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Parent(s):
0cc1ee9
Create app.py
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app.py
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import streamlit as st
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from pyabsa import available_checkpoints
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from pyabsa import ATEPCCheckpointManager
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import os
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#import tensorflow_hub as hub
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import numpy as np
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import pandas as pd
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import json
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checkpoint_map = available_checkpoints()
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aspect_extractor = ATEPCCheckpointManager.get_aspect_extractor(checkpoint='english',
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auto_device=True # False means load model on CPU
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)
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def main():
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st.set_page_config(page_title="Aspect based sentiment Anslysis", page_icon=":smiley:", layout="wide")
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st.title("Aspect based sentiment Anslysis :smiley:")
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st.header("Aspect based sentiment Anslysis")
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st.write("Enter a review:")
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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.")
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input_string = st.text_input("")
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if st.button("Enter"):
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st.write("Aspect and sentiment is:")
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example=[]
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example.append(input_string)
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inference_source = examples
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atepc_result = aspect_extractor.extract_aspect(inference_source=inference_source, #
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pred_sentiment=True, # Predict the sentiment of extracted aspect terms
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)
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for aspect, sentiment in zip(atepc_result[0]['aspect'], atepc_result[0]['sentiment']):
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st.write(aspect + ': ' + sentiment)
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if __name__ == "__main__":
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main()
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