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| import streamlit as st | |
| import torch | |
| import pandas as pd | |
| import numpy as np | |
| from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer | |
| st.title('Sentiment Analysis with Streamlit') | |
| speech = "" | |
| with open("tweet.txt") as file: | |
| speech = "".join(line.rstrip() for line in file) | |
| data = st.text_area(label="Text for Sentiment Analysis", value=speech) | |
| models = ["sachiniyer/tweet_toxicity", | |
| "distilbert-base-uncased-finetuned-sst-2-english", | |
| "Ghost1/bert-base-uncased-finetuned_for_sentiment_analysis1-sst2", | |
| "Seethal/sentiment_analysis_generic_dataset", | |
| "sbcBI/sentiment_analysis_model", | |
| "juliensimon/reviews-sentiment-analysis"] | |
| model_name = st.selectbox( | |
| 'Which model do you want to use', | |
| models) | |
| labels = ["toxic", "severe toxic", "obscene", "threat", "insult", "identity hate"] | |
| def score(item): | |
| return item['score'] | |
| def get_tokens(data, model): | |
| tokenizer = AutoTokenizer.from_pretrained("sachiniyer/tweet_toxicity") | |
| tokens = tokenizer(data, return_tensors="pt") | |
| return tokens | |
| def get_out(tokens, model): | |
| output = model(**tokens) | |
| return output | |
| def get_perc(output): | |
| return torch.sigmoid(output.logits).detach().numpy()[0] | |
| def get_dict(percs, data): | |
| sorted_indices = np.argsort(percs)[-2:] | |
| row = {"text": data, | |
| "label 1": labels[sorted_indices[1]], | |
| "perc 1": str(round(percs[sorted_indices[1]], 3)), | |
| "label 2": labels[sorted_indices[0]], | |
| "perc 2": str(round(percs[sorted_indices[0]], 3))} | |
| return row | |
| def get(data, model): | |
| tokens = get_tokens(data, model) | |
| output = get_out(tokens, model) | |
| percs = get_perc(output) | |
| d = get_dict(percs, data) | |
| return pd.DataFrame([d]) | |
| if st.button('Run model'): | |
| if model_name == "sachiniyer/tweet_toxicity": | |
| model = AutoModelForSequenceClassification.from_pretrained("sachiniyer/tweet_toxicity") | |
| d = get(data, model) | |
| st.table(d) | |
| else: | |
| generator = pipeline(model=model_name) | |
| st.markdown(generator(model_name)) | |