Update Demo.py

Demo.py

@@ -10,14 +10,6 @@ from model_loader import HFPretrainedModel
 from transformers import pipeline
 import torch
 
-@st.cache(hash_funcs={"MyUnhashableClass": lambda _: None})
-def load_sentiment_model():
-    return pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english")
-    
-@st.cache(hash_funcs={"MyUnhashableClass": lambda _: None})
-def load_campaign_model():
-    return HFPretrainedModel("distilbert-base-uncased", "deano/political-campaign-analysis-110922")
-
 if "session_id" not in st.session_state:
     st.session_state["session_id"] = uuid.uuid1()
     
@@ -42,14 +34,14 @@ if b:
     #upload_cap.caption("Extracting text from frames... (can take some time)")
     #text_ocr = ocr.get_formated_text(ocr.retrieve_text(TMP_PATH+"uploaded_video_tmp", frames_path = "tmp_frames-{"+str(st.session_state["session_id"])+"}", show_print = False))
     upload_cap.caption("Extracting text sentiment...")
-    sentiment_analysis = load_sentiment_model()
+    sentiment_analysis = pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english")
     text_sentiment = sentiment_analysis(text)[0]["label"]
     status_bar.progress(80)
     
     #shutil.rmtree(TMP_PATH)
     status_bar.progress(90)
     upload_cap.caption("Prediction...")
-    model = load_campaign_model()
+    model = HFPretrainedModel("distilbert-base-uncased", "deano/political-campaign-analysis-110922")
     #query_dict = {"text": [text], "text_ocr": [text_ocr]}
     query_dict = {"text": [text], "label_sentiment": [text_sentiment]}
     # Predicted confidence for each label