Update app.py

app.py

@@ -22,6 +22,7 @@ dataset = stl.selectbox(
         "huggan/few-shot-art-painting",
         "huggan/wikiart",
         "zh-plus/tiny-imagenet",
+        "huggan/flowers-102-categories",
         "lambdalabs/naruto-blip-captions",
         "detection-datasets/fashionpedia",
         "fantasyfish/laion-art",
@@ -37,30 +38,43 @@ search_term = None
 ret_images = []
 scores = []
 
-
-if dataset and stl.button("embed image dataset"):
-    with stl.spinner("Initializing and creating image embeddings from dataset"):
-        embedder = ripple.ImageEmbedder(
+#@stl.cache_data
+def embed_data(dataset):
+    embedder = ripple.ImageEmbedder(
             dataset, retrieval_type="text-image", dataset_type="huggingface"
-        )
-
-        embedded_data = embedder.create_embeddings(device="cpu")
-        stl.success("Sucessfully embedded and dcreated image index")
+    )
+    embedded_data = embedder.create_embeddings(device="cpu")
+    return embedded_data
 
-if embedded_data is not None:
+@stl.cache_resource
+def init_search(embedded_data):
     text_search = ripple.TextSearch(embedded_data, embedder.embed_model)
     stl.success("Initialized text search class")
+    return text_search
+
+def get_images_from_description(description):
+    scores, ret_images = description, k_images=4)
+    return scores, ret_images
+
+if dataset and stl.button("embed image dataset"):
+    with stl.spinner("Initializing and creating image embeddings from dataset"):
+        embedded_data = embed_data(dataset)
+        stl.success("Successfully embedded and created image index")
 
+if embedded_data:
+    finder = init_search(embedded_data)
+    
     search_term = stl.text_input("Text description/search for image")
 
-    if search_term:
-        with stl.spinner("retrieving images with description.."):
-            scores, ret_images = text_search.get_similar_images(search_term, k_images=4)
-            stl.success(f"sucessfully retrieved {len(ret_images)} images")
+if search_term:
+    with stl.spinner("retrieving images with description.."):
+        scores, ret_images = get_images_from_description(search_term)
+        stl.success(f"sucessfully retrieved {len(ret_images)} images")
 
 try:
     for count, score, image in tqdm(zip(range(len(ret_images)), scores, ret_images)):
         stl.image(image["image"][count])
         stl.write(score)
-except Exceptio as e:
+        
+except Exception as e:
     st.error(e)