Update helper_functions.py

helper_functions.py

@@ -4,21 +4,44 @@ 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
 from fastapi import HTTPException
-from sentence_transformers import SentenceTransformer, util
-
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+from sentenceTranformer import SentenceEmbeddingPipeline
+from transformers import AutoTokenizer
+from main import logger
+
+# Initialize
+# model_path = "Abdul-Ib/all-MiniLM-L6-v2-2024"
+# semantic_model = SentenceTransformer(model_path, cache_folder="./assets")
+
+try:
+    # Load the semantic model
+    tokenizer = AutoTokenizer.from_pretrained("./assets/onnx")
+    model = ORTModelForFeatureExtraction.from_pretrained(
+        "./assets/onnx", file_name="model_quantized.onnx"
+    )
+    semantic_model = SentenceEmbeddingPipeline(model=model, tokenizer=tokenizer)
+except Exception as e:
+    raise HTTPException(
+        status_code=500,
+        detail=f"An error occurred during semantic model loading: {e}",
+    )
 
 # Initialization
-normalizer = Normalizer()
-model_path = "Abdul-Ib/all-MiniLM-L6-v2-2024"
-semantic_model = SentenceTransformer(model_path, cache_folder="./assets")
-categorizer = fasttext.load_model("./assets/categorization_pipeline.ftz")
-
-category_map = np.load("./assets/category_map.npy", allow_pickle=True).item()
+try:
+    normalizer = Normalizer()
+    categorizer = fasttext.load_model("./assets/categorization_pipeline.ftz")
+    category_map = np.load("./assets/category_map.npy", allow_pickle=True).item()
+except Exception as e:
+    raise HTTPException(
+        status_code=500,
+        detail=f"An error occurred during initialization of categorizer and normalizer: {e}",
+    )
 
 
 def make_request(url: str) -> dict: