fix pipeline referencement

main.py

@@ -19,7 +19,7 @@ from typing import Dict, List, Union
 from optimum.onnxruntime import ORTModelForSequenceClassification, ORTOptimizer, ORTQuantizer
 from optimum.onnxruntime.configuration import OptimizationConfig, AutoQuantizationConfig
 from optimum.pipelines import pipeline as ort_pipeline
-from transformers import BertTokenizer, BertForSequenceClassification, pipeline
+from transformers import BertTokenizer, BertForSequenceClassification, pt_pipeline
 
 from utils import calculate_inference_time
 
@@ -105,7 +105,7 @@ def load_pipeline(pipeline_name: str) -> None:
     """
     if pipeline_name == "pt_pipeline":
         model = BertForSequenceClassification.from_pretrained(HUB_MODEL_PATH, num_labels=3)
-        pipeline = pipeline("sentiment-analysis", tokenizer=st.session_state["tokenizer"], model=model)
+        pipeline = pt_pipeline("sentiment-analysis", tokenizer=st.session_state["tokenizer"], model=model)
     elif pipeline_name == "ort_pipeline":
         model = ORTModelForSequenceClassification.from_pretrained(HUB_MODEL_PATH, from_transformers=True)
         if not ONNX_MODEL_PATH.exists():
@@ -120,7 +120,7 @@ def load_pipeline(pipeline_name: str) -> None:
         model = ORTModelForSequenceClassification.from_pretrained(
             OPTIMIZED_BASE_PATH, file_name=OPTIMIZED_MODEL_PATH.name
         )
-        pipeline = pipeline("text-classification", tokenizer=st.session_state["tokenizer"], model=model)
+        pipeline = ort_pipeline("text-classification", tokenizer=st.session_state["tokenizer"], model=model)
     elif pipeline_name == "ort_quantized_pipeline":
         if not QUANTIZED_MODEL_PATH.exists():
             quantization_config = AutoQuantizationConfig.arm64(is_static=False, per_channel=False)
@@ -130,7 +130,7 @@ def load_pipeline(pipeline_name: str) -> None:
         model = ORTModelForSequenceClassification.from_pretrained(
             QUANTIZED_BASE_PATH, file_name=QUANTIZED_MODEL_PATH.name
         )
-        pipeline = pipeline("text-classification", tokenizer=st.session_state["tokenizer"], model=model)
+        pipeline = ort_pipeline("text-classification", tokenizer=st.session_state["tokenizer"], model=model)
     print(type(pipeline))
     return pipeline