Update app.py

change to Chinese only

app.py

@@ -11,41 +11,19 @@ if USE_GPU and torch.cuda.is_available():
 else: 
     device = torch.device('cpu')
 
-MODEL_NAME_ENGLISH = "facebook/xlm-v-base"
-#SENTENCE_MODEL_NAME_ENGLISH = 'sentence-transformers/all-MiniLM-L6-v2'
-#WORD_MODEL_NAME_ENGLISH = 'vocab-transformers/distilbert-word2vec_256k-MLM_best'
-
-# chinese models
 MODEL_NAME_CHINESE = "IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece"
 
 WORD_PROBABILITY_THRESHOLD = 0.02
-#WORD_PROBABILITY_THRESHOLD_ENGLISH = 0.02
-#WORD_PROBABILITY_THRESHOLD_CHINESE = 0.02
 TOP_K_WORDS = 10
 
-ENGLISH_LANG = "English"
-CHINESE_LANG = "Chinese"
-
 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_model_english():
-    return pipeline("fill-mask", MODEL_NAME_ENGLISH, device = device)    
-
-@st.cache_data
-def get_wordlist_chinese():
-    return pd.read_csv('wordlist_chinese.csv')
-
-@st.cache_data
-def get_wordlist_english():
-    return pd.read_csv('wordlist_english.csv')
-
 def assess_chinese(word, sentence):
-    print("Assessing English")
+    print("Assessing Chinese")
     if sentence.lower().find(word.lower()) == -1:
         print('Sentence does not contain the word!')
         return
@@ -65,57 +43,19 @@ def assess_chinese(word, sentence):
 
     return top_k_prediction, score
 
-def assess_english(word, sentence):
-    if sentence.lower().find(word.lower()) == -1:
-        raise Exception("Sentence does not contain the target word")
-
-    text = sentence.replace(word.lower(), "<mask>")
-
-    top_k_prediction = mask_filler_english(text, top_k=TOP_K_WORDS)
-    target_word_prediction = mask_filler_english(text, targets = chr(9601)+word)
-
-    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 if \
-                                 output['token_str'] == word]
-    if len(top_k_prediction_filtered) == 0:
-        top_k_prediction.extend(target_word_prediction)
-
-    return top_k_prediction, score
-
-def assess_sentence(language, word, sentence):
-    if (language == ENGLISH_LANG):
-        return assess_english(word, sentence)
-    elif (language == CHINESE_LANG):
-        return assess_chinese(word, sentence)
+def assess_sentence(word, sentence):
+    return assess_chinese(word, sentence)
     
 def get_chinese_word():
-    include = (wordlist_chinese.assess == True) & (wordlist_chinese.Chinese.apply(len) == 2)
-    possible_words = wordlist_chinese[include]
-    word = possible_words.sample(1).iloc[0].Chinese
-    test_words = CHINESE_WORDLIST
-    word = np.random.choice(test_words)
-    return word
-
-def get_english_word():
-    include = (wordlist_english.assess == True)
-    possible_words = wordlist_english[include]
-    word = possible_words.sample(1).iloc[0].word
-    test_words = ["independent","satisfied","excited"]
-    word = np.random.choice(test_words)
+    possible_words = CHINESE_WORDLIST
+    word = np.random.choice(possible_words)
     return word
 
-def get_word(language):
-    if (language == ENGLISH_LANG):
-        return get_english_word()
-    elif (language == CHINESE_LANG):
-        return get_chinese_word()
+def get_word():
+    return get_chinese_word()
 
 mask_filler_chinese = get_model_chinese()
-mask_filler_english = get_model_english()
 wordlist_chinese = get_wordlist_chinese()
-wordlist_english = get_wordlist_english()
 
 def highlight_given_word(row):
     color = '#ACE5EE' if row.Words == target_word else 'white'
@@ -141,23 +81,21 @@ def get_top_5_results(top_k_prediction):
 
 #### Streamlit Page
 st.title("造句 Auto-marking Demo")
-language = st.radio("Select your language", (ENGLISH_LANG, CHINESE_LANG))
-#st.info("You are practising on " + language)
 
 if 'target_word' not in st.session_state:
-    st.session_state['target_word'] = get_word(language)
+    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(language)
+    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(language, target_word, sentence)
+    top_k_prediction, score = assess_sentence(target_word, sentence)
     with open('./result01.json', 'w') as outfile:
         outfile.write(str(top_k_prediction))