app.py

from transformers import pipeline, AutoTokenizer
import pandas as pd
import numpy as np
import torch
import streamlit as st

USE_GPU = True

if USE_GPU and torch.cuda.is_available():
    device = torch.device("cuda:0")
else: 
    device = torch.device('cpu')

MODEL_NAME_CHINESE = "IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece"
#MODEL_NAME_CHINESE = "IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-CWS-Chinese"


WORD_PROBABILITY_THRESHOLD = 0.02
TOP_K_WORDS = 200

CHINESE_WORDLIST = ['一定','一样','不得了','主观','从此','便于','俗话','倒霉','候选','充沛','分别','反倒','只好','同情','吹捧','咳嗽','围绕','如意','实行','将近','就职','应该','归还','当面','忘记','急忙','恢复','悲哀','感冒','成长','截至','打架','把握','报告','抱怨','担保','拒绝','拜访','拥护','拳头','拼搏','损坏','接待','握手','揭发','攀登','显示','普遍','未免','欣赏','正式','比如','流浪','涂抹','深刻','演绎','留念','瞻仰','确保','稍微','立刻','精心','结算','罕见','访问','请示','责怪','起初','转达','辅导','过瘾','运动','连忙','适合','遭受','重叠','镇静']

@st.cache_resource
def get_model_chinese():
    return pipeline("fill-mask", MODEL_NAME_CHINESE, device = device)

@st.cache_resource
def get_allowed_tokens():
    df = pd.read_csv('allowed_token_ids.csv')
    return set(list(df['token']))

def assess_chinese(word, sentence):
    print("Assessing Chinese")
    allowed_token_ids = get_allowed_tokens()
    
    if sentence.lower().find(word.lower()) == -1:
        print('Sentence does not contain the word!')
        return

    text = sentence.replace(word.lower(), "<mask>")

    top_k_prediction = mask_filler_chinese(text, top_k=TOP_K_WORDS)
    target_word_prediction = mask_filler_chinese(text, targets = word)

    norm_factor = 0
    for output in top_k_prediction: 
        if output['token'] not in allowed_token_ids:
            norm_factor += output['score']

    top_k_prediction_new = []
    for output in top_k_prediction:
        if output['token'] in allowed_token_ids:        
            output['score'] = output['score']/(1-min(0.5,norm_factor))
            top_k_prediction_new.append(output)

    target_word_prediction[0]['score'] = target_word_prediction[0]['score'] / (1-min(0.5,norm_factor))
    score = target_word_prediction[0]['score']

    # append the original word if its not found in the results
    top_k_prediction_filtered = [output for output in top_k_prediction_new if \
                                 output['token_str'] == word]
    if len(top_k_prediction_filtered) == 0:
        top_k_prediction_new.extend(target_word_prediction)

    return top_k_prediction_new, score

def assess_sentence(word, sentence):
    return assess_chinese(word, sentence)
    
def get_chinese_word():
    possible_words = CHINESE_WORDLIST
    word = np.random.choice(possible_words)
    return word

def get_word():
    return get_chinese_word()

mask_filler_chinese = get_model_chinese()
#wordlist_chinese = get_wordlist_chinese()

def highlight_given_word(row):
    color = '#ACE5EE' if row.Words == target_word else 'white'
    return [f'background-color:{color}'] * len(row)

def get_top_5_results(top_k_prediction):
    predictions_df = pd.DataFrame(top_k_prediction)
    predictions_df = predictions_df.drop(columns=["token", "sequence"])
    predictions_df = predictions_df.rename(columns={"score": "Probability", "token_str": "Words"})

    if (predictions_df[:5].Words == target_word).sum() == 0:
        print("target word not in top 5")
        top_5_df = predictions_df[:5]
        target_word_df = predictions_df[(predictions_df.Words == target_word)]
        print(target_word_df)
        top_5_df = pd.concat([top_5_df, target_word_df])

    else:
        top_5_df = predictions_df[:5]
    top_5_df['Probability'] = top_5_df['Probability'].apply(lambda x: f"{x:.2%}")

    return top_5_df

#### Streamlit Page
st.title("造句 Auto-marking Demo")

if 'target_word' not in st.session_state:
    st.session_state['target_word'] = get_word()
target_word = st.session_state['target_word']

st.write("Target word: ", target_word)
if st.button("Get new word"):
    st.session_state['target_word'] = get_word()
    st.experimental_rerun()

st.subheader("Form your sentence and input below!")
sentence = st.text_input('Enter your sentence here', placeholder="Enter your sentence here!")

if st.button("Grade"):
    top_k_prediction, score = assess_sentence(target_word, sentence)
    with open('./result01.json', 'w') as outfile:
        outfile.write(str(top_k_prediction))

    st.write(f"Probability: {score:.2%}")
    st.write(f"Target probability: {WORD_PROBABILITY_THRESHOLD:.2%}")
    predictions_df = get_top_5_results(top_k_prediction)
    df_style = predictions_df.style.apply(highlight_given_word, axis=1)

    if (score >= WORD_PROBABILITY_THRESHOLD):
#        st.balloons()
        st.success("Yay good job! 🕺 Practice again with other words", icon="✅")
        st.table(df_style)
    else:
        st.warning("Hmmm.. maybe try again?")
        st.table(df_style)