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import streamlit as st | |
from pyabsa import available_checkpoints | |
from pyabsa import ATEPCCheckpointManager | |
import os | |
#import tensorflow_hub as hub | |
import numpy as np | |
import pandas as pd | |
import json | |
checkpoint_map = available_checkpoints() | |
aspect_extractor = ATEPCCheckpointManager.get_aspect_extractor(checkpoint='english', | |
auto_device=True # False means load model on CPU | |
) | |
def main(): | |
st.set_page_config(page_title="Aspect based sentiment Anslysis", page_icon=":smiley:", layout="wide") | |
st.title("Aspect based sentiment Anslysis :smiley:") | |
st.header("Aspect based sentiment Anslysis") | |
st.write("Enter a review:") | |
st.write("e.g. Purchased this for my device, it worked as advertised. You can never have too much phone memory, since I download a lot of stuff this was a no brainer for me.") | |
input_string = st.text_input("") | |
if st.button("Enter"): | |
with st.spinner("Extracting aspects and sentiments..."): | |
examples = [] | |
examples.append(input_string) | |
inference_source = examples | |
atepc_result = aspect_extractor.extract_aspect(inference_source=inference_source, # | |
pred_sentiment=True, # Predict the sentiment of extracted aspect terms | |
) | |
st.write("Aspect and sentiment is:") | |
for aspect, sentiment in zip(atepc_result[0]['aspect'], atepc_result[0]['sentiment']): | |
st.write(aspect + ': ' + sentiment) | |
if __name__ == "__main__": | |
main() |