Update utils.py

utils.py

@@ -6,48 +6,8 @@ import grequests
 import requests
 
 import torch
-import clip
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-def encode_search_query(model, search_query):
-    with torch.no_grad():
-        tokenized_query = clip.tokenize(search_query)
-        # print("tokenized_query: ", tokenized_query.shape)
-        # Encode and normalize the search query using CLIP
-        text_encoded = model.encode_text(tokenized_query.to(device))
-        text_encoded /= text_encoded.norm(dim=-1, keepdim=True)
-
-        # Retrieve the feature vector
-        # print("text_encoded: ", text_encoded.shape)
-        return text_encoded
-
-
-def find_best_matches(text_features, photo_features, photo_ids, results_count=5):
-    # Compute the similarity between the search query and each photo using the Cosine similarity
-    # print("text_features: ", text_features.shape)
-    # print("photo_features: ", photo_features.shape)
-    similarities = (photo_features @ text_features.T).squeeze(1)
-
-    # Sort the photos by their similarity score
-    best_photo_idx = (-similarities).argsort()
-    # print("best_photo_idx: ", best_photo_idx.shape)
-    # print("best_photo_idx: ", best_photo_idx[:results_count])
-
-    result_list = [photo_ids[i] for i in best_photo_idx[:results_count]]
-    # print("result_list: ", len(result_list))
-    # Return the photo IDs of the best matches
-    return result_list
-
-
-def search_unslash(search_query, photo_features, photo_ids, results_count=10):
-    # Encode the search query
-    text_features = encode_search_query(search_query)
-
-    # Find the best matches
-    best_photo_ids = find_best_matches(text_features, photo_features, photo_ids, results_count)
-
-    return best_photo_ids
-
 
 
 def filter_invalid_urls(urls, photo_ids):
@@ -58,7 +18,8 @@ def filter_invalid_urls(urls, photo_ids):
     valid_image_urls = []
     for i, res in enumerate(results):
         if res and res.status_code == 200:
-            valid_image_urls.append(urls[i])
+            u = f"https://unsplash.com/photos/{photo_ids[i]}/download?w=100"
+            valid_image_urls.append(u)
             valid_image_ids.append(photo_ids[i])
 
     return dict(