code added

app.py

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+import gradio as gr
+import torch
+import clip
+from PIL import Image
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device=device)
+
+def clip(image, text):
+    
+    image = preprocess(image).unsqueeze(0).to(device)
+    text = clip.tokenize([text]).to(device)
+
+    with torch.no_grad():
+        image_features = model.encode_image(image)
+        text_features = model.encode_text(text)
+        
+        logits_per_image, logits_per_text = model(image, text)
+        probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+    return probs[0]
+
+demo = gr.Interface(fn=clip, inputs=["text", "image"], outputs="text")
+demo.launch()

requirements.txt

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+pytorch
+torchvision
+# ftfy regex tqdm
+git+https://github.com/openai/CLIP.git