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interface/about_interface.py

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+import gradio as gr
+
+def render_about_tab():    
+    with gr.TabItem("🤝"):
+        
+        # def create_tool_section(tool_name, korean_description, english_description):
+        #     with gr.Accordion(tool_name, open=False):
+        #         with gr.Accordion("한국어", open=False):    
+        #             gr.Markdown(korean_description)
+        #         with gr.Accordion("English", open=False):        
+        #             gr.Markdown(english_description)
+
+        # # Portfolio Rebalancing Tool Section
+        # create_tool_section(
+        #     "RE-BALANCING CALCULATOR",
+        #     """
+        #     ## 📊 리밸런싱 계산기
+
+        #     이 도구는 주어진 주식 보유량, 현금 금액 및 현금 비율을 바탕으로 포트폴리오를 분석하고 재조정합니다. 
+
+        #     ### 입력값
+
+        #     - **주식 보유량 (holdings)**: 각 주식의 코드, 통화 코드, 수량, 목표 비율을 입력합니다. 여러 주식 종목은 쉼표로 구분하여 입력합니다. 예: `AAPL USD 10 0.30,TSLA USD 5 0.20` 
+        #     - **현금 금액 (cash_amount)**: 포트폴리오에 포함된 현금의 금액입니다. 예: `1000`
+        #     - **현금 비율 (cash_ratio)**: 포트폴리오에서 현금이 차지하는 비율입니다. 예: `10` (10%)
+
+        #     ### 기능
+
+        #     1. **입력 파싱**: 주식 보유량, 현금 금액 및 비율을 분석하여 각 주식의 목표 비율을 계산합니다.
+        #     2. **환율 조회**: 각 주식의 통화 코드와 메인 통화 간의 환율을 조회하여 주식 가격을 변환합니다.
+        #     3. **포트폴리오 구축**: 주식과 현금 정보를 바탕으로 포트폴리오를 구축합니다.
+        #     4. **포트폴리오 재조정 정보 제공**: 현재 포트폴리오의 가치와 목표 비율을 비교하여 재조정 필요성을 분석합니다.
+        #     - **현재 보유량 및 가치**: 각 주식 및 현금의 현재 보유량과 가치를 계산합니다.
+        #     - **재조정 항목**: 목표 비율에 맞추기 위해 매수 또는 매도해야 할 주식 수량과 금액을 계산합니다.
+        #     - **통화별 요약**: 포트폴리오의 통화별 분포를 요약합니다.
+
+        #     ### 결과
+
+        #     - **포트폴리오 정보**: 현재 포트폴리오의 총 가치 및 현금 정보를 포함한 HTML 코드입니다.
+        #     - **현재 보유량 및 가치**: 각 주식의 현재 비율 및 가치에 대한 정보입니다.
+        #     - **재조정 분석**: 주식의 매수/매도 조정이 필요한 항목 및 금액에 대한 상세 분석입니다.
+        #     - **통화별 요약**: 포트폴리오의 통화별 비율 및 총 가치를 요약한 정보입니다.
+
+        #     이 도구를 통해 포트폴리오를 효율적으로 재조정하고, 주식 및 현금의 비율을 최적화할 수 있습니다! 📈💼
+
+        #     ### 중요 사항
+        #     - 주식 코드와 통화 코드가 유효한지 확인하세요.
+        #     - 이 도구는 주가와 환율의 실시간 데이터를 사용하므로 인터넷 연결이 필요합니다.
+        #     - Yahoo Finance 및 FinanceDataReader의 데이터를 사용합니다.
+
+        #     ### 면책 조항
+        #     이 도구에서 제공하는 정보는 일반적인 정보 제공 목적만을 위해 제공됩니다. 사이트의 모든 정보는 선의로 제공되지만, 사이트의 정보의 정확성, 적절성, 유효성, 신뢰성, 가용성 또는 완전성에 대해 명시적이든 묵시적이든 어떠한 종류의 진술이나 보증을 하지 않습니다. 사이트의 사용과 사이트의 정보를 신뢰하는 것은 전적으로 사용자의 책임입니다.
+        #     """,
+        #     """
+        #     ## 📊 RE-BALANCING CALCULATOR
+
+        #     This tool analyzes and rebalances a portfolio based on given stock holdings, cash amount, and cash ratio.
+
+        #     ### Input Values
+
+        #     - **Stock Holdings (holdings)**: Enter the stock code, currency code, quantity, and target weight for each stock. Separate multiple stocks with commas. Example: `AAPL USD 10 0.30,TSLA USD 5 0.20`
+        #     - **Cash Amount (cash_amount)**: The amount of cash in the portfolio. Example: `1000`
+        #     - **Cash Ratio (cash_ratio)**: The proportion of cash in the portfolio. Example: `10` (10%)
+
+        #     ### Functionality
+
+        #     1. **Parse Input**: Analyzes stock holdings, cash amount, and ratio to calculate the target weights for each stock.
+        #     2. **Exchange Rate Lookup**: Retrieves exchange rates between each stock's currency code and the main currency to convert stock prices.
+        #     3. **Build Portfolio**: Constructs the portfolio based on stock and cash information.
+        #     4. **Get Portfolio Rebalancing Information**: Compares the current portfolio value and target weights to analyze rebalancing needs.
+        #     - **Current Holdings and Value**: Calculates the current holdings and value of each stock and cash.
+        #     - **Rebalancing Items**: Calculates the quantities and amounts of stocks to buy or sell to meet target weights.
+        #     - **Currency Summary**: Summarizes the portfolio's distribution by currency.
+
+        #     ### Results
+
+        #     - **Portfolio Information**: HTML code containing the total value of the current portfolio and cash information.
+        #     - **Current Holdings and Value**: Information on the current weights and values of each stock.
+        #     - **Rebalancing Analysis**: Detailed analysis of the adjustments needed to buy/sell stocks to meet target weights.
+        #     - **Currency Summary**: Summary of portfolio distribution by currency, including total value.
+
+        #     Use this tool to efficiently rebalance your portfolio and optimize the ratio of stocks and cash! 📈💼
+
+        #     ### Important Notes
+        #     - Ensure stock codes and currency codes are valid.
+        #     - This tool requires an internet connection as it uses real-time stock prices and exchange rates.
+        #     - Data is sourced from Yahoo Finance and FinanceDataReader.
+
+        #     ### Disclaimer
+        #     The information provided by this tool is for general informational purposes only. All information on the site is provided in good faith, but we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the site. Your use of the site and reliance on any information on the site is solely at your own risk.
+        #     """
+        # )
+
+        # # Stock Comparison Tool Section
+        # create_tool_section(
+        #     "STOCK PRICE TREND VISUALIZATION",
+        #     """
+        #     ## 📈 주식 가격 추세 시각화
+
+        #     이 코드는 주어진 주식 코드들의 가격 추세를 시각화합니다. 여러 주식의 가격을 추적하고 비교하기 위해 사용됩니다. 📊
+
+        #     ### 입력값
+
+        #     - **주식 코드 (stock_codes)**: 가격을 추적할 주식의 코드들입니다. 쉼표로 구분된 문자열 형식으로 입력합니다. 예: `AAPL,GOOGL,MSFT`
+        #     - **일수 (days)**: 가격을 조회할 기간입니다. 예: `30` (최근 30일)
+
+        #     ### 기능
+
+        #     1. **주식 가격 데이터 가져오기**: 주어진 주식 코드와 기간에 대해 주식 가격 데이터를 가져옵니다.
+        #     2. **가격 변동 시각화**: 각 주식의 가격 변동을 시각화하여 비교할 수 있는 그래프를 생성합니다.
+        #     - **상대 가격**: 각 주식의 가격을 처음 가격에 대해 상대적으로 변동을 보여줍니다.
+        #     - **스타일**: 그래프에는 색상 그라디언트와 그림자 효과가 적용됩니다.
+        #     - **애니메이션**: 선의 애니메이션 효과가 추가되어 시각적으로 더욱 매력적입니다.
