Display scores

app.py

@@ -27,6 +27,7 @@ st.markdown("""Search for medical images with natural language powered by a CLIP
  [Radiology Objects in COntext (ROCO) dataset](https://github.com/razorx89/roco-dataset).""")
 st.markdown("""Example queries:
 * `ultrasound scans`
+* `pathology`
 * `PET scan`""")
 
 image_list, image_embeddings = load_image_embeddings()
@@ -43,11 +44,13 @@ if st.button("Search"):
     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)
-    matching_images = image_list[dot_prod.argsort()[-k:]]
+    topk_images = dot_prod.argsort()[-k:]
+    matching_images = image_list[topk_images]
+    top_scores = 1. - dot_prod[topk_images]
     #show images
     
-    for img_path in matching_images:
+    for img_path, score in zip(matching_images, top_scores):
         img = plt.imread(os.path.join(img_dir, img_path))
-        st.write(img_path)
         st.image(img)
+        st.write(f"{img_path} ({score:.2f})", help="score")