tweak how i normalize

app.py

@@ -37,7 +37,7 @@ def image_to_embedding(input_im):
     prepro = preprocess(input_im).unsqueeze(0).to(device)
     with torch.no_grad():
         image_embeddings = model.encode_image(prepro)
-    # image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
+    image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
     image_embeddings_np = image_embeddings.cpu().to(torch.float32).detach().numpy()
     return image_embeddings_np
 
@@ -45,7 +45,7 @@ def prompt_to_embedding(prompt):
     text = tokenizer([prompt]).to(device)
     with torch.no_grad():
         prompt_embededdings = model.encode_text(text)
-    # prompt_embededdings /= prompt_embededdings.norm(dim=-1, keepdim=True)
+    prompt_embededdings /= prompt_embededdings.norm(dim=-1, keepdim=True)
     prompt_embededdings_np = prompt_embededdings.cpu().to(torch.float32).detach().numpy()    
     return prompt_embededdings_np
 
@@ -90,12 +90,13 @@ def main(
         # dowload image
         import requests
         from io import BytesIO
-        response = requests.get(result["url"])
-        if not response.ok:
-            continue
         try:
+            response = requests.get(result["url"])
+            if not response.ok:
+                continue
             bytes = BytesIO(response.content)
             image = Image.open(bytes)
+            image.title = str(result["similarity"]) + ' ' + result["caption"]
             images.append(image)
         except Exception as e:
             print(e)
@@ -145,7 +146,7 @@ def update_average_embeddings(embedding_base64s_state, embedding_powers):
     # final_embedding = final_embedding / num_embeddings
     
     # normalize embeddings in numpy
-    # final_embedding /= np.linalg.norm(final_embedding)
+    final_embedding /= np.linalg.norm(final_embedding)
     
     embeddings_b64 = embedding_to_base64(final_embedding)
     return embeddings_b64
@@ -292,7 +293,7 @@ with gr.Blocks() as demo:
         with gr.Column(scale=5):
             gr.Markdown(
 """
-# Soho-Clip
+# Soho-Clip Embedding Explorer
 
 A tool for exploring CLIP embedding spaces.