app.py

import requests
import pandas as pd
import json
import time
import plotly.graph_objs as go
import streamlit as st
from pytrends.request import TrendReq

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

# Input fields
search_keyword = st.text_input("請輸入要搜索的關鍵字:", "平板")
page_number = st.number_input("請輸入要搜索的頁數:", value=1, min_value=1, max_value=100)
start_date = st.text_input("請輸入 Google 趨勢的開始日期 (格式: YYYY-MM-DD):", "2024-08-01")
end_date = st.text_input("請輸入 Google 趨勢的結束日期 (格式: YYYY-MM-DD):", "2024-08-11")

# Create a button to start the process
if st.button("開始搜索"):
    # 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)

    momo_df = pd.DataFrame()
    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, 'source': 'MOMO'})

        momo_df = pd.DataFrame(momo_product_list)
        st.write("MOMO 商品數據:", momo_df)

    # 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'])

            # Safely select only available columns
            available_columns = ['name', 'describe', 'price', 'orig']
            selected_columns = [col for col in available_columns if col in pchome_df.columns]
            pchome_df = pchome_df[selected_columns]
            if 'orig' in pchome_df.columns:
                pchome_df = pchome_df.rename(columns={'orig': 'original_price'})
            pchome_df['source'] = 'PCHOME'
            pchome_data = pd.concat([pchome_data, pchome_df])
            time.sleep(1)
    
    if not pchome_data.empty:
        st.write("PCHOME 商品數據:", pchome_data)

    # Combine MOMO and PCHOME data for overall analysis
    combined_df = pd.concat([momo_df, pchome_data], ignore_index=True)

    # Google Trends Analysis
    pytrend = TrendReq(hl="zh-TW", tz=-480)
    time_range = f'{start_date} {end_date}'
    pytrend.build_payload(kw_list=[search_keyword], cat=0, timeframe=time_range, geo="TW", gprop="")
    trend_data = pytrend.interest_over_time().drop(columns=["isPartial"])

    if not trend_data.empty:
        st.write("Google 趨勢數據:", trend_data)

    # MOMO Plot
    if not momo_df.empty:
        momo_avg_price = momo_df['price'].mean()
        momo_fig = go.Figure()
        momo_fig.add_trace(go.Scatter(
            x=momo_df['title'],
            y=momo_df['price'],
            mode='markers',
            marker=dict(color='blue'),
            name='MOMO 價格'
        ))
        momo_fig.add_hline(y=momo_avg_price, line_dash="dash", line_color="red",
                           annotation_text=f'平均價格: {momo_avg_price:.2f}', annotation_position="top right")
        momo_fig.update_layout(
            title=f'MOMO 電商網站上 "{search_keyword}" 的銷售價格 (平均價格: {momo_avg_price:.2f})',
            xaxis_title='商品名稱',
            yaxis_title='價格',
            yaxis=dict(range=[0, momo_df['price'].max() * 1.2]),  # Extend Y-axis
            xaxis_tickfont=dict(size=10, family="Arial, italic")   # Italic and smaller font for product names
        )
        st.plotly_chart(momo_fig)

    # PCHOME Plot
    if not pchome_data.empty:
        pchome_avg_price = pchome_data['price'].mean()
        pchome_fig = go.Figure()
        pchome_fig.add_trace(go.Scatter(
            x=pchome_data['name'],
            y=pchome_data['price'],
            mode='markers',
            marker=dict(color='green'),
            name='PCHOME 價格'
        ))
        pchome_fig.add_hline(y=pchome_avg_price, line_dash="dash", line_color="red",
                             annotation_text=f'平均價格: {pchome_avg_price:.2f}', annotation_position="top right")
        pchome_fig.update_layout(
            title=f'PCHOME 電商網站上 "{search_keyword}" 的銷售價格 (平均價格: {pchome_avg_price:.2f})',
            xaxis_title='商品名稱',
            yaxis_title='價格',
            yaxis=dict(range=[0, pchome_data['price'].max() * 1.2]),  # Extend Y-axis
            xaxis_tickfont=dict(size=10, family="Arial, italic")       # Italic and smaller font for product names
        )
        st.plotly_chart(pchome_fig)

    # Pie Chart based on prices
    if not combined_df.empty:
        pie_fig = go.Figure(go.Pie(
            labels=combined_df['title'],
            values=combined_df['price'],
            textinfo='label+percent',
            insidetextorientation='radial'
        ))
        pie_fig.update_layout(title="商品價格比例圖")
        st.plotly_chart(pie_fig)

    # MOMO Sunburst Chart
    if not momo_df.empty:
        sunburst_momo_fig = go.Figure(go.Sunburst(
            labels=momo_df['title'],
            parents=momo_df['source'],
            values=momo_df['price'],
            branchvalues='total',
            textinfo='label+percent parent'
        ))
        sunburst_momo_fig.update_layout(title="MOMO 商品價格 Sunburst 圖")
        st.plotly_chart(sunburst_momo_fig)

    # PCHOME Sunburst Chart
    if not pchome_data.empty:
        sunburst_pchome_fig = go.Figure(go.Sunburst(
            labels=pchome_data['name'],
            parents=pchome_data['source'],
            values=pchome_data['price'],
            branchvalues='total',
            textinfo='label+percent parent'
        ))
        sunburst_pchome_fig.update_layout(title="PCHOME 商品價格 Sunburst 圖")
        st.plotly_chart(sunburst_pchome_fig)

    # Google Trends Plot
    if not trend_data.empty:
        trends_fig = go.Figure()
        trends_fig.add_trace(go.Scatter(
            x=trend_data.index,
            y=trend_data[search_keyword],
            mode='lines',
            line=dict(color='purple'),
            name='Google 趨勢'
        ))
        trends_fig.update_layout(title=f'Google 趨勢 - "{search_keyword}"', xaxis_title='時間', yaxis_title='熱門度')
        st.plotly_chart(trends_fig)