+        #     3. **데이터 테이블 생성**: 각 날짜와 주식의 가격을 포함한 HTML 테이블을 생성합니다.
+
+        #     ### 결과
+
+        #     - **그래프**: 최근 `days` 일 동안의 상대 주식 가격 추세를 보여주는 그래프입니다.
+        #     - **데이터 테이블**: 날짜별로 각 주식의 가격을 포함하는 HTML 테이블입니다.
+
+        #     ### 사용 예제
+
+        #     주식 코드와 기간을 입력하면, 다음과 같은 결과를 얻을 수 있습니다:
+
+        #     - **주식 코드**: `AAPL,GOOGL,MSFT`
+        #     - **일수**: `30`
+
+        #     결과는 최근 30일 동안의 주식 가격 변동을 보여주는 그래프와 데이터 테이블이 포함됩니다.
+
+        #     이 도구를 통해 주식 가격의 변동 추세를 한눈에 확인하고 분석할 수 있습니다! 📈💡
+
+        #     ### 중요 사항
+        #     - 주식 코드가 유효한지 확인하세요.
+        #     - 이 도구는 주가의 실시간 데이터를 사용하므로 인터넷 연결이 필요합니다.
+        #     - Yahoo Finance 및 FinanceDataReader의 데이터를 사용합니다.
+
+        #     ### 면책 조항
+        #     이 도구에서 제공하는 정보는 일반적인 정보 제공 목적만을 위해 제공됩니다. 사이트의 모든 정보는 선의로 제공되지만, 사이트의 정보의 정확성, 적절성, 유효성, 신뢰성, 가용성 또는 완전성에 대해 명시적이든 묵시적이든 어떠한 종류의 진술이나 보증을 하지 않습니다. 사이트의 사용과 사이트의 정보를 신뢰하는 것은 전적으로 사용자의 책임입니다.
+        #     """,
+        #     """
+        #     ## 📈 Stock Price Trend Visualization
+
+        #     This script visualizes the price trends of given stock codes over a specified period. It allows you to track and compare the price movements of multiple stocks. 📊
+
+        #     ### Input Values
+
+        #     - **Stock Codes (stock_codes)**: The codes of the stocks you want to track. Enter them as a comma-separated string. Example: `AAPL,GOOGL,MSFT`
+        #     - **Days (days)**: The period for which you want to fetch the price data. Example: `30` (last 30 days)
+
+        #     ### Functionality
+
+        #     1. **Fetch Stock Price Data**: Retrieves stock price data for the given stock codes and period.
+        #     2. **Visualize Price Trends**: Creates a graph to visualize and compare the price trends of each stock.
+        #     - **Relative Price**: Shows each stock's price relative to its initial price.
+        #     - **Style**: The graph includes color gradients and shadow effects.
+        #     - **Animation**: Line animation effects are added for visual appeal.
+        #     3. **Generate Data Table**: Creates an HTML table with the price data for each stock and date.
+
+        #     ### Results
+
+        #     - **Graph**: A graph showing the relative stock price trends over the last `days` days.
+        #     - **Data Table**: An HTML table displaying the prices of each stock by date.
+
+        #     ### Example Usage
+
+        #     Given stock codes and days, you can obtain results such as:
+
+        #     - **Stock Codes**: `AAPL,GOOGL,MSFT`
+        #     - **Days**: `30`
+
+        #     The result will include a graph showing the price trends over the last 30 days and an HTML table with the price data.
+
+        #     Use this tool to easily analyze and visualize stock price movements! 📈💡
+
+        #     ### Important Notes
+        #     - Ensure stock codes are valid.
+        #     - This tool requires an internet connection as it uses real-time stock prices.
+        #     - Data is sourced from Yahoo Finance and FinanceDataReader.
+
+        #     ### Disclaimer
+        #     The information provided by this tool is for general informational purposes only. All information on the site is provided in good faith, but we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the site. Your use of the site and reliance on any information on the site is solely at your own risk.
+        #     """
+        # )
+
+        # # Cost Averaging Tool Section
+        # create_tool_section(
+        #     "DOLLAR-COST AVERAGING CALCULATOR",
+        #     """
+        #     ## 💸 물타기 계산기
+
+        #     물타기(Dollar-Cost Averaging, DCA) 계산기를 이용해 보세요! 이 계산기는 특정 주식에 대한 추가 투자를 할 때 평균 구매 가격을 계산해줍니다. 📈
+
+        #     ### 입력값 설정 📊
+
+        #     다음의 값을 입력해 주세요:
+
+        #     - **이전 평균 가격 (old_avg_price)**: 기존 주식의 평균 가격 💵
+        #     - **이전 수량 (old_quantity)**: 기존 주식의 수량 📦
+        #     - **새 가격 (new_price)**: 새로 구매한 주식의 가격 💲
+        #     - **새 수량 (new_quantity)**: 새로 구매한 주식의 수량 📈
+
+        #     ### 계산 방법 🔢
+
+        #     1. **현재 투자 금액 계산**: 이전 평균 가격과 이전 수량을 곱하여 현재 투자 금액을 계산합니다.
+        #     2. **추가 투자 금액 계산**: 새 가격과 새 수량을 곱하여 추가 투자 금액을 계산합니다.
+        #     3. **총 투자 금액 및 총 주식 수 계산**: 현재 투자 금액과 추가 투자 금액을 합쳐서 총 투자 금액을 계산하고, 이전 수량과 새 수량을 더하여 총 주식 수를 계산합니다.
+        #     4. **새 평균 가격 계산**: 총 투자 금액을 총 주식 수로 나누어 새로운 평균 가격을 계산합니다.
+        #     5. **수익률 계산**: 이전과 새로운 수익률을 계산하여 투자 결과를 분석합니다.
+
+        #     ### 결과 🎯
+
+        #     **1) 이전 수익률 (Old Return)**
+
+        #     이전 평균 가격을 기준으로 한 수익률을 표시합니다.
+
+        #     **2) 새로운 수익률 (New Return)**
+
+        #     새로운 평균 가격을 기준으로 한 수익률을 표시합니다.
+
+        #     **3) 추가 투자 금액 (Additional Investment)**
+
+        #     추가로 투자한 금액을 표시합니다.
+
+        #     **4) 새로운 평균 가격 (Average Price)**
+
+        #     새롭게 계산된 평균 가격을 표시합니다.
+
+        #     **5) 총 수량 (Total Quantity)**
+
+        #     총 주식 수량을 표시합니다.
+
+        #     **6) 총 투자 금액 (Total Investment)**
+
+        #     총 투자 금액을 표시합니다.
+
+        #     ### 예제 📉
+
+        #     - **이전 평균 가격**: 100
+        #     - **이전 수량**: 10
+        #     - **새 가격**: 120
+        #     - **새 수량**: 5
+
+        #     결과:
+        #     - 새로운 평균 가격: 105
+        #     - 총 수량: 15
+        #     - 총 투자 금액: 1,575
+        #     - 이전 수익률: 20.00%
+        #     - 새로운 수익률: 14.29%
+        #     - 추가 투자 금액: 600
+
+        #     물타기 계산기를 통해 투자의 평균 구매 가격을 쉽게 계산해 보세요! 📊💰
+
+        #     ### 면책 조항
+        #     이 도구에서 제공하는 정보는 일반적인 정보 제공 목적만을 위해 제공됩니다. 사이트의 모든 정보는 선의로 제공되지만, 사이트의 정보의 정확성, 적절성, 유효성, 신뢰성, 가용성 또는 완전성에 대해 명시적이든 묵시적이든 어떠한 종류의 진술이나 보증을 하지 않습니다. 사이트의 사용과 사이트의 정보를 신뢰하는 것은 전적으로 사용자의 책임입니다.                
