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app.py

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+import os
+import numpy as np
+import torch
+
+import skimage
+from PIL import Image
+
+import open_clip
+
+import gradio as gr
+
+model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
+model.eval()  # model in train mode by default, impacts some models with BatchNorm or stochastic depth active
+tokenizer = open_clip.get_tokenizer('ViT-B-32')
+
+target_labels = ["page","chelsea","astronaut","rocket",
+                 "motorcycle_right","camera","horse","coffee",
+                 'logo']
+
+original_images = []
+images = []
+file_names = []
+
+for filename in [filename for filename in os.listdir(skimage.data_dir) if filename.endswith(".png") or filename.endswith(".jpg")]:
+    name = os.path.splitext(filename)[0]
+    if name not in target_labels:
+        continue
+
+    image = Image.open(os.path.join(skimage.data_dir, filename)).convert("RGB")
+
+    original_images.append(image)
+    images.append(preprocess(image))
+    file_names.append(filename)
+
+image_input = torch.tensor(np.stack(images))
+with torch.no_grad(), torch.cuda.amp.autocast():
+    image_features = model.encode_image(image_input).float()
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+
+
+def identify_image(input_description):
+    if input_description is None: return None
+    text_tokens = tokenizer([input_description])
+    with torch.no_grad(), torch.cuda.amp.autocast():
+        text_features = model.encode_text(text_tokens).float()
+        text_features /= text_features.norm(dim=-1, keepdim=True)
+    text_probs = (100.0 * image_features @ text_features.T)
+    top_probs, _ = text_probs.cpu().topk(1, dim=-1)
+    return original_images[top_probs.argmax().item()]
+
+with gr.Blocks() as demo:
+    gr.HTML("<h1 align = 'center'> Image Search </h1>")
+    gr.HTML("<h4 align = 'center'> Identify the most suitable image for description provided.</h4>")
+    
+    gr.Gallery(value = original_images,
+        label="Images to search from", show_label=True, elem_id="gallery"
+        , columns=[3], rows=[3], object_fit="contain", height="auto")
+    
+    content = gr.Textbox(label = "Enter search text here")
+    inputs = [
+            content,
+            ]
+    gr.Examples(["Page of text about segmentation",
+                "Facial photo of a tabby cat",
+                "Portrait of an astronaut with the American flag",
+                "Rocket standing on a launchpad",
+                "Red motorcycle standing in a garage",
+                "Person looking at a camera on a tripod",
+                "Black-and-white silhouette of a horse",
+                "Cup of coffee on a saucer",
+                 "A snake in the background"], 
+                inputs = inputs)
+    
+    generate_btn = gr.Button(value = 'Identify')
+    outputs  = [gr.Image(label = "Is this the image you are referring to?",
+                         height = 512, width = 512)]
+    generate_btn.click(fn = identify_image, inputs= inputs, outputs = outputs)
+
+## for collab
+# demo.launch(debug=True) 
+
+if __name__ == '__main__':
+    demo.launch() 

requirements.txt

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+scikit-image
+open_clip_torch
+pillow
+torch