Create app.py

app.py

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+
+import clip
+import pickle
+import requests
+from PIL import Image
+import numpy as np
+
+
+is_gpu = False 
+device = CUDA(0) if is_gpu else "cpu"
+
+from datasets import load_dataset
+dataset = load_dataset("jamescalam/unsplash-25k-photos", split="train")
+
+emb_filename = 'unsplash-25k-photos-embeddings-indexes.pkl'
+with open(emb_filename, 'rb') as emb:
+        id2url, img_names, img_emb = pickle.load(emb)
+
+
+orig_clip_model, orig_clip_processor = clip.load("ViT-B/32", device=device, jit=False)
+
+def search(search_query):
+
+    with torch.no_grad():
+        # Encode and normalize the description using CLIP
+        text_encoded = orig_clip_model.encode_text(clip.tokenize(search_query))
+        text_encoded /= text_encoded.norm(dim=-1, keepdim=True)
+
+
+    # Retrieve the description vector
+    text_features = text_encoded.cpu().numpy()
+
+    # Compute the similarity between the descrption and each photo using the Cosine similarity
+    similarities = (text_features @ img_emb.T).squeeze(0)
+
+    # Sort the photos by their similarity score
+    best_photos = similarities.argsort()[::-1]
+    best_photos = best_photos[:15]
+    #best_photos = sorted(zip(similarities, range(img_emb.shape[0])), key=lambda x: x[0], reverse=True)
+
+    best_photo_ids = img_names[best_photos]
+
+    imgs = []
+
+    # Iterate over the top 5 results
+    for id in best_photo_ids:
+
+        id, _ = id.split('.')
+        url = id2url.get(id, "")
+        if url == "": continue
+
+        
+        r = requests.get(url + "?w=512", stream=True)
+        img = Image.open(r.raw)
+        #credits = f'Photo by <a href="https://unsplash.com/@{photo["photographer_username"]}?utm_source=NaturalLanguageImageSearch&utm_medium=referral">{photo["photographer_first_name"]} {photo["photographer_last_name"]}</a> on <a href="https://unsplash.com/?utm_source=NaturalLanguageImageSearch&utm_medium=referral">Unsplash</a>'
+        imgs.append(img)
+        #display(HTML(f'Photo by <a href="https://unsplash.com/@{photo["photographer_username"]}?utm_source=NaturalLanguageImageSearch&utm_medium=referral">{photo["photographer_first_name"]} {photo["photographer_last_name"]}</a> on <a href="https://unsplash.com/?utm_source=NaturalLanguageImageSearch&utm_medium=referral">Unsplash</a>'))
+    print()
+
+        if len(imgs) == 5: break
+
+    return imgs
+
+with gr.Blocks() as demo:
+    with gr.Column(variant="panel"):
+        with gr.Row(variant="compact"):
+            text = gr.Textbox(
+                label="Enter your prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+            ).style(
+                container=False,
+            )
+            search_btn = gr.Button("Search for images").style(full_width=False)
+
+        gallery = gr.Gallery(
+            label="Generated images", show_label=False, elem_id="gallery"
+        ).style(grid=[3,3,5], height="auto")
+
+    search_btn.click(search, text, gallery)
+
+demo.launch()