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Runtime error
| import streamlit as st | |
| import tensorflow as tf | |
| from transformers import pipeline | |
| from textblob import TextBlob | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| import torch.nn.functional as F | |
| textIn = st.text_input("Input Text Here:", "I really like the color of your car!") | |
| option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('MILESTONE 3', 'Pipeline', 'TextBlob')) | |
| st.write('You selected:', option) | |
| if option == 'MILESTONE 3': | |
| st.write('test1') | |
| model_name_0 = "Rathgeberj/milestone3_0" | |
| model_0 = AutoModelForSequenceClassification.from_pretrained(model_name_0) | |
| tokenizer_0 = AutoTokenizer.from_pretrained(model_name_0) | |
| classifier_0 = pipeline(task="sentiment-analysis", model=model_0, tokenizer=tokenizer_0) | |
| model_name_1 = "Rathgeberj/milestone3_1" | |
| model_1 = AutoModelForSequenceClassification.from_pretrained(model_name_1) | |
| tokenizer_1 = AutoTokenizer.from_pretrained(model_name_1) | |
| classifier_1 = pipeline(task="sentiment-analysis", model=model_1, tokenizer=tokenizer_1) | |
| model_name_2 = "Rathgeberj/milestone3_2" | |
| model_2 = AutoModelForSequenceClassification.from_pretrained(model_name_2) | |
| tokenizer_2 = AutoTokenizer.from_pretrained(model_name_2) | |
| classifier_2 = pipeline(task="sentiment-analysis", model=model_2, tokenizer=tokenizer_2) | |
| model_name_3 = "Rathgeberj/milestone3_3" | |
| model_3 = AutoModelForSequenceClassification.from_pretrained(model_name_3) | |
| tokenizer_3 = AutoTokenizer.from_pretrained(model_name_3) | |
| classifier_3 = pipeline(task="sentiment-analysis", model=model_3, tokenizer=tokenizer_3) | |
| model_name_4 = "Rathgeberj/milestone3_4" | |
| model_4 = AutoModelForSequenceClassification.from_pretrained(model_name_4) | |
| tokenizer_4 = AutoTokenizer.from_pretrained(model_name_4) | |
| classifier_4 = pipeline(task="sentiment-analysis", model=model_4, tokenizer=tokenizer_4) | |
| model_name_5 = "Rathgeberj/milestone3_5" | |
| model_5 = AutoModelForSequenceClassification.from_pretrained(model_name_5) | |
| tokenizer_5 = AutoTokenizer.from_pretrained(model_name_5) | |
| classifier_5 = pipeline(task="sentiment-analysis", model=model_5, tokenizer=tokenizer_5) | |
| models = [model_0, model_1, model_2, model_3, model_4, model_5] | |
| tokenizers = [tokenizer_0, tokenizer_1, tokenizer_2, tokenizer_3, tokenizer_4, tokenizer_5] | |
| classifiers = [classifier_0, classifier_1, classifier_2, classifier_3, classifier_4, classifier_5] | |
| # X_train = [textIn] | |
| # batch = tokenizer(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt") | |
| st.write('test2') | |
| if option == 'Pipeline': | |
| model_name = "distilbert-base-uncased-finetuned-sst-2-english" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| classifier = pipeline(task="sentiment-analysis", model=model, tokenizer=tokenizer) | |
| preds = classifier(textIn) | |
| preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds] | |
| st.write('According to Pipeline, input text is ', preds[0]['label'], ' with a confidence of ', preds[0]['score']) | |
| if option == 'TextBlob': | |
| polarity = TextBlob(textIn).sentiment.polarity | |
| subjectivity = TextBlob(textIn).sentiment.subjectivity | |
| sentiment = '' | |
| if polarity < 0: | |
| sentiment = 'Negative' | |
| elif polarity == 0: | |
| sentiment = 'Neutral' | |
| else: | |
| sentiment = 'Positive' | |
| st.write('According to TextBlob, input text is ', sentiment, ' and a subjectivity score (from 0 being objective to 1 being subjective) of ', subjectivity) | |
| #------------------------------------------------------------------------ | |
| # tokens = tokenizer.tokenize(textIn) | |
| # token_ids = tokenizer.convert_tokens_to_ids(tokens) | |
| # input_ids = tokenizer(textIn) | |
| # X_train = [textIn] | |
| # batch = tokenizer(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt") | |
| # # batch = torch.tensor(batchbatch["input_ids"]) | |
| # with torch.no_grad(): | |
| # outputs = model(**batch, labels=torch.tensor([1, 0])) | |
| # predictions = F.softmax(outputs.logits, dim=1) | |
| # labels = torch.argmax(predictions, dim=1) | |
| # labels = [model.config.id2label[label_id] for label_id in labels.tolist()] | |
| # # save_directory = "saved" | |
| # tokenizer.save_pretrained(save_directory) | |
| # model.save_pretrained(save_directory) | |
| # tokenizer = AutoTokenizer.from_pretrained(save_directory) | |
| # model = AutoModelForSequenceClassification.from_pretrained(save_directory) | |
| #------------------------------------------------------------------------ |