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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))
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