+        #     """,
+        #     """
+        #     ## 💸 Dollar-Cost Averaging Calculator
+
+        #     Try out the Dollar-Cost Averaging (DCA) calculator! This tool helps you calculate the average purchase price of stocks when making additional investments. 📈
+
+        #     ### Input Values 📊
+
+        #     Please enter the following details:
+
+        #     - **Old Average Price (old_avg_price)**: The average price of the existing stocks 💵
+        #     - **Old Quantity (old_quantity)**: The quantity of the existing stocks 📦
+        #     - **New Price (new_price)**: The price of the newly purchased stocks 💲
+        #     - **New Quantity (new_quantity)**: The quantity of the newly purchased stocks 📈
+
+        #     ### Calculation Process 🔢
+
+        #     1. **Current Investment**: Calculate the current investment amount by multiplying the old average price by the old quantity.
+        #     2. **Additional Investment**: Calculate the additional investment amount by multiplying the new price by the new quantity.
+        #     3. **Total Investment and Total Quantity**: Add the current investment amount and additional investment amount to get the total investment. Sum the old quantity and new quantity to get the total quantity of stocks.
+        #     4. **New Average Price**: Calculate the new average price by dividing the total investment by the total quantity of stocks.
+        #     5. **Return Calculation**: Calculate the old and new returns to analyze the investment results.
+
+        #     ### Results 🎯
+
+        #     **1) Old Return**
+
+        #     Displays the return based on the old average price.
+
+        #     **2) New Return**
+
+        #     Displays the return based on the new average price.
+
+        #     **3) Additional Investment**
+
+        #     Shows the amount of additional investment made.
+
+        #     **4) Average Price**
+
+        #     Displays the newly calculated average price.
+
+        #     **5) Total Quantity**
+
+        #     Shows the total quantity of stocks.
+
+        #     **6) Total Investment**
+
+        #     Displays the total investment amount.
+
+        #     ### Example 📉
+
+        #     - **Old Average Price**: 100
+        #     - **Old Quantity**: 10
+        #     - **New Price**: 120
+        #     - **New Quantity**: 5
+
+        #     Results:
+        #     - New Average Price: 105
+        #     - Total Quantity: 15
+        #     - Total Investment: 1,575
+        #     - Old Return: 20.00%
+        #     - New Return: 14.29%
+        #     - Additional Investment: 600
+
+        #     Use the Dollar-Cost Averaging Calculator to easily compute the average purchase price of your investments! 📊💰
+
+        #     ### Disclaimer
+        #     The information provided by this tool is for general informational purposes only. All information on the site is provided in good faith, but we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the site. Your use of the site and reliance on any information on the site is solely at your own risk.
+        #     """
+        # )
+
+        # # Retirement Planning Tool Section
+        # create_tool_section(
+        #     "RETIREMENT PLANNING CALCULATOR",
+        #     """
+        #     ## 🏖️ 은퇴 계획 계산기 사용법
+
+        #     안녕하세요! 😊 은퇴 계획 계산기에 오신 것을 환영합니다. 이 도구는 여러분의 은퇴 후 재정 상황을 예측하고 준비하는 데 도움을 줄 거예요. 아래는 이 계산기의 작동 방식과 필요한 정보입니다. 
+
+        #     ### 1. 입력 값 📊
+
+        #     계산기에서 다음 정보를 입력해 주세요:
+
+        #     - **현재 나이 (current_age)**: 현재 나이 🎂
+        #     - **은퇴 나이 (retirement_age)**: 은퇴하고 싶은 나이 🎉
+        #     - **평생 기대 수명 (life_expectancy)**: 예상되는 평생 수명 🌟
+        #     - **월별 필요한 생활비 (monthly_income_required)**: 매월 필요한 생활비 💵
+        #     - **인플레이션율 (inflation_rate)**: 물가 상승률 📈
+        #     - **현재 투자액 (current_investment)**: 현재 보유한 투자액 💰
+        #     - **월별 추가 투자액 (monthly_investment)**: 매월 추가로 투자할 금액 💸
+        #     - **월 투자액의 연간 증가액 (annual_increase_in_monthly_investment)**: 매월 증가할 투자액(매년 월 적립식 투자금에서 늘려나갈 금액. (ex: 매년 10만원씩 늘릴 예정이라면, 100,000 입력)) 📈
+        #     - **배당금 재투자 여부 (reinvest_dividends)**: 배당금을 재투자할지 여부 🔄
+        #     - **은퇴 전 수익률 (pre_retirement_roi)**: 은퇴 전 예상 연 수익률 📊
+        #     - **은퇴 전 배당 수익률 (pre_retirement_dividend_yield)**: 은퇴 전 배당금 수익률 📈
+        #     - **은퇴 후 수익률 (post_retirement_roi)**: 은퇴 후 예상 연 수익률 🌿
+        #     - **은퇴 후 배당 수익률 (post_retirement_dividend_yield)**: 은퇴 후 배당금 수익률 💵
+
+        #     ### 2. 계산 과정 🔢
+
+        #     **1) 은퇴 전 자산 계산**
+
+        #     현재 자산과 매월 추가 투자액을 고려해 은퇴 전 자산을 계산해요. 월별 수익률을 적용하여 자산이 어떻게 성장할지 예측합니다.
+
+        #     **공식:**
+        #     \[ \text{월간 수익률} = \left(1 + \frac{\text{연 수익률}}{100}\right)^{\frac{1}{12}} - 1 \]
+
+        #     **2) 은퇴 후 자산 및 배당 수익 계산**
+
+        #     은퇴 시점의 자산과 배당 수익을 기록하고, 은퇴 후 매년 자산과 배당 수익을 업데이트합니다. 매년 필요한 생활비도 물가 상승률에 맞춰 조정합니다.
+
+        #     **3) 추가 현금 필요량 계산**
+
+        #     배당 수익과 필요한 생활비를 비교하여 추가로 필요한 현금을 계산해요.
+
+        #     **공식:**
+        #     \[ \text{연간 필요한 생활비} = \text{월별 필요한 생활비} \times 12 \]
+        #     \[ \text{추가 현금 필요량} = \text{연간 필요한 생활비} - \text{연간 배당 수익} \]
+
+        #     ### 3. 결과 출력 📈
+
+        #     계산된 결과는 다음과 같은 형식으로 제공됩니다:
+
+        #     - **은퇴 직후 필요한 생활비**와 **배당 수익**을 한눈에 볼 수 있어요.
+        #     - 은퇴 후 매년 자산, 필요한 생활비, 배당 수익 등을 표로 확인할 수 있습니다.
+        #     - 추가로 필요한 현금을 계산하여 총 필요 현금을 보여줍니다.
+
+        #     ### 예제 🎁
+
+        #     **입력 값 예시:**
+
+        #     - 현재 나이: 40세
+        #     - 은퇴 나이: 65세
+        #     - 평생 기대 수명: 85세
+        #     - 월별 필요한 생활비: 2,000,000 원
+        #     - 인플레이션율: 2%
+        #     - 현재 투자액: 50,000,000 원
+        #     - 월별 추가 투자액: 500,000 원
+        #     - 월별 투자 증가액: 50,000 원
+        #     - 배당금 재투자 여부: 예
+        #     - 은퇴 전 수익률: 5%
+        #     - 은퇴 전 배당 수익률: 2%
+        #     - 은퇴 후 수익률: 4%
+        #     - 은퇴 후 배당 수익률: 2%
+
+        #     **출력 결과:**
+
+        #     - 은퇴 직후 월별 필요한 생활비: 2,000,000 원
+        #     - 은퇴 직후 월별 배당 수익: 500,000 원
+        #     - 총 추가 현금 필요량: 매년 추가로 필요한 금액
+
+        #     **그래프와 CSV 파일 📉:**
+
+        #     - 은퇴 계획을 시각적으로 보여주는 그래프와 계산 결과를 포함한 CSV 파일 다운로드 링크도 제공됩니다.
+
+        #     은퇴 계획 계산기를 통해 여러분의 재정 계획을 미리 준비하고, 안정적인 미래를 계획해보세요! 🌟💼
+
+
+        #     ### 면책 조항
+        #     이 도구에서 제공하는 정보는 일반적인 정보 제공 목적만을 위해 제공됩니다. 사이트의 모든 정보는 선의로 제공되지만, 사이트의 정보의 정확성, 적절성, 유효성, 신뢰성, 가용성 또는 완전성에 대해 명시적이든 묵시적이든 어떠한 종류의 진술이나 보증을 하지 않습니다. 사이트의 사용과 사이트의 정보를 신뢰하는 것은 전적으로 사용자의 책임입니다.
