Update src/utils.py

src/utils.py

@@ -194,7 +194,8 @@ def offer_finder_by_category(search_input: str, search_category_tuple: Tuple, ca
     - search_input: a string
     - search_category_tuple: a tuple of (upper_category, product_category)
     - category_dict: a dictionary of categories. Keys are upper categories and values are lists of product categories
-    - offered_brands:  a dataframe of offers (OFFER, BRAND, RETAILER) that are avaialble in our database
+    - offers:  a dataframe of offers (OFFER, BRAND, RETAILER) that are avaialble in our database
+    - offered_brands: a list of offers from offer.df
     - brand_belong_category_dict: a dictionary of brands and the categories they belong to
     - score: a string of either 'cosine' or 'jaccard'
     - threshold: a float between 0 and 1
@@ -292,7 +293,8 @@ def search_offers(search_input: str=example_search, offers: pd.DataFrame=df_offe
        # check category
        cat_check = find_category(search_input, category_dict)
        if cat_check is None:
-           raise SearchFailedError('No brand/retailer/category is found. Please try again.')
+           print('No brand/retailer/category is found. Please try again.')
+           return None
        else:
             # we assume people just search one category at a time
             cat_tuple = cat_check # ('Alcohol', 'beer')
@@ -306,7 +308,8 @@ def search_offers(search_input: str=example_search, offers: pd.DataFrame=df_offe
             print('No offers matched retailer/category is found. Now trying to recommend based on category.')
             cat_check = find_category(search_input, category_dict)
             if cat_check is None:
-                raise SearchFailedError('No brand/retailer/category is found. Please try again.')
+                print('No brand/retailer/category is found. Please try again.')
+                return None
             else:
                 cat_tuple = cat_check
                 search_results = offer_finder_by_category(search_input, cat_tuple, category_dict, offers, offered_brands, brand_belong_category_dict, score, score_threshold)