Create app.py

app.py

@@ -0,0 +1,85 @@
+import os
+import random
+import gradio as gr
+import torch
+import clip
+import numpy as np
+import pandas as pd
+
+device = "mps" if torch.backends.mps.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device=device)
+print('Using ' + device)
+
+features_path = 'features/'
+
+
+
+photo_features = np.load(features_path + "features.npy")
+photo_ids = pd.read_csv(features_path+ "updated_file.csv")
+descriptions = list(photo_ids['description'])
+photo_filenames = list(photo_ids['photo_id'])
+
+
+
+def clip_search(search_string):
+        
+    with torch.no_grad():
+        # Encode and normalize the description using CLIP
+        text_encoded = model.encode_text(clip.tokenize(search_string).to(device))
+        text_encoded /= text_encoded.norm(dim=-1, keepdim=True)
+        # Retrieve the description vector and the photo vectors
+    text_features = text_encoded.cpu().numpy()
+
+    # Compute the similarity between the descrption and each photo using the Cosine similarity
+    similarities = list((text_features @ photo_features.T).squeeze(0))
+
+    # Sort the photos by their similarity score
+    candidates = sorted(zip(similarities, range(photo_features.shape[0])), key=lambda x: x[0], reverse=True)
+    
+    images = []
+    for i in range(30):
+        # Retrieve the photo ID
+        idx = candidates[i][1]
+        photo_id = photo_filenames[idx]
+        caption = descriptions[idx]
+
+        images.append([('images/' + str(photo_id)),  caption])
+    return images
+
+css = "footer {display: none !important;} .gradio-container {min-height: 0px !important;}"
+with gr.Blocks(css = css) as demo:
+    with gr.Column(variant="panel"):
+        with gr.Row(variant="compact"):
+            search_string = gr.Textbox(
+                label="Evocative Search",
+                show_label=True,
+                max_lines=1,
+                placeholder="Type something abstruse, or click a suggested search below.",
+            ).style(
+                container=False,
+            )
+            btn = gr.Button("Retrieve Images", variant="primary").style(full_width=False)
+        with gr.Row(variant="compact"):
+            suggest1 = gr.Button("rococo", variant="secondary").style(size="sm")    
+            suggest2 = gr.Button("brutalism", variant="secondary").style(size="sm")    
+            suggest3 = gr.Button("classical", variant="secondary").style(size="sm")       
+            suggest4 = gr.Button("gothic", variant="secondary").style(size="sm")    
+            suggest5 = gr.Button("foliate", variant="secondary").style(size="sm")   
+        gallery = gr.Gallery(
+            label=False, show_label=False, elem_id="gallery"
+        ).style(grid=[6], height="100%",)
+
+    suggest1.click(clip_search, inputs=suggest1, outputs=gallery)
+    suggest2.click(clip_search, inputs=suggest2, outputs=gallery)
+    suggest3.click(clip_search, inputs=suggest3, outputs=gallery)
+    suggest4.click(clip_search, inputs=suggest4, outputs=gallery)
+    suggest5.click(clip_search, inputs=suggest5, outputs=gallery)
+    btn.click(clip_search, inputs=search_string, outputs=gallery)
+    search_string.submit(clip_search, search_string, gallery)
+
+
+
+if __name__ == "__main__":
+    demo.launch(share=False, server_name='0.0.0.0')
+demo.close()
+#     demo.launch()