+        #     """,
+        #     """
+        #     ## 🏖️ Retirement Planning Calculator Guide
+
+        #     Welcome to the Retirement Planning Calculator! 😊 This tool helps you forecast and prepare for your financial situation after retirement. Here’s how it works and what information you need to provide:
+
+        #     ### 1. Input Values 📊
+
+        #     Please provide the following details:
+
+        #     - **Current Age (current_age)**: Your current age 🎂
+        #     - **Retirement Age (retirement_age)**: Age at which you plan to retire 🎉
+        #     - **Life Expectancy (life_expectancy)**: Expected lifespan 🌟
+        #     - **Monthly Income Required (monthly_income_required)**: Monthly expenses needed 💵
+        #     - **Inflation Rate (inflation_rate)**: Rate of inflation 📈
+        #     - **Current Investment (current_investment)**: Amount currently invested 💰
+        #     - **Monthly Investment (monthly_investment)**: Additional monthly investment 💸
+        #     - **Annual Increase in Monthly Investment (annual_increase_in_monthly_investment)**: Increase in monthly investment (For example, if you plan to increase your monthly investment by $100,000 each year, enter 100,000.) 📈
+        #     - **Reinvest Dividends (reinvest_dividends)**: Whether to reinvest dividends 🔄
+        #     - **Pre-Retirement ROI (pre_retirement_roi)**: Expected annual return before retirement 📊
+        #     - **Pre-Retirement Dividend Yield (pre_retirement_dividend_yield)**: Dividend yield before retirement 📈
+        #     - **Post-Retirement ROI (post_retirement_roi)**: Expected annual return after retirement 🌿
+        #     - **Post-Retirement Dividend Yield (post_retirement_dividend_yield)**: Dividend yield after retirement 💵
+
+        #     ### 2. Calculation Process 🔢
+
+        #     **1) Pre-Retirement Asset Calculation**
+
+        #     The calculator uses your current assets and monthly contributions to forecast your asset growth before retirement. Monthly returns are applied to estimate how your assets will grow.
+
+        #     **Formula:**
+        #     \[ \text{Monthly Return} = \left(1 + \frac{\text{Annual ROI}}{100}\right)^{\frac{1}{12}} - 1 \]
+
+        #     **2) Post-Retirement Asset and Dividend Income Calculation**
+
+        #     The calculator tracks your assets and dividend income at retirement, then updates these figures annually. It also adjusts monthly income needs for inflation.
+
+        #     **3) Additional Cash Needed Calculation**
+
+        #     It compares dividend income with required income to determine if additional cash is needed.
+
+        #     **Formula:**
+        #     \[ \text{Annual Income Required} = \text{Monthly Income Required} \times 12 \]
+        #     \[ \text{Additional Cash Required} = \text{Annual Income Required} - \text{Annual Dividend Income} \]
+
+        #     ### 3. Results 📈
+
+        #     The results are displayed as follows:
+
+        #     - **Immediate Post-Retirement Income Needs** and **Dividend Income** for easy comparison.
+        #     - Annual asset, required income, and dividend income details in a table.
+        #     - Total additional cash required to cover any shortfall.
+
+        #     ### Example 🎁
+
+        #     **Example Input Values:**
+
+        #     - Current Age: 40 years
+        #     - Retirement Age: 65 years
+        #     - Life Expectancy: 85 years
+        #     - Monthly Income Required: $2,000
+        #     - Inflation Rate: 2%
+        #     - Current Investment: $50,000
+        #     - Monthly Investment: $500
+        #     - Monthly Increase in Investment: $50
+        #     - Reinvest Dividends: Yes
+        #     - Pre-Retirement ROI: 5%
+        #     - Pre-Retirement Dividend Yield: 2%
+        #     - Post-Retirement ROI: 4%
+        #     - Post-Retirement Dividend Yield: 2%
+
+        #     **Output Results:**
+
+        #     - Monthly Income Required Immediately After Retirement: $2,000
+        #     - Monthly Dividend Income Immediately After Retirement: $500
+        #     - Total Additional Cash Required: Amount needed annually
+
+        #     **Graph and CSV File 📉:**
+
+        #     - Visual graphs of the retirement plan and a CSV file with detailed calculations are available for download.
+
+        #     Use the Retirement Planning Calculator to prepare for your future and ensure a secure retirement! 🌟💼
+
+        #     ### Disclaimer
+        #     The information provided by this tool is for general informational purposes only. All information on the site is provided in good faith, but we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the site. Your use of the site and reliance on any information on the site is solely at your own risk.
+        #     """
+        # )
+        gr.Markdown("""
+            <h2 style='margin: 5px'>Support us</h2>
+            
+            If you find this tool useful and would like to support the development of such projects, please consider making a donation. Your support is greatly appreciated.
+
+            [![Donate with PayPal](https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=M8SBRC396DPBW)
+
+            Or, if you prefer, you can also support us through Toss at:
+
+            <a href="https://toss.me/eichijei" target="_blank">
+                <img src="https://static.toss.im/logos/png/1x/logo-toss.png" alt="Donate with Toss" style="width: 150px;">
+            </a>
+            """)
+

interface/dollar_cost_averaging_interface.py

@@ -0,0 +1,22 @@
+import gradio as gr
+from modules.dollar_cost_averaging import dollar_cost_averaging
+
+examples = [
+    [1000, 9000, 560, 20000]
+]
+# Define the inputs
+title = gr.Markdown("<h2 style='margin: 5px'>Dollar Cost Averaging Calculator</h2>")
+first_purchase = gr.Markdown("<h3 class='h3_title'>FIRST PURCHASE</h3>")
+old_price = gr.Textbox(label="Old Price", value=7850)
+old_quantity = gr.Textbox(label="Quantity", value=70)
+second_purchase = gr.Markdown("<h3 class='h3_title'>SECOND PURCHASE</h3>")
+new_price = gr.Textbox(label="New Price", value=6700)
+new_quantity = gr.Textbox(label="Quantity", value=70)
+
+input = [old_price, old_quantity, new_price, new_quantity]
+output = gr.HTML()
+component_rows= [first_purchase, old_price, old_quantity, second_purchase, new_price, new_quantity]
+
+# Define the update function
+def update_output(*args):
+    return dollar_cost_averaging(*args)

interface/rebalancing_interface.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from modules.rebalancing import rebalancing_tool
+from modules.utils import currency_codes, current_time
+
+examples = [
+    ["KRW", "458730 KRW 580 [8],\n368590 KRW 80 [2],", 507977],
+    ["KRW", "SCHD USD 500 [8],\nQQQ USD 200 [2],\n458730 KRW 580 [8],\n368590 KRW 80 [2],", 10000000]
+]
+
+# Define Gradio components
+main_currency = gr.Dropdown(
+    label="Main Currency",
+    choices=currency_codes,
+    value="KRW",
+    # info="Assets converted to main currency"
+)
+title_setup = gr.Markdown(f"""<h3 class='h3_title'>Portfolio Holdings</h3>""")
+holdings = gr.Textbox(
+    label="Portfolio [Ratio]",
+    lines=1,
+    # info="Format: Ticker Currency Quantity [Target_Weight], ...",
+    placeholder="Format: Ticker Currency Quantity [Target_Weight], ...",
+    value="SCHD USD 7 [3], 458730 KRW 580 [4], 368590 KRW 80 [1],"
+)
+title_cash = gr.Markdown("<h3 class='h3_title'>Contribution or withdrawal</h3>")
+cash_amount = gr.Number(
+    label="amount",
+    value=507977
+)
+allow_sales = gr.Checkbox(
+    value=True,
+    label="If checked, the table below will display both buy and sell transactions. If unchecked, only buy transactions will be shown."
