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import streamlit as st
from .services import SentimentAnalyzer
from functools import lru_cache
# @st.cache(allow_output_mutation=False, hash_funcs={Tokenizer: str})
@lru_cache(maxsize=1)
def load_text_generator():
predictor = SentimentAnalyzer()
return predictor
predictor = load_text_generator()
def write():
st.markdown(
"""
# Arabic Sentiment Analysis
"""
)
input_text = st.text_input(
"Enter your text here:",
)
if st.button("Predict"):
with st.spinner("Predicting..."):
prediction, score, all_score = predictor.predict([input_text])
st.write(f"Result: {prediction[0]}")
detailed_score = {
"Positive": all_score[0][0],
"Neutral": all_score[0][1],
"Negative": all_score[0][2],
}
st.write("All scores:")
st.write(detailed_score)