import os
import requests
import pandas as pd
import json
import time
import matplotlib as mpl
import matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import streamlit as st

# Streamlit 應用程式標題
st.title('電商商品價格分析 - MOMO & PChome')

# 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:", "手機")
page_number = st.number_input("請輸入要搜索的頁數:", min_value=1, value=1)

if st.button('搜索'):
    momo_data = pd.DataFrame()
    pchome_data = pd.DataFrame()

    # MOMO 平台搜索邏輯
    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_from_api = momo_response.json()
        momo_products = momo_data_from_api.get('rtnSearchData', {}).get('goodsInfoList', [])

        momo_product_list = []
        for product in momo_products:
            name = product.get('goodsName', '')
            price = product.get('goodsPrice', '')
            price_str = str(price)
            if '(' in price_str:
                price_str = price_str.split('(')[0]
            price_str = price_str.replace(',', '').replace('$', '')
            try:
                product_price = float(price_str)
            except ValueError:
                product_price = 0
            momo_product_list.append({'title': name, 'price': product_price})

        momo_data = pd.DataFrame(momo_product_list)
    else:
        st.error(f"MOMO 請求失敗，狀態碼: {momo_response.status_code}")

    # PChome 平台搜索邏輯
    for i in range(1, page_number + 1):
        pchome_url = f'https://ecshweb.pchome.com.tw/search/v3.3/all/results?q={search_keyword}&page={i}&sort=sale/dc'
        pchome_list_req = requests.get(pchome_url)
        if pchome_list_req.status_code == 200:
            pchome_getdata = json.loads(pchome_list_req.content)
            pchome_todataFrame = pd.DataFrame(pchome_getdata['prods'])
            pchome_todataFrame.rename(columns={'name': 'title'}, inplace=True)
            pchome_data = pd.concat([pchome_data, pchome_todataFrame])
            time.sleep(1)  # Respectful delay
        else:
            st.error(f"PChome 請求失敗，狀態碼: {pchome_list_req.status_code}")

    # 計算 MOMO 的統計數據
    if not momo_data.empty:
        momo_max_price = momo_data['price'].max()
        momo_min_price = momo_data['price'].min()
        momo_max_product = momo_data[momo_data['price'] == momo_max_price]['title'].values[0]
        momo_min_product = momo_data[momo_data['price'] == momo_min_price]['title'].values[0]
        momo_mean_price = momo_data['price'].mean()

        st.subheader("MOMO 統計數據")
        st.write(f"最高價格商品: {momo_max_product}")
        st.write(f"價格: {momo_max_price}")
        st.write(f"最低價格商品: {momo_min_product}")
        st.write(f"價格: {momo_min_price}")
        st.write(f"平均價格: {momo_mean_price:.2f}")

        # 顯示 MOMO 的 DataFrame
        st.write(momo_data[['title', 'price']])

    # 計算 PChome 的統計數據
    if not pchome_data.empty:
        pchome_max_price = pchome_data['price'].max()
        pchome_min_price = pchome_data['price'].min()
        pchome_max_product = pchome_data[pchome_data['price'] == pchome_max_price]['title'].values[0]
        pchome_min_product = pchome_data[pchome_data['price'] == pchome_min_price]['title'].values[0]
        pchome_mean_price = pchome_data['price'].mean()

        st.subheader("PChome 統計數據")
        st.write(f"最高價格商品: {pchome_max_product}")
        st.write(f"價格: {pchome_max_price}")
        st.write(f"最低價格商品: {pchome_min_product}")
        st.write(f"價格: {pchome_min_price}")
        st.write(f"平均價格: {pchome_mean_price:.2f}")

        # 顯示 PChome 的 DataFrame
        st.write(pchome_data[['title', 'price']])

    # 繪製合併的價格點狀圖
    if not momo_data.empty or not pchome_data.empty:
        # 字型設定
        font_url = 'https://github.com/googlefonts/noto-cjk/raw/main/Sans/OTF/TraditionalChinese/NotoSansCJKtc-Regular.otf'
        font_response = requests.get(font_url)
        font_path = 'NotoSansCJKtc-Regular.otf'
        with open(font_path, "wb") as font_file:
            font_file.write(font_response.content)
        fm.fontManager.addfont(font_path)
        mpl.rc('font', family='Noto Sans CJK TC')

        # 合併兩個 DataFrame
        momo_data['platform'] = 'MOMO'
        pchome_data['platform'] = 'PChome'
        all_data = pd.concat([momo_data, pchome_data])

        # 繪製價格點狀圖
        fig, ax = plt.subplots(figsize=(10, 6))
        for platform in all_data['platform'].unique():
            platform_data = all_data[all_data['platform'] == platform]
            ax.scatter(platform_data.index, platform_data['price'], label=platform, s=100, alpha=0.7)

        ax.set_title(f'電商平台 "{search_keyword}" 價格比較 - MOMO & PChome', fontsize=20, fontweight='bold')
        ax.set_xlabel('商品 ID', fontsize=14)
        ax.set_ylabel('價格', fontsize=14)
        ax.axhline(y=all_data['price'].mean(), color='red', linestyle='--', linewidth=2, label=f'總平均價格: {all_data["price"].mean():.2f}')
        ax.legend(fontsize=10, loc='upper left')
        ax.grid(axis='y', linestyle='--', alpha=0.5)
        ax.set_facecolor('#f8f8f8')
        plt.tight_layout()

        st.pyplot(fig)
        os.remove(font_path)
    else:
        st.warning("未能從 MOMO 或 PChome 獲取到數據")