+)
+
+input = [main_currency, holdings, cash_amount, allow_sales]
+output = gr.HTML()
+component_rows = [title_setup, main_currency, holdings, title_cash, cash_amount, allow_sales]
+
+# Define the update function
+def update_output(*args):
+    return rebalancing_tool(*args)

interface/retirement_planning_interface.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from modules.retirement_planning import retirement_planning
+
+# Define examples for retirement planning
+examples = [
+    [38, 55, 85, 2000000, 3.0, 10000000, 500000, 0, True, 8, 8, 3.3, 3.3]
+]
+
+# Define the input components
+title = gr.Markdown("<h2 style='margin: 5px'>Retirement Planning</h2>")
+title_profile = gr.Markdown("<h3 class='h3_title'>PROFILE</h3>")
+current_age = gr.Slider(label="Current Age (15-60 Years)", value=28, minimum=15, maximum=60, step=1)
+retirement_age = gr.Slider(label="Retirement Age (Upto 70 Years)", value=55, minimum=15, maximum=70, step=1)
+life_expectancy = gr.Slider(label="Life Expectancy (Upto 100 Years)", value=80, minimum=30, maximum=100, step=1)
+title_savings = gr.Markdown("<h3 class='h3_title'>SAVINGS</h3>")
+monthly_income_required = gr.Number(label="Monthly Income Required (CPP)", value=2000000)
+inflation_rate = gr.Number(label="Inflation Rate (%)", value=3)
+current_investment = gr.Number(label="Current Investment", value=10000000)
+monthly_investment = gr.Number(label="Monthly Investment", value=500000)
+annual_increase_in_monthly_investment = gr.Number(label="Annual Increase in Monthly Investment", value=0)  # 추가된 입력
+reinvest_dividends = gr.Checkbox(label="Reinvest Dividends", value=True)
+title_growth = gr.Markdown("<h3 class='h3_title'>GROWTH</h3>")
+pre_retirement_roi = gr.Number(label="Return On Investment (Pre-retirement) (%)", value=8)
+post_retirement_roi = gr.Number(label="Return On Investment (Post-retirement) (%)", value=8)
+pre_retirement_dividend_yield = gr.Number(label="Dividend Yield (Pre-retirement) (%)", value=3.3)
+post_retirement_dividend_yield = gr.Number(label="Dividend Yield (Post-retirement) (%)", value=3.3)
+
+input = [current_age, retirement_age, life_expectancy, monthly_income_required, inflation_rate, current_investment, monthly_investment, annual_increase_in_monthly_investment, reinvest_dividends, pre_retirement_roi, post_retirement_roi, pre_retirement_dividend_yield, post_retirement_dividend_yield]
+output = gr.HTML()
+component_rows = [
+    current_age, retirement_age, life_expectancy,[monthly_income_required, inflation_rate],
+    current_investment, monthly_investment, annual_increase_in_monthly_investment, [reinvest_dividends],
+    [pre_retirement_roi, post_retirement_roi], [pre_retirement_dividend_yield, post_retirement_dividend_yield]
+]
+
+# Define the update function
+def update_output(*args):
+    return retirement_planning(*args)
+

interface/share_price_trend_interface.py

@@ -0,0 +1,30 @@
+import gradio as gr
+from modules.share_price_trend import share_price_trend
+
+examples = [
+    ["AAPL,GOOGL,MSFT", 90],
+    ["SCHD,QQQ", 365]
+]
+
+# Define Gradio components for inputs
+title = gr.Markdown("<h2 style='margin: 5px'>share price trend</h2>")
+stock_codes = gr.Textbox(
+    label="Stock Codes",
+    # info="Enter stock codes separated by comma.",
+    placeholder="e.g., AAPL,GOOGL,MSFT",
+    value="SCHD,QQQ"
+)
+period = gr.Slider(
+    label="Number of Days",
+    value=90,
+    maximum=3650
+)
+
+# Define output component
+input = [stock_codes, period]
+output = gr.HTML()
+component_rows = [[stock_codes], [period]]
+
+# Define the update function
+def update_output(*args):
+    return share_price_trend(*args)

modules/dollar_cost_averaging.py

@@ -0,0 +1,101 @@
+from modules.utils import load_css
+
+def calculate_dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
+    # 입력값을 숫자로 변환 (각 입력값이 None일 경우 0.0으로 설정)
+    old_avg_price = float(old_avg_price) if old_avg_price else 0.0
+    old_quantity = float(old_quantity) if old_quantity else 0.0
+    new_price = float(new_price) if new_price else 0.0
+    new_quantity = float(new_quantity) if new_quantity else 0.0
+
+    # 현재 투자 금액 계산 (이전 평균 가격 * 이전 수량)
+    current_investment = old_avg_price * old_quantity
+    # 추가 투자 금액 계산 (새 가격 * 새 수량)
+    additional_investment = new_price * new_quantity
+    # 총 투자 금액 계산 (현재 투자 금액 + 추가 투자 금액)
+    total_investment = current_investment + additional_investment
+    # 총 주식 수 계산 (이전 수량 + 새 수량)
+    total_quantity = old_quantity + new_quantity
+    # 새 평균 가격 계산 (총 투자 금액 / 총 주식 수)
+    new_avg_price = total_investment / total_quantity if total_quantity != 0 else 0.0
+    # 이전 수익률 계산 (이전 평균 가격을 기준으로)
+    old_return = (new_price / old_avg_price - 1) * 100 if old_avg_price != 0 else 0.0
+    # 새로운 수익률 계산 (새 가격 / 새 평균 가격 - 1) * 100
+    new_return = (new_price / new_avg_price - 1) * 100 if new_avg_price != 0 else 0.0
+    
+    # 새 평균 가격, 총 수량, 총 투자 금액, 새로운 수익률, 추가 투자 금액, 이전 수익률 반환
+    return new_avg_price, total_quantity, total_investment, new_return, additional_investment, old_return
+
+def dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
+    css = load_css()
+
+    # 입력값을 숫자로 변환
+    old_avg_price = float(old_avg_price) if old_avg_price else 0.0
+    old_quantity = float(old_quantity) if old_quantity else 0.0
+    new_price = float(new_price) if new_price else 0.0
+    new_quantity = float(new_quantity) if new_quantity else 0.0
+
+    # 평균 가격, 총 수량, 총 투자 금액, 수익률 및 추가 투자 금액 계산
+    new_avg_price, total_quantity, total_investment, new_return, additional_investment, old_return = calculate_dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity)
+
+    # 수익률에 따른 클래스 설정
+    emoji_return = ""
+    new_return_class = ""
+    old_return_class = ""
+    if new_return > old_return:
+        emoji_return = "💧"
+    elif new_return < old_return:
+        emoji_return = "🔥"
+    else: 
+        emoji_return = ""
+
+    if new_return > 0:
+        new_return_class = f"<span style='color: #4caf50; font-weight: bold;'>{new_return:+,.2f}%</span>"
+    elif new_return < 0:
+        new_return_class = f"<span style='color: #f44336; font-weight: bold;'>{new_return:,.2f}%</span>"
+    else:
+        new_return_class = f"<span><strong>0</strong></span>"
+
+    if old_return > 0:
+        old_return_class = f"<span style='color: #4caf50; font-weight: bold;'>{old_return:+,.2f}%</span>"
+    elif old_return < 0:
+        old_return_class = f"<span style='color: #f44336; font-weight: bold;'>{old_return:,.2f}%</span>"
+    else:
+        old_return_class = f"<span><strong>0</strong></span>"
+
+    # HTML 결과 생성
+    result_html = css + f"""
+    <div class="wrap-text">
+        <div>
+            <div style="margin-bottom: 1.5rem;">
+                <!-- 새로운 수익률 표시 -->
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">{emoji_return}</div>
+                <div style="font-size: 1.5rem;">
+                    {old_return_class} ➜ {new_return_class}
+                </div>
+                <hr style="margin: 1.5rem 0;">
+            </div>
+        </div>
+        <div>
+            <div style="margin-bottom: 1.5rem;">
+                <!-- 평균 가격 표시 -->
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Average Price</div>
+                <div style="font-size: 1.5rem; font-weight: bold; color: #1c75bc;">
+                    <span style='color: #1678fb'>{new_avg_price:,.0f}</span>
+                </div>
+                <hr style="margin: 1.5rem 0;">
+            </div>
+        </div>
+        <div>
+            <div style="margin-bottom: 1.5rem;">
