app.py

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 time
import json
from io import BytesIO

# Streamlit app title
st.title("MOMO & PCHOME 商品搜索和價格分析")

# 從使用者獲取搜索關鍵字和頁數
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)

# 如果用戶輸入了搜索關鍵字
if st.button("抓取並分析"):
    # MOMO 数据抓取
    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"
    }
    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_products = []
        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_products.append({'title': name, 'price': product_price, 'platform': 'MOMO'})
        momo_df = pd.DataFrame(momo_products)
        st.write("MOMO 商品數據:", momo_df)
    else:
        st.error(f"MOMO 請求失敗，狀態碼: {momo_response.status_code}")

    # PCHOME 数据抓取
    pchome_url = f'https://ecshweb.pchome.com.tw/search/v3.3/all/results?q={search_keyword}&page={page_number}&sort=sale/dc'
    pchome_response = requests.get(pchome_url)
    
    if pchome_response.status_code == 200:
        pchome_data = json.loads(pchome_response.content).get('prods', [])
        pchome_products = [{'title': item['name'], 'price': item['price'], 'platform': 'PCHOME'} for item in pchome_data]
        pchome_df = pd.DataFrame(pchome_products)
        st.write("PCHOME 商品數據:", pchome_df)
    else:
        st.error(f"PCHOME 請求失敗，狀態碼: {pchome_response.status_code}")

    # 合併兩平台數據
    if not momo_df.empty and not pchome_df.empty:
        combined_df = pd.concat([momo_df, pchome_df], ignore_index=True)
    elif not momo_df.empty:
        combined_df = momo_df
    elif not pchome_df.empty:
        combined_df = pchome_df
    else:
        combined_df = pd.DataFrame()

    if not combined_df.empty:
        st.write("合併後的商品數據:", combined_df)

        # 生成 CSV 下載連結
        csv = combined_df.to_csv(index=False).encode('utf-8')
        st.download_button(
            label="下載資料",
            data=csv,
            file_name=f"{search_keyword}_momo_pchome_data.csv",
            mime='text/csv',
        )

    # 數據分析和視覺化
    if not momo_df.empty:
        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}")

    if not pchome_df.empty:
        pchome_avg_price = pchome_df['price'].mean()
        st.write(f"PCHOME 平均價格: {pchome_avg_price:.2f}")
        st.write(f"PCHOME 最高價格: {pchome_df['price'].max():.2f}")
        st.write(f"PCHOME 最低價格: {pchome_df['price'].min():.2f}")

    # 字型設定
    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')

    # MOMO 繪製價格折線圖
    if not momo_df.empty:
        fig_momo, ax_momo = plt.subplots(figsize=(15, 8))
        momo_df['price'][:70].plot(ax=ax_momo, 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=20, 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.legend(fontsize=12, loc='upper left')
        plt.grid(axis='y', linestyle='--', alpha=0.5)
        plt.tight_layout()
        st.pyplot(fig_momo)

    # PCHOME 繪製價格折線圖
    if not pchome_df.empty:
        fig_pchome, ax_pchome = plt.subplots(figsize=(15, 8))
        pchome_df['price'][:70].plot(ax=ax_pchome, marker='o', linestyle='-', color='skyblue', linewidth=2, markersize=8)
        plt.title(f'PCHOME 電商網站上 "{search_keyword}" 的銷售價格', fontsize=20, fontweight='bold', color='navy')
        plt.axhline(y=pchome_avg_price, color='red', linestyle='--', linewidth=2, label=f'參考價格: {pchome_avg_price:.2f}')
        plt.legend(fontsize=12, loc='upper left')
        plt.grid(axis='y', linestyle='--', alpha=0.5)
        plt.tight_layout()
        st.pyplot(fig_pchome)