| import os | |
| import logging | |
| import torch | |
| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from transformers import pipeline | |
| sentiment_model_path = 'cardiffnlp/twitter-xlm-roberta-base-sentiment' | |
| def load_model(): | |
| keys = ['tokenizer', 'model', 'classifier'] | |
| if any(st.session_state.get(key) is None for key in keys): | |
| with st.spinner('Loading the model might take a couple of seconds...'): | |
| try: | |
| if os.environ.get('item-desirability'): | |
| model_path = 'magnolia-psychometrics/item-desirability' | |
| else: | |
| model_path = os.getenv('model_path') | |
| auth_token = os.environ.get('item-desirability') or True | |
| st.session_state.tokenizer = AutoTokenizer.from_pretrained( | |
| pretrained_model_name_or_path=model_path, | |
| use_fast=True, | |
| use_auth_token=auth_token | |
| ) | |
| st.session_state.model = AutoModelForSequenceClassification.from_pretrained( | |
| pretrained_model_name_or_path=model_path, | |
| num_labels=1, | |
| ignore_mismatched_sizes=True, | |
| use_auth_token=auth_token | |
| ) | |
| st.session_state.classifier = pipeline( | |
| task='sentiment-analysis', | |
| model=sentiment_model_path, | |
| tokenizer=sentiment_model_path, | |
| use_fast=False, | |
| top_k=3 | |
| ) | |
| logging.info('Loaded models and tokenizer!') | |
| except Exception as e: | |
| logging.error(f'Error while loading models/tokenizer: {e}') | |
| def z_score(y, mean=.04853076, sd=.9409466): | |
| return (y - mean) / sd | |
| def score_text(input_text): | |
| with st.spinner('Predicting...'): | |
| classifier_output = st.session_state.classifier(input_text) | |
| classifier_output_dict = {x['label']: x['score'] for x in classifier_output[0]} | |
| sentiment = classifier_output_dict['positive'] - classifier_output_dict['negative'] | |
| inputs = st.session_state.tokenizer(text=input_text, padding=True, return_tensors='pt') | |
| with torch.no_grad(): | |
| score = st.session_state.model(**inputs).logits.squeeze().tolist() | |
| desirability = z_score(score) | |
| return sentiment, desirability |