+                <!-- 추가 투자 금액 표시 -->
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Transaction</div>
+                <div style="font-size: 1.5rem; font-weight: bold; color: #1c75bc;">
+                    <span style='color: #1678fb'>{additional_investment:,.0f}</span>
+                </div>
+                <hr style="margin: 1.5rem 0;">
+            </div>
+        </div>
+
+    </div>
+    """
+    return result_html

modules/rebalancing.py

@@ -0,0 +1,261 @@
+import math
+import FinanceDataReader as fdr
+import yfinance as yf
+from concurrent.futures import ThreadPoolExecutor
+from modules.utils import load_css, get_currency_symbol, format_quantity, plot_treemap
+
+def parse_input(holdings, cash_amount):
+    try:
+        lines = holdings.strip().split(',')
+        stock_inputs = []
+        total_target_ratio = 0
+        
+        for line in lines:
+            parts = line.split()
+            if len(parts) == 4:
+                stock_code, currency_code, quantity_expr, target_ratio_expr = parts
+                # 수량과 비중을 숫자로 변환
+                quantity = float(quantity_expr.replace(' ', ''))
+                # 목표 비율이 빈 대괄호일 경우 0으로 처리
+                if target_ratio_expr.strip() == '[]':
+                    target_ratio = 0
+                else:
+                    target_ratio = float(eval(target_ratio_expr.strip('[]').replace(' ', '')))
+                
+                # 수량을 정수로 내림 처리
+                quantity = math.floor(quantity)
+                
+                stock_inputs.append((currency_code.upper(), stock_code, quantity, target_ratio))
+                total_target_ratio += target_ratio
+
+        return stock_inputs, cash_amount
+    except Exception as e:
+        raise ValueError(f"Input parsing error: {e}")
+
+def get_portfolio_exchange_rate(currency_code, main_currency):
+    try:
+        if currency_code.lower() == main_currency.lower():
+            return 1.0
+        
+        ticker = f"{currency_code.upper()}{main_currency.upper()}=X"
+        data = yf.download(ticker, period='1d', progress=False)
+        if not data.empty:
+            return data['Close'].iloc[0]
+        else:
+            raise ValueError("<p style='color: red;'>Failed to retrieve exchange rate data.</p>")
+    except Exception as e:
+        raise ValueError(f"<p style='color: red;'>Exchange rate retrieval error: {e}</p>")
+
+def get_portfolio_exchange_reflected_stock_price(stock_code, currency_code, main_currency):
+    try:
+        new_price = get_portfolio_current_stock_price(stock_code)
+        exchange_rate = get_portfolio_exchange_rate(currency_code, main_currency)
+        return math.floor(new_price * exchange_rate)
+    except Exception as e:
+        raise ValueError(f"<p style='color: red;'>Exchange reflected stock price error: {e}</p>")
+
+def get_portfolio_current_stock_price(stock_code):
+    try:
+        df = fdr.DataReader(stock_code)
+        return df['Close'].iloc[-1]
+    except Exception as e:
+        raise ValueError(f"<p style='color: red;'>Current stock price retrieval error: {e}</p>")
+
+def build_portfolio(stock_inputs, main_currency):
+    portfolio = {}
+    target_ratios = {}
+    
+    with ThreadPoolExecutor() as executor:
+        results = executor.map(lambda x: (x[1], get_portfolio_exchange_reflected_stock_price(x[1], x[0], main_currency), x[2], x[3], x[0]), stock_inputs)
+    
+    total_value = 0
+    for stock_code, new_price, quantity, target_ratio, currency_code in results:
+        portfolio[stock_code] = {'quantity': quantity, 'price': new_price, 'currency': currency_code}
+        target_ratios[stock_code] = target_ratio
+        total_value += new_price * quantity
+
+    # 목표 비율의 합이 0인 경우, 기본 비율 설정
+    total_target_ratio = sum(target_ratios.values())
+    if total_target_ratio == 0:
+        # 기본 비율을 설정하거나, 균등 분배
+        num_stocks = len(stock_inputs)
+        default_ratio = num_stocks / num_stocks
+        for stock_code in target_ratios:
+            target_ratios[stock_code] = default_ratio
+
+    return portfolio, target_ratios, total_value
+
+def generate_portfolio_info(portfolio, target_ratios, total_value, main_currency, cash_amount):
+    css = load_css()
+    currency_symbol = get_currency_symbol(main_currency)
+    
+    # 현재 상태 차트
+    current_weights = {stock_code: (stock['price'] * stock['quantity'] / total_value) * 100 for stock_code, stock in portfolio.items()}
+    current_chart = plot_treemap(current_weights)
+
+    portfolio_info = css + f"""
+    <div class="wrap-text">
+        <div>
+            <div style="margin-bottom: 1.5rem;">
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Your Current Portfolio (Before Trades)</div>
+                    <span style='font-size: 1.5rem; font-weight: bold; color: #1678fb'>{currency_symbol}{format_quantity(total_value)}</span>
+                <hr style="margin: 1.5rem 0;">
+                {current_chart}
+            </div>
+        </div>
+    <br>
+    """
+
+    # 현재 비율 및 가치를 계산
+    current_values = {stock_code: stock['price'] * stock['quantity'] for stock_code, stock in portfolio.items()}
+    sorted_stocks = current_values.items()
+
+    # 현재 보유량 및 가치 섹션 표시
+    current_info_html = "<h3>Your Portfolio Holdings</h3><div class='table-container'><table>"
+    current_info_html += "<thead><tr><th>Stock Code</th><th>Current Weight (%)</th><th>Current Value</th></tr></thead><tbody>"
+    for stock_code, current_value in sorted_stocks:
+        weight = (current_value / total_value) * 100
+        current_info_html += (
+            f"<tr>"
+            f"<td>{stock_code.upper()}</td>"
+            f"<td>{weight:.1f}%</td>"
+            f"<td>{currency_symbol}{current_value:,.0f}</td>"
+            f"</tr>"
+        )
+    current_info_html += "</tbody></table></div><br>"
+
+    # 통화별 포트폴리오 요약 생성
+    currency_totals = {currency: {'amount': 0, 'weight': 0} for currency in set(stock_data['currency'] for stock_data in portfolio.values())}
+    for stock_code, stock_data in portfolio.items():
+        currency = stock_data['currency']
+        current_value = stock_data['price'] * stock_data['quantity']
+        currency_totals[currency]['amount'] += current_value
+        currency_totals[currency]['weight'] += current_value / total_value
+
+    sorted_currencies = currency_totals.items()
+
+    # 통화별 요약 테이블 생성
+    currency_table = "<h3>Your Portfolio by Currency</h3><div class='table-container wrap-text'><table>"
+    currency_table += "<thead><tr><th>Currency</th><th>Total Weight (%)</th><th>Total Value</th></tr></thead><tbody>"
+    for currency, data in sorted_currencies:
+        currency_table += (
+            f"<tr>"
+            f"<td>{currency.upper()}</td>"
+            f"<td>{data['weight'] * 100:.1f}%</td>"
+            f"<td>{currency_symbol}{data['amount']:,}</td>"            
+            f"</tr>"
+        )
+    currency_table += "</tbody></table></div><br>"
+
+    return portfolio_info + current_info_html + currency_table
+
+def generate_rebalancing_analysis(portfolio, target_ratios, total_value, main_currency, cash_amount, allow_selling):
+    currency_symbol = get_currency_symbol(main_currency)
+    
+    total_trade_value = 0
+    adjustments = []
+    new_total_value = cash_amount + total_value
+
+    for stock_code, stock_data in portfolio.items():
+        current_value = stock_data['price'] * stock_data['quantity']
+        target_ratio = target_ratios.get(stock_code, 0)
+        target_weight = target_ratio / sum(target_ratios.values())
