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| import streamlit as st | |
| from huggingface_hub import InferenceApi | |
| import pandas as pd | |
| from transformers import pipeline | |
| STYLE = """ | |
| <style> | |
| img { | |
| max-width: 100%; | |
| } | |
| th { | |
| text-align: left!important | |
| } | |
| td { | |
| font-size: | |
| } | |
| </style> | |
| """ | |
| MASK_TOKEN = "<mask>" | |
| EMOJI_MAP = { | |
| "anger": "😡", | |
| "fear": "😱", | |
| "happy": "😄", | |
| "love": "😍", | |
| "sadness": "😭", | |
| "positive": "🤗", | |
| "negative": "😤", | |
| "neutral": "😐", | |
| } | |
| def display_table(df: pd.DataFrame, subheader: str): | |
| st.subheader(subheader) | |
| st.table(df) | |
| def setup(): | |
| st.markdown(STYLE, unsafe_allow_html=True) | |
| st.markdown( | |
| """ | |
| # 🇮🇩 Indonesian RoBERTa Base | |
| Demo Powered by [Indonesian RoBERTa Base](https://huggingface.co/flax-community/indonesian-roberta-base). | |
| """ | |
| ) | |
| st.sidebar.subheader("Settings") | |
| def main(): | |
| setup() | |
| analyze = st.sidebar.selectbox( | |
| "What should we analyze?", | |
| ("Emotion", "Sentiment"), | |
| help="Classifier model to choose for text analysis", | |
| ) | |
| user_input = st.text_input( | |
| f"Insert a sentence to predict with a {MASK_TOKEN} token // Masukkan kalimat untuk diisi dengan token {MASK_TOKEN}", | |
| value=f"Gila! Hari ini aku {MASK_TOKEN} banget..", | |
| ) | |
| mlm_model = "flax-community/indonesian-roberta-base" | |
| mask_api = InferenceApi(mlm_model) | |
| if analyze == "Emotion": | |
| sa_model = "StevenLimcorn/indonesian-roberta-base-emotion-classifier" | |
| elif analyze == "Sentiment": | |
| sa_model = "w11wo/indonesian-roberta-base-sentiment-classifier" | |
| sa_pipeline = pipeline("sentiment-analysis", model=sa_model, tokenizer=sa_model) | |
| if len(user_input) > 0: | |
| try: | |
| user_input.index(MASK_TOKEN) | |
| except ValueError: | |
| st.error( | |
| f"Please enter a sentence with the correct {MASK_TOKEN} token // Harap masukkan kalimat dengan token {MASK_TOKEN} yang benar" | |
| ) | |
| else: | |
| # render masked language modeling table | |
| mlm_result = mask_api(inputs=user_input) | |
| if mlm_result == None: | |
| st.write("Model is loading. Please try again later...") | |
| return | |
| mlm_df = pd.DataFrame(mlm_result) | |
| mlm_df.drop(columns=["token", "token_str"], inplace=True) | |
| mlm_df_styled = mlm_df.copy(deep=False) | |
| mlm_df_styled = mlm_df_styled.style.set_properties( | |
| subset=["sequence", "score"], **{"text-align": "left"} | |
| ) | |
| display_table(mlm_df_styled, "🎈 Top 5 Predictions") | |
| # render sentiment analysis table | |
| sa_df = pd.DataFrame(columns=["sequence", "label", "score"]) | |
| for sequence in mlm_df["sequence"].values: | |
| sa_output = sa_pipeline(sequence) # predict for every mlm output | |
| result_dict = {"sequence": sequence} | |
| result_dict.update(sa_output[0]) | |
| sa_df = sa_df.append(result_dict, ignore_index=True) | |
| sa_df["label"] = sa_df["label"].apply(lambda x: x + " " + EMOJI_MAP[x]) | |
| sa_df_styled = sa_df.copy(deep=False) | |
| sa_df_styled = sa_df_styled.style.set_properties( | |
| subset=["sequence", "label", "score"], **{"text-align": "left"} | |
| ) | |
| display_table(sa_df_styled, "🤔 By saying that, I guess you are feeling..") | |
| if __name__ == "__main__": | |
| main() | |