(img):
+    image = transform(img).unsqueeze(0).to(device)
+    text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device)
+
+    # Calculate features
+    with torch.no_grad():
+        image_features = model.encode_image(image)
+        text_features = model.encode_text(text_inputs)
+
+    # Pick the top 5 most similar labels for the image
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+    similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+    values, indices = similarity[0].topk(5)
+
+    text=""
+    # Print the result
+    for value, index in zip(values, indices):
+        text+=f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%\n"
+    return text
+
+inputs = gr.inputs.Image(type='pil', label="Original Image")
+outputs = gr.outputs.Textbox(type="str", label="Text Output")
+
+title = "CLIP"
+description = "CLIP demo"
+
+gr.Interface(classify, inputs, outputs, title=title, description=description).launch(debug=True)
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