Add spinner to search

app.py

@@ -36,21 +36,19 @@ model, processor = load_model()
 query = st.text_input("Enter your query here:")
 
 if st.button("Search"):
-    st.write(f"Searching our image database for {query}...")
-
-    inputs = processor(text=[query], images=None, return_tensors="jax", padding=True)
-
-    query_embedding = model.get_text_features(**inputs)
-    query_embedding = np.asarray(query_embedding)
-    query_embedding = query_embedding / np.linalg.norm(query_embedding, axis=-1, keepdims=True)
-    dot_prod = np.sum(np.multiply(query_embedding, image_embeddings), axis=1)
-    topk_images = dot_prod.argsort()[-k:]
-    matching_images = image_list[topk_images]
-    top_scores = 1. - dot_prod[topk_images]
-    #show images
-    
-    for img_path, score in zip(matching_images, top_scores):
-        img = plt.imread(os.path.join(img_dir, img_path))
-        st.image(img)
-        st.write(f"{img_path} ({score:.2f})", help="score")
+    with st.spinner(f"Searching ROCO test set for {query}..."):
+        inputs = processor(text=[query], images=None, return_tensors="jax", padding=True)
+
+        query_embedding = model.get_text_features(**inputs)
+        query_embedding = np.asarray(query_embedding)
+        query_embedding = query_embedding / np.linalg.norm(query_embedding, axis=-1, keepdims=True)
+        dot_prod = np.sum(np.multiply(query_embedding, image_embeddings), axis=1)
+        topk_images = dot_prod.argsort()[-k:]
+        matching_images = image_list[topk_images]
+        top_scores = 1. - dot_prod[topk_images]
+        #show images
+        for img_path, score in zip(matching_images, top_scores):
+            img = plt.imread(os.path.join(img_dir, img_path))
+            st.image(img)
+            st.write(f"{img_path} ({score:.2f})", help="score")