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import streamlit as st |
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from huggingface_hub import InferenceApi |
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import pandas as pd |
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from transformers import pipeline |
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STYLE = """ |
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<style> |
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img { |
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max-width: 100%; |
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} |
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th { |
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text-align: left!important |
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} |
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td { |
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font-size: |
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} |
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</style> |
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""" |
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MASK_TOKEN = "<mask>" |
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EMOJI_MAP = { |
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"anger": "😡", |
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"fear": "😱", |
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"happy": "😄", |
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"love": "😍", |
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"sadness": "😭", |
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"positive": "🤗", |
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"negative": "😤", |
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"neutral": "😐", |
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} |
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def display_table(df: pd.DataFrame, subheader: str): |
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st.subheader(subheader) |
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st.table(df) |
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def setup(): |
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st.markdown(STYLE, unsafe_allow_html=True) |
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st.markdown( |
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""" |
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# 🇮🇩 Indonesian RoBERTa Base |
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Demo Powered by [Indonesian RoBERTa Base](https://huggingface.co/flax-community/indonesian-roberta-base). |
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""" |
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) |
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st.sidebar.subheader("Settings") |
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def main(): |
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setup() |
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analyze = st.sidebar.selectbox( |
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"What should we analyze?", |
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("Emotion", "Sentiment"), |
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help="Classifier model to choose for text analysis", |
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) |
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user_input = st.text_input( |
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f"Insert a sentence to predict with a {MASK_TOKEN} token // Masukkan kalimat untuk diisi dengan token {MASK_TOKEN}", |
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value=f"Gila! Hari ini aku {MASK_TOKEN} banget..", |
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) |
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mlm_model = "BigSalmon/BestMask2" |
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mask_api = InferenceApi(mlm_model) |
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if len(user_input) > 0: |
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try: |
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user_input.index(MASK_TOKEN) |
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except ValueError: |
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st.error( |
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f"Please enter a sentence with the correct {MASK_TOKEN} token // Harap masukkan kalimat dengan token {MASK_TOKEN} yang benar" |
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) |
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else: |
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mlm_result = mask_api(inputs=user_input) |
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if mlm_result == None: |
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st.write("Model is loading. Please try again later...") |
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return |
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mlm_df = pd.DataFrame(mlm_result) |
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mlm_df.drop(columns=["token", "token_str"], inplace=True) |
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mlm_df_styled = mlm_df.copy(deep=False) |
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mlm_df_styled = mlm_df_styled.style.set_properties( |
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subset=["sequence", "score"], **{"text-align": "left"} |
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) |
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display_table(mlm_df_styled, "🎈 Top 5 Predictions") |
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if __name__ == "__main__": |
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main() |
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