unt2tled commited on
Commit
4f0eb76
1 Parent(s): b82d52c

Update Demo.py

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Files changed (1) hide show
  1. Demo.py +2 -10
Demo.py CHANGED
@@ -10,14 +10,6 @@ from model_loader import HFPretrainedModel
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  from transformers import pipeline
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  import torch
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- @st.cache(hash_funcs={"MyUnhashableClass": lambda _: None})
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- def load_sentiment_model():
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- return pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english")
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-
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- @st.cache(hash_funcs={"MyUnhashableClass": lambda _: None})
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- def load_campaign_model():
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- return HFPretrainedModel("distilbert-base-uncased", "deano/political-campaign-analysis-110922")
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-
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  if "session_id" not in st.session_state:
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  st.session_state["session_id"] = uuid.uuid1()
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@@ -42,14 +34,14 @@ if b:
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  #upload_cap.caption("Extracting text from frames... (can take some time)")
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  #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))
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  upload_cap.caption("Extracting text sentiment...")
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- sentiment_analysis = load_sentiment_model()
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  text_sentiment = sentiment_analysis(text)[0]["label"]
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  status_bar.progress(80)
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  #shutil.rmtree(TMP_PATH)
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  status_bar.progress(90)
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  upload_cap.caption("Prediction...")
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- model = load_campaign_model()
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  #query_dict = {"text": [text], "text_ocr": [text_ocr]}
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  query_dict = {"text": [text], "label_sentiment": [text_sentiment]}
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  # Predicted confidence for each label
 
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  from transformers import pipeline
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  import torch
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  if "session_id" not in st.session_state:
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  st.session_state["session_id"] = uuid.uuid1()
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  #upload_cap.caption("Extracting text from frames... (can take some time)")
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  #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))
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  upload_cap.caption("Extracting text sentiment...")
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+ sentiment_analysis = pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english")
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  text_sentiment = sentiment_analysis(text)[0]["label"]
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  status_bar.progress(80)
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  #shutil.rmtree(TMP_PATH)
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  status_bar.progress(90)
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  upload_cap.caption("Prediction...")
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+ model = HFPretrainedModel("distilbert-base-uncased", "deano/political-campaign-analysis-110922")
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  #query_dict = {"text": [text], "text_ocr": [text_ocr]}
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  query_dict = {"text": [text], "label_sentiment": [text_sentiment]}
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  # Predicted confidence for each label