Update app.py

app.py

@@ -45,24 +45,6 @@ st.info(
     "Due to limited resources only low memory models are available. Run this [app locally](https://github.com/cdpierse/transformers-interpret-streamlit) to run the full selection of available models. "
 )
 
-# uncomment the options below to test out the app with a variety of classification models.
-models = {
-    # "textattack/distilbert-base-uncased-rotten-tomatoes": "",
-    # "textattack/bert-base-uncased-rotten-tomatoes": "",
-    # "textattack/roberta-base-rotten-tomatoes": "",
-    # "mrm8488/bert-mini-finetuned-age_news-classification": "BERT-Mini finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
-    # "nateraw/bert-base-uncased-ag-news": "BERT finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
-    "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT model finetuned on SST-2 sentiment analysis task. Predicts positive/negative sentiment.",
-    # "ProsusAI/finbert": "BERT model finetuned to predict sentiment of financial text. Finetuned on Financial PhraseBank data. Predicts positive/negative/neutral.",
-    "sampathkethineedi/industry-classification": "DistilBERT Model to classify a business description into one of 62 industry tags.",
-    "MoritzLaurer/policy-distilbert-7d": "DistilBERT model finetuned to classify text into one of seven political categories.",
-    # # "MoritzLaurer/covid-policy-roberta-21": "(Under active development ) RoBERTA model finetuned to identify COVID policy measure classes ",
-    # "mrm8488/bert-tiny-finetuned-sms-spam-detection": "Tiny bert model finetuned for spam detection. 0 == not spam, 1 == spam",
-}
-model_name = st.sidebar.selectbox(
-    "Choose a classification model", list(models.keys())
-)
-model, tokenizer = load_model(model_name)