Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""keyword_extraction"""
+
+import requests
+import jieba
+from keybert import KeyBERT
+from sklearn.feature_extraction.text import CountVectorizer
+import streamlit as st
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import FontProperties
+
+# 下載字體
+def download_font(url, save_path):
+    response = requests.get(url)
+    with open(save_path, 'wb') as f:
+        f.write(response.content)
+
+# 字體URL和保存路徑
+font_url = 'https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download'
+font_path = 'TaipeiSansTCBeta-Regular.ttf'
+
+# 下載字體
+download_font(font_url, font_path)
+
+# 設置字體
+font_prop = FontProperties(fname=font_path)
+
+# 讀取繁體中文詞典
+# jieba.set_dictionary('path_to_your_dict.txt')  # 繁體中文詞典的實際路徑，若需要繁體字典請取消註解並設置正確路徑
+
+# 2. 定義斷詞函數
+def jieba_tokenizer(text):
+    return jieba.lcut(text)
+
+# 3. 初始化CountVectorizer並定義KeyBERT模型
+vectorizer = CountVectorizer(tokenizer=jieba_tokenizer)
+kw_model = KeyBERT()
+
+# 4. 提取關鍵詞的函數
+def extract_keywords(doc):
+    keywords = kw_model.extract_keywords(doc, vectorizer=vectorizer)
+    return keywords
+
+# 5. 畫圖函數
+def plot_keywords(keywords, title):
+    words = [kw[0] for kw in keywords]
+    scores = [kw[1] for kw in keywords]
+
+    plt.figure(figsize=(10, 6))
+    plt.barh(words, scores, color='skyblue')
+    plt.xlabel('分數', fontproperties=font_prop)
+    plt.title(title, fontproperties=font_prop)
+    plt.gca().invert_yaxis()  # 反轉Y軸，使得分數最高的關鍵詞在最上面
+    plt.xticks(fontproperties=font_prop)
+    plt.yticks(fontproperties=font_prop)
+    st.pyplot(plt)
+
+# 6. 建立Streamlit網頁應用程式
+st.title("中文關鍵詞提取工具")
+doc = st.text_area("請輸入文章：")
+
+if st.button("提取關鍵詞"):
+    if doc:
+        keywords = extract_keywords(doc)
+        st.write("關鍵詞提取結果：")
+        for keyword in keywords:
+            st.write(f"{keyword[0]}: {keyword[1]:.4f}")
+        
+        plot_keywords(keywords, "關鍵詞提取結果")
+
+        # 使用另一個模型進行關鍵詞提取
+        kw_model_multilingual = KeyBERT(model='distiluse-base-multilingual-cased-v1')
+        keywords_multilingual = kw_model_multilingual.extract_keywords(doc, vectorizer=vectorizer)
+        st.write("多語言模型關鍵詞提取結果：")
+        for keyword in keywords_multilingual:
+            st.write(f"{keyword[0]}: {keyword[1]:.4f}")
+        
+        plot_keywords(keywords_multilingual, "多語言模型關鍵詞提取結果")
+    else:
+        st.write("請輸入文章內容以進行關鍵詞提取。")