import asyncio import gc import logging import os import pandas as pd import psutil import streamlit as st from PIL import Image from streamlit import components #from streamlit.caching import clear_cache from transformers import AutoModelForSequenceClassification, AutoTokenizer from transformers_interpret import SequenceClassificationExplainer os.environ["TOKENIZERS_PARALLELISM"] = "false" #logging.basicConfig( # format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO #) #def print_memory_usage(): # logging.info(f"RAM memory % used: {psutil.virtual_memory()[2]}") #@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=1) def load_model(model_name): return ( AutoModelForSequenceClassification.from_pretrained(model_name), AutoTokenizer.from_pretrained(model_name), ) print ("before main") def main(): st.title("Transformers Interpet Demo App") print ("before main") image = Image.open("./images/tight@1920x_transparent.png") st.sidebar.image(image, use_column_width=True) st.sidebar.markdown( "Check out the package on [Github](https://github.com/cdpierse/transformers-interpret)" ) 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) print ("Model loaded") if model_name.startswith("textattack/"): model.config.id2label = {0: "NEGATIVE (0) ", 1: "POSITIVE (1)"} model.eval() print ("Model Evaluated") cls_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer) print ("Model Explained") if cls_explainer.accepts_position_ids: emb_type_name = st.sidebar.selectbox( "Choose embedding type for attribution.", ["word", "position"] ) if emb_type_name == "word": emb_type_num = 0 if emb_type_name == "position": emb_type_num = 1 else: emb_type_num = 0 explanation_classes = ["predicted"] + list(model.config.label2id.keys()) explanation_class_choice = st.sidebar.selectbox( "Explanation class: The class you would like to explain output with respect to.", explanation_classes, ) my_expander = st.beta_expander( "Click here for a description of models and their tasks" ) with my_expander: st.json(models) # st.info("Max char limit of 350 (memory management)") text = st.text_area( "Enter text to be interpreted", "I like you, I love you", height=400, max_chars=850, ) print ("Before button") if st.button('Say hello'): st.write('Why hello there') else: st.write('Goodbye') print ("After test button") if st.button("Interpret Text"): #print_memory_usage() st.text("Output") with st.spinner("Interpreting your text (This may take some time)"): print ("Interpreting text") if explanation_class_choice != "predicted": word_attributions = cls_explainer( text, class_name=explanation_class_choice, embedding_type=emb_type_num, internal_batch_size=2, ) else: word_attributions = cls_explainer( text, embedding_type=emb_type_num, internal_batch_size=2 ) if word_attributions: print ("Word Attributions") word_attributions_expander = st.beta_expander( "Click here for raw word attributions" ) with word_attributions_expander: st.json(word_attributions) #components.v1.html( # cls_explainer.visualize()._repr_html_(), scrolling=True, height=350 #) print ("end of stuff") if __name__ == "__main__": main()