+        target_value = new_total_value * target_weight
+        difference = target_value - current_value
+
+        if not allow_selling and difference < 0:
+            # 매도를 허용하지 않는 경우, 매도는 생략
+            trade_quantity = 0
+        else:
+            trade_quantity = math.floor(difference / stock_data['price']) if difference > 0 else -math.ceil(-difference / stock_data['price'])
+        
+        # 거래가 가능한지 확인 (현금 잔고 확인 포함)
+        if trade_quantity > 0 and not allow_selling:
+            trade_quantity = min(trade_quantity, cash_amount // stock_data['price'])
+        
+        new_quantity = trade_quantity + stock_data['quantity']
+        new_value = new_quantity * stock_data['price']
+        trade_value = trade_quantity * stock_data['price']
+        total_trade_value += abs(trade_value)
+        
+        current_value_pct = (current_value / total_value) * 100
+        adjustments.append((difference, current_value, target_ratio, current_value_pct, trade_quantity, stock_code, stock_data['price'], new_value, trade_value, stock_data['quantity'], new_quantity, target_weight, stock_data['currency']))
+
+    total_new_stock_value = sum(adj[7] for adj in adjustments)
+    new_weights = {stock_code: (new_value / total_new_stock_value) * 100 for _, _, _, _, _, stock_code, _, new_value, _, _, _, _, _ in adjustments}
+    new_chart = plot_treemap(new_weights)
+
+    for i in range(len(adjustments)):
+        (difference, current_value, target_ratio, current_value_pct, trade_quantity, stock_code, price, new_value, trade_value, old_quantity, new_quantity, target_weight, currency) = adjustments[i]
+        new_value_pct = (new_value / total_new_stock_value) * 100
+        adjustments[i] = (difference, current_value, target_ratio, current_value_pct, trade_quantity, stock_code, price, new_value, trade_value, old_quantity, new_quantity, target_weight, new_value_pct, currency)
+
+    cash_balance = new_total_value - total_new_stock_value
+
+    result_message = f"""
+    <div>
+        <div style="margin-bottom: 1.5rem;">
+            <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Your Adjusted Portfolio (After Trades)</div>
+            <span style='font-size: 1.5rem; font-weight: bold; color: #1678fb'>{currency_symbol}{format_quantity(total_new_stock_value)}</span>
+            Adjusted Portfolio
+            <div></div>
+            <span style='font-size: 1.5rem; font-weight: bold; color: #1678fb'>{currency_symbol}{format_quantity(cash_balance)}</span>
+            cash balance
+            <hr style="margin: 1.5rem 0;">
+            {new_chart}            
+        </div>
+    </div>
+    <br>
+    <h3>Trades to Re-Balance Your Portfolio</h3>
+    <div class='table-container wrap-text'><table>"""
+    result_message += "<thead><tr><th>Stock Code</th><th>Current Weight (%)</th><th>Target Ratio</th><th>Target Weight (%)</th><th>Buy or Sell?</th><th>Trade Amount</th><th>Current Price per Share</th><th>Estimated # of<br> Shares to Buy or Sell</th><th>Quantity of Units</th><th>Market Value</th><th>% Asset Allocation</th></tr></thead><tbody>"
+
+    for (difference, current_value, target_ratio, current_value_pct, trade_quantity, stock_code, price, new_value, trade_value, old_quantity, new_quantity, target_weight, new_value_pct, currency) in adjustments:
+        Buy_or_Sell = ""
+        if trade_quantity > 0:
+            Buy_or_Sell = f"<span class='buy-sell buy'>Buy</span>"
+        elif trade_quantity < 0:
+            Buy_or_Sell = f"<span class='buy-sell sell'>Sell</span>"
+        else:
+            Buy_or_Sell = f"<span></span>"
+
+        current_value_pct_str = f"{current_value_pct:.1f}%"
+        target_ratio_str = f"<span class='highlight-edit'>{target_ratio}</span>"
+        target_weight_str = f"{target_weight * 100:.1f}%"
+        trade_value_str = f"<span class='highlight-sky'>{format_quantity(trade_value)}</span>" if trade_value != 0 else ''
+        price_str = f"{currency_symbol}{price:,.0f}"         
+        trade_quantity_str = f"<span class='highlight-sky'>{format_quantity(trade_quantity)}</span>" if trade_value != 0 else ''
+        old_quantity_str = f"{old_quantity:,.0f} → {new_quantity:,.0f}" 
+        new_value_str = f"{currency_symbol}{new_value:,.0f}"
+        new_value_pct_str = f"{new_value_pct:.1f}%"
+
+        result_message += (
+            f"<tr>"
+            f"<td>{stock_code.upper()}</td>"
+            f"<td>{current_value_pct_str}</td>"
+            f"<td>{target_ratio_str}</td>"
+            f"<td>{target_weight_str}</td>"
+            f"<td>{Buy_or_Sell}</td>"
+            f"<td>{trade_value_str}</td>"
+            f"<td>{price_str}</td>"
+            f"<td>{trade_quantity_str}</td>"
+            f"<td>{old_quantity_str}</td>"
+            f"<td>{new_value_str}</td>"
+            f"<td>{new_value_pct_str}</td>"
+            f"</tr>"
+        )
+
+    result_message += "</tbody></table><br></div></div>"
+
+    return result_message
+
+def rebalancing_tool(main_currency, holdings, cash_amount, allow_selling):
+    try:
+        stock_inputs, cash_amount = parse_input(holdings, cash_amount)
+        portfolio, target_ratios, total_value = build_portfolio(stock_inputs, main_currency)
+        portfolio_info = generate_portfolio_info(portfolio, target_ratios, total_value, main_currency, cash_amount)
+        rebalancing_analysis = generate_rebalancing_analysis(portfolio, target_ratios, total_value, main_currency, cash_amount, allow_selling)
+
+        return portfolio_info + rebalancing_analysis
+    except Exception as e:
+        return f"<p style='color: red;'>An error occurred: {e}</p>"

modules/retirement_planning.py

@@ -0,0 +1,222 @@
+import base64
+import csv
+from io import StringIO
+import matplotlib.pyplot as plt
+import io
+from modules.utils import load_css
+
+def retirement_planning(
+    current_age=None, 
+    retirement_age=None, 
+    life_expectancy=None, 
+    monthly_income_required=None, 
+    inflation_rate=None, 
+    current_investment=None, 
+    monthly_investment=None, 
+    annual_increase_in_monthly_investment=None,  # 추가된 입력
+    reinvest_dividends=None, 
+    pre_retirement_roi=None, 
+    post_retirement_roi=None, 
+    pre_retirement_dividend_yield=None, 
+    post_retirement_dividend_yield=None
+):
+    # NoneType일 때 0으로 처리
+    current_age = current_age if current_age is not None else 0
+    retirement_age = retirement_age if retirement_age is not None else 0
+    current_investment = current_investment if current_investment is not None else 0
+    monthly_investment = monthly_investment if monthly_investment is not None else 0
+    annual_increase_in_monthly_investment = annual_increase_in_monthly_investment if annual_increase_in_monthly_investment is not None else 0
+    pre_retirement_roi = pre_retirement_roi if pre_retirement_roi is not None else 0
+    post_retirement_roi = post_retirement_roi if post_retirement_roi is not None else 0
+    pre_retirement_dividend_yield = pre_retirement_dividend_yield if pre_retirement_dividend_yield is not None else 0
+    post_retirement_dividend_yield = post_retirement_dividend_yield if post_retirement_dividend_yield is not None else 0
+    life_expectancy = life_expectancy if life_expectancy is not None else 0
+    monthly_income_required = monthly_income_required if monthly_income_required is not None else 0
+    inflation_rate = inflation_rate if inflation_rate is not None else 0
+
+    # 은퇴 전후의 년 수 계산
+    if retirement_age > life_expectancy:
