Add application file

app.py

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+import gradio as gr
+import torch
+import clip
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device=device)
+
+
+def predict(image):
+    labels = "Japanese, Chinese, Roman, Greek, Etruscan, Scandinavian, Celtic, Medieval, Victorian, Neoclassic, Romanticism, Art Nouveau, Art deco, Cyberpunk "
+    labels = labels.split(',')
+
+    image = preprocess(image).unsqueeze(0).to(device)
+    text = clip.tokenize([f"a character of origin {c}" for c in labels]).to(device)
+
+    with torch.inference_mode():
+        logits_per_image, logits_per_text = model(image, text)
+        probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+    return {k: float(v) for k, v in zip(labels, probs[0])}
+
+# probs = predict(Image.open("../CLIP/CLIP.png"), "cat, dog, ball")
+# print(probs)
+
+
+gr.Interface(fn=predict,
+             inputs=[
+                 gr.inputs.Image(label="Image to classify.", type="pil")],
+             theme="grass",
+             outputs="label",
+             description="Zero Shot Image classification..").launch()

requirements.txt

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+git+https://github.com/openai/CLIP
+torch
+Jinja2