Update app.py

app.py

@@ -11,22 +11,22 @@ from pytrends.request import TrendReq
 import plotly.express as px
 from datetime import datetime, timedelta
 
-# Set up Streamlit app title
+# 設置 Streamlit 應用標題
 st.title("🐣MOMO 🆚 PCHOME 商品搜索和價格分析👁️‍🗨️")
 
-# Get user input
+# 獲取用戶輸入
 search_keyword = st.text_input("請輸入要搜索的關鍵字: ", "筆電")
 page_number = st.number_input("請輸入要搜索的頁數: ", min_value=1, max_value=100, value=1, step=1)
 
-# Date range input for Pytrends
+# Pytrends 的日期範圍輸入
 start_date = st.date_input("選擇開始日期", value=datetime.today() - timedelta(days=30))
 end_date = st.date_input("選擇結束日期", value=datetime.today())
 
-# Create a button to start the scraping process
+# 創建一個按鈕來開始爬取過程
 if st.button("開始搜索"):
     start_time = time.time()
     
-    # MOMO scraping
+    # MOMO 爬取
     momo_url = "https://apisearch.momoshop.com.tw/momoSearchCloud/moec/textSearch"
     momo_headers = {
         "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36"
@@ -60,13 +60,13 @@ if st.button("開始搜索"):
         momo_df = pd.DataFrame(momo_product_list)
         st.write("MOMO 商品數據:", momo_df)
         
-        # MOMO data analysis
+        # MOMO 數據分析
         momo_avg_price = momo_df['price'].mean()
         st.write(f"MOMO 平均價格: {momo_avg_price:.2f}")
         st.write(f"MOMO 最高價格: {momo_df['price'].max():.2f}")
         st.write(f"MOMO 最低價格: {momo_df['price'].min():.2f}")
         
-        # MOMO visualization
+        # MOMO 視覺化
         font_url = "https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download"
         font_response = requests.get(font_url)
         with open("TaipeiSansTCBeta-Regular.ttf", "wb") as font_file:
@@ -87,7 +87,7 @@ if st.button("開始搜索"):
         plt.tight_layout()
         st.pyplot(fig)
         
-        # MOMO Sunburst图
+        # MOMO Sunburst圖
         if not momo_df.empty:
             momo_sunburst = momo_df.groupby(['title', 'price']).size().reset_index(name='count')
             fig_momo_sunburst = px.sunburst(
@@ -103,7 +103,7 @@ if st.button("開始搜索"):
 
 
     
-    # PCHOME scraping
+    # PCHOME 爬取
     pchome_base_url = 'https://ecshweb.pchome.com.tw/search/v3.3/all/results?q='
     pchome_data = pd.DataFrame()
     
@@ -119,7 +119,7 @@ if st.button("開始搜索"):
             pchome_df = pchome_df[selected_columns]
             if 'orig' in pchome_df.columns:
                 pchome_df = pchome_df.rename(columns={'orig': 'original_price'})
-            pchome_df['platform'] = 'PCHOME'  # Add platform identifier
+            pchome_df['platform'] = 'PCHOME'  # 添加平台識別符
             pchome_data = pd.concat([pchome_data, pchome_df])
             time.sleep(1)
         else:
@@ -128,13 +128,13 @@ if st.button("開始搜索"):
     if not pchome_data.empty:
         st.write("PCHOME 商品數據:", pchome_data)
         
-        # PCHOME data analysis
+        # PCHOME 數據分析
         pchome_avg_price = pchome_data['price'].mean()
         st.write(f"PCHOME 平均價格: {pchome_avg_price:.2f}")
         st.write(f"PCHOME 最高價格: {pchome_data['price'].max():.2f}")
         st.write(f"PCHOME 最低價格: {pchome_data['price'].min():.2f}")
         
-        # PCHOME visualization
+        # PCHOME 視覺化
         fig, ax = plt.subplots(figsize=(15, 8))
         ax.plot(pchome_data.index[:70], pchome_data['price'][:70], 'o', color='skyblue', markersize=8)
         ax.set_title(f'PCHOME 電商網站上 "{search_keyword}" 的銷售價格', fontsize=20, fontweight='bold')
@@ -148,7 +148,7 @@ if st.button("開始搜索"):
         plt.tight_layout()
         st.pyplot(fig)
 
-        # PCHOME Sunburst图
+        # PCHOME Sunburst圖
         if not pchome_data.empty:
             pchome_sunburst = pchome_data.groupby(['name', 'price']).size().reset_index(name='count')
             fig_pchome_sunburst = px.sunburst(
@@ -160,10 +160,10 @@ if st.button("開始搜索"):
             fig_pchome_sunburst.update_layout(margin=dict(t=50, l=25, r=25, b=25))
             st.plotly_chart(fig_pchome_sunburst)
     
-    # Combine MOMO and PCHOME data
+    # 合併 MOMO 和 PCHOME 的數據
     combined_data = pd.concat([momo_df, pchome_data], ignore_index=True)
     
-    # Add a button to download the combined data as CSV
+    # 添加一個按鈕以CSV格式下載合併後的數據
     csv = combined_data.to_csv(index=False, encoding='utf-8-sig').encode('utf-8-sig')
     st.download_button(
         label="下載CSV檔案",
@@ -173,9 +173,9 @@ if st.button("開始搜索"):
     )
     
     end_time = time.time()
-    st.write(f"Execution time: {end_time - start_time:.2f} seconds")
+    st.write(f"執行時間: {end_time - start_time:.2f} 秒")
 
-    # Pytrends Analysis
+    # Pytrends 分析
     pytrend = TrendReq(hl="zh-TW", tz=-480)
     keywords = [search_keyword] # 使用列表形式
     pytrend.build_payload(
@@ -190,7 +190,7 @@ if st.button("開始搜索"):
     if "isPartial" in df.columns:
         df = df.drop(["isPartial"], axis=1)
     
-    # Plotting Trend Data
+    # 繪製趨勢數據
     fig, ax = plt.subplots(figsize=(12, 8), dpi=80)
     ax.plot(df.index, df[keywords[0]], label=keywords[0], lw=3.0, marker='o', markersize=8, color='#4285F4', linestyle='-')
     
@@ -200,22 +200,22 @@ if st.button("開始搜索"):
     ax.legend()
     ax.grid(True, linestyle='--', alpha=0.6)
 
-    # Removing spines
+    # 移除軸線
     ax.spines['top'].set_visible(False)
     ax.spines['right'].set_visible(False)
 
-    # Customize tick colors
+    # 自訂刻度顏色
     ax.tick_params(axis='x', colors='#4285F4')
     ax.tick_params(axis='y', colors='#4285F4')
 
-    # Customize legend
+    # 自訂圖例
     legend = ax.legend()
     legend.get_frame().set_alpha(0.5)
     legend.get_lines()[0].set_linestyle('-')
 
-    # Display the plot
+    # 顯示圖表
     plt.tight_layout()
     st.pyplot(fig)
 
-    # Print out statistics
+    # 輸出統計數據
     st.write(df.describe())