Create app.py

app.py

@@ -0,0 +1,134 @@
+import streamlit as st
+import requests
+import json
+import pandas as pd
+import time
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+
+# Set page config
+st.set_page_config(
+    page_title="PChome 商品分析器",
+    page_icon="📊",
+    layout="wide"
+)
+
+# Title and description
+st.title("PChome 商品分析器")
+st.markdown("這個應用程式可以爬取並分析 PChome 上的商品資訊")
+
+# Input section
+with st.sidebar:
+    st.header("搜尋設定")
+    keyword = st.text_input("請輸入搜尋關鍵字", "行李箱")
+    page_num = st.number_input("要爬取的頁數", min_value=1, max_value=10, value=1)
+    
+# Function to scrape PChome data
+def scrape_pchome(keyword, page_num):
+    alldata = pd.DataFrame()
+    
+    with st.spinner(f'正在爬取 {page_num} 頁的資料...'):
+        for i in range(1, page_num + 1):
+            # Progress bar
+            progress = st.progress((i - 1) / page_num)
+            
+            url = f'https://ecshweb.pchome.com.tw/search/v3.3/all/results?q={keyword}&page={i}&sort=sale/dc'
+            
+            try:
+                list_req = requests.get(url)
+                getdata = json.loads(list_req.content)
+                
+                if 'prods' in getdata and getdata['prods']:
+                    todataFrame = pd.DataFrame(getdata['prods'])
+                    alldata = pd.concat([alldata, todataFrame])
+                    
+                time.sleep(2)  # Reduced sleep time for better user experience
+                
+            except Exception as e:
+                st.error(f"爬取第 {i} 頁時發生錯誤: {str(e)}")
+                break
+                
+            progress.progress((i) / page_num)
+            
+    return alldata
+
+# Function to create analysis plots
+def create_analysis_plots(df):
+    # Basic statistics
+    st.subheader("基本統計資訊")
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.metric("平均價格", f"NT$ {df['price'].mean():,.0f}")
+    with col2:
+        st.metric("最高價格", f"NT$ {df['price'].max():,.0f}")
+    with col3:
+        st.metric("最低價格", f"NT$ {df['price'].min():,.0f}")
+
+    # Price trend plot
+    st.subheader("價格趨勢圖")
+    fig, ax = plt.subplots(figsize=(15, 8))
+    df['price'][:70].plot(
+        color='skyblue',
+        linewidth=2,
+        marker='o',
+        markersize=8,
+        ax=ax
+    )
+    
+    mean_price = df['price'].mean()
+    ax.axhline(y=mean_price, color='red', linestyle='--', linewidth=2, 
+               label=f'平均價格: NT$ {mean_price:,.0f}')
+    
+    plt.title(f'{datetime.now().strftime("%Y%m%d")} PChome {keyword} 售價分析', 
+              fontsize=20, fontweight='bold')
+    plt.xlabel('商品編號', fontsize=14)
+    plt.ylabel('價格 (NT$)', fontsize=14)
+    plt.xticks(rotation=45)
+    plt.grid(True, alpha=0.3)
+    plt.legend()
+    st.pyplot(fig)
+    
+    # Price distribution plot
+    st.subheader("價格分布圖")
+    fig2, ax2 = plt.subplots(figsize=(12, 6))
+    sns.histplot(data=df['price'], bins=30, kde=True, ax=ax2)
+    plt.title('商品價格分布', fontsize=16)
+    plt.xlabel('價格 (NT$)', fontsize=12)
+    plt.ylabel('數量', fontsize=12)
+    st.pyplot(fig2)
+
+# Main app logic
+if st.sidebar.button('開始分析'):
+    # Record start time
+    start_time = time.time()
+    
+    # Scrape data
+    data = scrape_pchome(keyword, page_num)
+    
+    if not data.empty:
+        # Display raw data
+        st.subheader("原始資料")
+        st.dataframe(data[['name', 'price']])
+        
+        # Create analysis plots
+        create_analysis_plots(data)
+        
+        # Download button for CSV
+        csv = data.to_csv(index=False).encode('utf-8-sig')
+        st.download_button(
+            label="下載完整資料 (CSV)",
+            data=csv,
+            file_name=f'pchome_{keyword}_{datetime.now().strftime("%Y%m%d")}.csv',
+            mime='text/csv'
+        )
+        
+        # Display execution time
+        end_time = time.time()
+        st.info(f'分析完成！執行時間：{end_time - start_time:.2f} 秒')
+    else:
+        st.error("沒有找到相關商品資料")
+
+# Footer
+st.markdown("---")
+st.markdown("Made with ❤️ by Your Name")