Update app.py

app.py

@@ -12,7 +12,7 @@ stl.write(
     "An app that uses text input to search for described images, using embeddings of selected image datasets. Uses contrastive learning models(CLIP) and the sentence-transformers"
 )
 stl.link_button(
-    label="link to github and full library code",
+    label="Full library code",
     url="https://github.com/kelechi-c/ripple_net",
 )
 
@@ -21,7 +21,7 @@ dataset = stl.selectbox(
     options=[
         "huggan/few-shot-art-painting",
         "huggan/wikiart",
-        "zh-plus/tiny-imagenet(",
+        "zh-plus/tiny-imagenet",
         "lambdalabs/naruto-blip-captions",
         "detection-datasets/fashionpedia",
         "fantasyfish/laion-art",
@@ -33,7 +33,7 @@ dataset = stl.selectbox(
 embedded_data = None
 embedder = None
 text_search = None
-
+search_term = None
 ret_images = []
 scores = []
 
@@ -51,15 +51,16 @@ if embedded_data is not None:
     text_search = ripple.TextSearch(embedded_data, embedder.embed_model)
     stl.success("Initialized text search class")
 
-search_term = stl.text_input("Text description/search for image")
+    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)}")
-
-for count, score, image in tqdm(zip(range(len(ret_images)), scores, ret_images)):
-    stl.image(image["image"][count])
-    stl.write(score)
+        scores, ret_images = text_search.get_similar_images(search_term, k_images=4)
+        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:
+    st.error(e)