Spaces:
Runtime error
Runtime error
File size: 4,879 Bytes
35f56ba 7749ef6 47ef74f cfa2b70 cd87a42 8389a97 d71bb22 26f6079 cd87a42 26dac8d cd87a42 26f6079 e43f53b 26f6079 47ef74f 26dac8d 8389a97 26dac8d c704d04 c6e02a8 26f6079 26dac8d 26f6079 26dac8d 47ef74f d5b90e7 dff0151 21d64ee dff0151 26f6079 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
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
from transformers import BertForMaskedLM
model = BertForMaskedLM.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
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_1"
# model_0 = AutoModelForSequenceClassification.from_pretrained(model_name_0)
model_0 = BertForMaskedLM.from_pretrained("Rathgeberj/milestone3_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)
#------------------------------------------------------------------------ |