Create app.py

app.py

@@ -0,0 +1,138 @@
+import os
+import requests
+import pandas as pd
+import matplotlib as mpl
+import matplotlib.font_manager as fm
+import matplotlib.pyplot as plt
+import streamlit as st
+import json
+import time
+
+# Set up Streamlit app title
+st.title("MOMO & PCHOME 商品搜索和價格分析")
+
+# Get user input
+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)
+
+# Create a button to start the scraping process
+if st.button("開始搜索"):
+    start_time = time.time()
+    
+    # MOMO scraping
+    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"
+    }
+    momo_payload = {
+        "host": "momoshop",
+        "flag": "searchEngine",
+        "data": {
+            "searchValue": search_keyword,
+            "curPage": str(page_number),
+            "priceS": "0",
+            "priceE": "9999999",
+            "searchType": "1"
+        }
+    }
+    momo_response = requests.post(momo_url, headers=momo_headers, json=momo_payload)
+    
+    if momo_response.status_code == 200:
+        momo_data = momo_response.json().get('rtnSearchData', {}).get('goodsInfoList', [])
+        momo_product_list = []
+        for product in momo_data:
+            name = product.get('goodsName', '')
+            price = product.get('goodsPrice', '')
+            price_str = str(price).split('(')[0].replace(',', '').replace('$', '')
+            try:
+                product_price = float(price_str)
+            except ValueError:
+                product_price = 0
+            momo_product_list.append({'title': name, 'price': product_price, 'platform': 'MOMO'})
+        
+        momo_df = pd.DataFrame(momo_product_list)
+        st.write("MOMO 商品數據:", momo_df)
+        
+        # MOMO data analysis
+        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
+        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:
+            font_file.write(font_response.content)
+        fm.fontManager.addfont("TaipeiSansTCBeta-Regular.ttf")
+        mpl.rc('font', family='Taipei Sans TC Beta')
+        
+        fig, ax = plt.subplots(figsize=(30, 15))
+        momo_df['price'][:70].plot(ax=ax, marker='o', linestyle='-', color='skyblue', linewidth=2, markersize=8)
+        plt.title(f'MOMO 電商網站上 "{search_keyword}" 的銷售價格', fontsize=30, fontweight='bold', color='navy')
+        plt.axhline(y=momo_avg_price, color='red', linestyle='--', linewidth=2, label=f'參考價格: {momo_avg_price:.2f}')
+        plt.xlabel('商品索引', fontsize=20, color='gray')
+        plt.ylabel('價格', fontsize=20, color='gray')
+        plt.legend(fontsize=12, loc='upper left')
+        plt.grid(axis='y', linestyle='--', alpha=0.5)
+        plt.xticks(rotation=45, ha='right', fontsize=12, color='gray')
+        plt.yticks(fontsize=12, color='gray')
+        ax.set_facecolor('#f8f8f8')
+        plt.tight_layout()
+        st.pyplot(fig)
+    else:
+        st.error(f"MOMO 請求失敗，狀態碼: {momo_response.status_code}")
+    
+    # PCHOME scraping
+    pchome_base_url = 'https://ecshweb.pchome.com.tw/search/v3.3/all/results?q='
+    pchome_data = pd.DataFrame()
+    
+    for i in range(1, page_number + 1):
+        pchome_url = f'{pchome_base_url}{search_keyword}&page={i}&sort=sale/dc'
+        pchome_response = requests.get(pchome_url)
+        if pchome_response.status_code == 200:
+            pchome_json_data = json.loads(pchome_response.content)
+            pchome_df = pd.DataFrame(pchome_json_data['prods'])
+            pchome_df['platform'] = 'PCHOME'  # Add platform identifier
+            pchome_data = pd.concat([pchome_data, pchome_df])
+            time.sleep(1)
+        else:
+            st.error(f"PCHOME 請求失敗，狀態碼: {pchome_response.status_code}")
+    
+    if not pchome_data.empty:
+        st.write("PCHOME 商品數據:", pchome_data)
+        
+        # PCHOME data analysis
+        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
+        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')
+        ax.axhline(y=pchome_avg_price, color='red', linestyle='--', linewidth=2, label=f'參考價格: {pchome_avg_price:.2f}')
+        ax.set_xlabel('商品索引', fontsize=14)
+        ax.set_ylabel('價格', fontsize=14)
+        ax.tick_params(axis='x', rotation=45, labelsize=12)
+        ax.tick_params(axis='y', labelsize=12)
+        ax.legend(fontsize=12, loc='upper left')
+        ax.grid(axis='y', linestyle='--', alpha=0.7)
+        plt.tight_layout()
+        st.pyplot(fig)
+    
+    # Combine MOMO and PCHOME data
+    combined_data = pd.concat([momo_df, pchome_data], ignore_index=True)
+    
+    # Add a button to download the combined data as CSV
+    csv = combined_data.to_csv(index=False).encode('utf-8')
+    st.download_button(
+        label="下載CSV檔案",
+        data=csv,
+        file_name=f'{search_keyword}_combined_data.csv',
+        mime='text/csv'
+    )
+    
+    end_time = time.time()
+    st.write(f"Execution time: {end_time - start_time:.2f} seconds")