Update helper_functions.py

helper_functions.py

@@ -1,10 +1,8 @@
 # Import necessary libraries
 import requests
 import numpy as np
-import nest_asyncio
 import fasttext
 import torch
-nest_asyncio.apply()
 from typing import List
 from rank_bm25 import BM25L
 from normalizer import Normalizer
@@ -62,7 +60,7 @@ def full_text_search(query: str, keyword_search: BM25L) -> np.ndarray:
     - np.ndarray: The scores of the search results.
     """
     try:
-        tokenized_query = normalizer.translate_text(query).split(" ")
+        tokenized_query = query.split(" ")
         ft_scores = keyword_search.get_scores(tokenized_query)
         return ft_scores
     except Exception as e:
@@ -86,7 +84,7 @@ def semantic_search(query: str, doc_embeddings: torch.Tensor) -> torch.Tensor:
     """
     try:
         query_embedding = semantic_model.encode(
-            normalizer.translate_text(query), convert_to_tensor=True
+            query, convert_to_tensor=True
         )
         cos_sim = util.cos_sim(query_embedding, doc_embeddings)[0]
         return cos_sim
@@ -265,19 +263,4 @@ def is_cheapest(queries: list, request_json: list) -> None:
             status_code=500,
             detail=f"An error occurred during cheapest product identification: {e}",
         )
-        
-def check_keys(request_json: List[dict], required_keys: list):
-    """
-    Check if each dictionary in a list contains all the required keys.
-
-    Parameters:
-        request_json (list): A list of dictionaries to be checked.
-        required_keys (list): A list of keys that each dictionary must contain.
-
-    Returns:
-        bool: True if all dictionaries in the list contain all required keys, False otherwise.
-    """
-    for item in request_json:
-        if not all(key in item for key in required_keys):
-            raise HTTPException(status_code=400, detail=f"Missing keys in dictionary: {item}")