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import streamlit as st #Web App
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
#title
st.title("Sentiment Analysis")
def analyze(input, model):
return "This is a sample output"
# load my fine-tuned model
fine_tuned = "jbraha/tweet-bert"
labels = {'LABEL_0': 'toxic', 'LABEL_1': 'severe_toxic', 'LABEL_2': 'obscene', 'LABEL_3': 'threat',
'LABEL_4': 'insult', 'LABEL_5': 'identity_hate'}
# make a dictionary of the labels with keys like "LABEL_0" and values like "toxic"
#text insert
input = st.text_area("Insert text to be analyzed", value="Nice to see you today.",
height=None, max_chars=None, key=None, help=None, on_change=None,
args=None, kwargs=None, placeholder=None, disabled=False,
label_visibility="visible")
option = st.selectbox(
'Choose a transformer model:',
('Default', 'Fine-Tuned' , 'Roberta'))
if option == 'Fine-Tuned':
model = AutoModelForSequenceClassification.from_pretrained(fine_tuned)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
elif option == 'Roberta':
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer, top_k=None)
else:
classifier = pipeline('sentiment-analysis')
if st.button('Analyze'):
result = classifier(input)
print(result)
print(type(result))
output = None
result = result[0]
if option == 'Fine-Tuned':
output = {'Toxic': result['LABEL_0']}
del result['LABEL_0']
output[max(result, key=result.get)] = result[max(result, key=result.get)]
else:
output = result
st.write(output)
else:
st.write('Excited to analyze!')
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