Upload app.py

app.py

@@ -0,0 +1,156 @@
+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")
+    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)
+
+    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_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 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)
+    return word
+
+def get_word(language):
+    if (language == ENGLISH_LANG):
+        return get_english_word()
+    elif (language == CHINESE_LANG):
+        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'
+    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")
+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)
+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.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)
+    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! That's a great sentence 🕺 Practice again with other word", icon="✅")
+        st.table(df_style)
+    else:
+        st.warning("Hmmm.. maybe try again?")
+        st.table(df_style)
+