Update app.py

app.py

@@ -1,51 +1,11 @@
 import gradio as gr
 import torch
-import asyncio
 from helper_functions import *
 from rank_bm25 import BM25L
-import nest_asyncio
 import time
-nest_asyncio.apply()
-from aiogoogletrans import Translator
 import pprint
-
-# Initialize the translator
-translator = Translator()
-
-async def translate_bulk(bulk: list) -> list:
-    """
-    Translate the given text to English and return the translated text.
-
-    Args:
-    - text (str): The text to translate.
-
-    Returns:
-    - str: The translated text.
-    """
-    try:
-        translated_bulk = await translator.translate(bulk, dest="en")
-        translated_bulk = [
-            translated_text.text.lower().strip() for translated_text in translated_bulk
-        ]
-    except Exception as e:
-        print(f"Bulk Translation failed: {e}")
-        translated_bulk = [
-            text.lower().strip() for text in bulk
-        ]  # Use original text if translation fails
-    return translated_bulk
-
-async def encode_document(document: str):
-    """_summary_
-
-    Args:
-        document (str): _description_
-
-    Returns:
-        _type_: _description_
-    """
-    return semantic_model.encode(document, convert_to_tensor=True)
         
-async def predict(query):
+def predict(query):
     start_time = time.time()
     normalized_query_list = (
         [normalizer.clean_text(query)]
@@ -102,14 +62,8 @@ async def predict(query):
         categorize_time = categorize_end_time - categorize_start_time
     except Exception as e: 
         return {"error": f"An error occurred while categorizing products: {e}"}
-
-    try:
-        translation_start_time = time.time()
-        representation_list = await translate_bulk(tasks)
-    except Exception as e:
-        representation_list = tasks
-        print(f"An error occurred while translating: {e}")
-    translation_time = time.time() - translation_start_time
+    
+    representation_list = tasks
     
     try:
         # Tokenize representations for keyword search
@@ -123,16 +77,9 @@ async def predict(query):
     
     # Encode representations for semantic search
     encode_start_time = time.time()
-    try:
-        embeddings = await asyncio.gather(
-            *[encode_document(document) for document in representation_list]
-        )
-        doc_embeddings = torch.stack(embeddings)
-    except Exception as e:
-        doc_embeddings = semantic_model.encode(
+    doc_embeddings = semantic_model.encode(
             representation_list, convert_to_tensor=True
         )
-        print(f"An error occurred while encoding documents: {e}")
     encode_end_time = time.time()
     encode_time = encode_end_time - encode_start_time
     
@@ -159,8 +106,7 @@ async def predict(query):
 
         hits = {"results": results, "time_taken": time_taken, "normalize_query_time": normalize_query_time,
                 "request_time": request_time, "normalization_time": normalization_time,
-                "translation_time": translation_time, "categorize_time": categorize_time, 
-                "tokenization_time": tokenization_time, "encode_time": encode_time, 
+                "categorize_time": categorize_time, "tokenization_time": tokenization_time, "encode_time": encode_time, 
                 "calculate_interrelations_time": calculate_interrelations_time,
                 "process_time": process_time_taken}