+        return "<p style='color: red;'>Error: Retirement age cannot be greater than life expectancy.</p>"
+    
+    if retirement_age < current_age:
+        return "<p style='color: red;'>Error: Retirement age cannot be less than current age.</p>"
+    
+    years_to_retirement = retirement_age - current_age
+    post_retirement_years = life_expectancy - retirement_age
+
+    # 현재 투자액으로 초기 투자 설정
+    total_investment = current_investment
+
+    # 은퇴 전 월간 이자율 계산
+    monthly_return_pre = (1 + pre_retirement_roi / 100) ** (1 / 12) - 1
+
+    # 은퇴 시점의 투자 계산
+    for year in range(years_to_retirement):
+        for month in range(12):
+            # 월간 투자액과 이자율을 적용하여 총 투자액 갱신
+            total_investment = (total_investment + monthly_investment) * (1 + monthly_return_pre)
+            # 배당금을 재투자할 경우 배당금 추가
+            if reinvest_dividends:
+                total_investment += total_investment * (pre_retirement_dividend_yield / 100 / 12)
+        # 연간 증가액을 월 투자액에 추가
+        monthly_investment += annual_increase_in_monthly_investment
+
+    # 은퇴 시작 시점의 총 투자액과 연간 배당 수익 저장
+    investment_at_retirement = total_investment
+    annual_dividend_at_retirement = investment_at_retirement * (pre_retirement_dividend_yield / 100)
+    monthly_dividend_at_retirement = annual_dividend_at_retirement / 12
+
+    # 은퇴 후 월간 이자율 계산
+    monthly_return_post = (1 + post_retirement_roi / 100) ** (1 / 12) - 1
+
+    # 연간 물가상승률을 반영한 월 생활비 계산
+    monthly_income_required_inflated = monthly_income_required
+    monthly_income_required_over_time = []
+    for age in range(current_age, life_expectancy + 1):
+        if age >= retirement_age:
+            monthly_income_required_over_time.append((age, monthly_income_required_inflated))
+        monthly_income_required_inflated *= (1 + inflation_rate / 100 / 12) ** 12
+
+    annual_income_required_at_retirement = monthly_income_required_over_time[0][1] * 12
+    monthly_income_required_at_retirement = monthly_income_required_over_time[0][1]
+
+    # 은퇴 후 투자 목록 초기화
+    post_retirement_investments = [(retirement_age, investment_at_retirement, annual_income_required_at_retirement, annual_dividend_at_retirement, monthly_dividend_at_retirement, monthly_income_required_at_retirement, annual_dividend_at_retirement - annual_income_required_at_retirement)]
+
+    # 은퇴 후 각 년도의 투자 및 배당 수익 계산
+    for year in range(1, post_retirement_years + 1):
+        # 은퇴 후 수익률을 적용하여 총 투자액 갱신
+        total_investment *= (1 + post_retirement_roi / 100)
+        # 연간 배당 수익 계산
+        annual_dividend_income = total_investment * (post_retirement_dividend_yield / 100)
+        # 월간 배당 수익 계산
+        monthly_dividend_income = annual_dividend_income / 12
+        # 연도별 물가상승률 반영한 월 생활비 갱신
+        inflated_income_required = monthly_income_required_over_time[year][1] if year < len(monthly_income_required_over_time) else monthly_income_required_over_time[-1][1]
+        # 각 연도별 투자와 배당 수익 및 월 생활비를 리스트에 추가
+        difference = annual_dividend_income - inflated_income_required * 12
+        post_retirement_investments.append((retirement_age + year, total_investment, inflated_income_required * 12, annual_dividend_income, monthly_dividend_income, inflated_income_required, difference))
+
+    # 마이너스 값의 difference 합계 계산
+    negative_differences_sum = sum(diff for _, _, _, _, _, _, diff in post_retirement_investments if diff < 0)
+
+    # CSV 파일 생성
+    csv_output = StringIO()
+    csv_writer = csv.writer(csv_output)
+    csv_writer.writerow(['Age', 'SAVINGS', 'Annual Income Required', 'Annual Dividend Income', 'Monthly Income Required', 'Monthly Dividend Income', 'Additional Cash Required'])
+    for age, investment, annual_income_required, annual_dividend_income, monthly_dividend_income, income_required, difference in post_retirement_investments:
+        additional_cash_required = f'{abs(difference):,.0f}' if difference < 0 else ''
+        csv_writer.writerow([age, f'{investment:,.0f}', f'{annual_income_required:,.0f}', f'{income_required:,.0f}', f'{annual_dividend_income:,.0f}', f'{monthly_dividend_income:,.0f}', additional_cash_required])
+
+    csv_string = csv_output.getvalue().encode('utf-8')
+    csv_base64 = base64.b64encode(csv_string).decode('utf-8')
+
+    # style.css에서 CSS 읽기
+    css = load_css()
+
+    # SVG 그래프 생성
+    fig, ax = plt.subplots()
+    ages = [investment[0] for investment in post_retirement_investments]
+    income_required = [investment[2] for investment in post_retirement_investments]
+    dividend_income = [investment[3] for investment in post_retirement_investments]
+
+    ax.plot(ages, income_required, label='Income Required', color='red')
+    ax.plot(ages, dividend_income, label='Dividend Income', color='green')
+    ax.set_xlabel('Age')
+    ax.set_ylabel('Amount')
+    # ax.set_title('Retirement Plan Overview')
+    ax.legend()
+    ax.grid(True)
+
+    # 그래프를 SVG 형식으로 저장
+    svg_output = io.StringIO()
+    plt.savefig(svg_output, format='svg')
+    plt.close(fig)
+    svg_data = svg_output.getvalue()
+    svg_base64 = base64.b64encode(svg_data.encode('utf-8')).decode('utf-8')
+    
+    graph_html = f'<h3 style="margin-bottom: 0.5rem;">Retirement Planning: Income vs. Dividends</h3>'
+
+    # 은퇴 계획에 대한 HTML 결과 생성
+    result_html = css + f"""
+    <div class="wrap-text">
+        <div>
+            
+            <div style="margin-bottom: 1.5rem;">
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Total Additional Cash Required After Retirement</div>
+                    <span style='font-size: 1.5rem; font-weight: bold; color: #1678fb'>{abs(negative_differences_sum):,.0f}</span>
+                <hr style="margin: 1.5rem 0;">
+            </div>
+            <div style="margin-bottom: 1.5rem;">
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Income Required Immediately After Retirement</div>
+                    <span style='font-size: 1.5rem; font-weight: bold; color: #1678fb'>{annual_income_required_at_retirement:,.0f}</span>
+                    Annual
+                    <div></div>
+                    <span style='font-size: 1.5rem; font-weight: bold; color: #1678fb'>{monthly_income_required_at_retirement:,.0f}</span>
+                    MONTHLY
+                <hr style="margin: 1.5rem 0;">
+            </div>
+            <div style="margin-bottom: 1.5rem;">
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Dividend Income Immediately After Retirement</div>
+                    <span style='font-size: 1.5rem; font-weight: bold; color: #1678fb'>{annual_dividend_at_retirement:,.0f}</span>
+                    Annual
+                    <div></div>                    
+                    <span style='font-size: 1.5rem; font-weight: bold; color: #1678fb'>{monthly_dividend_at_retirement:,.0f}</span>
+                    MONTHLY
+                <hr style="margin: 1.5rem 0;">
+            </div>
+            <div style="margin-bottom: 1.5rem;">
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;"></div>
+                {graph_html}
+                <img src="data:image/svg+xml;base64,{svg_base64}" alt="Retirement Plan Graph" style="width: 100%; height: auto;"/>
+            </div>  
+        </div>
+    </div>
+    <div style="margin-bottom: 2rem;"></div>
+    <div style="display: flex; align-items: center; justify-content: space-between;">
+        <h3>Retirement Plan Overview</h3>
+        <a href="data:text/csv;base64,{csv_base64}" download="retirement_planning.csv" style="padding: 10px 20px; border: 1px solid; border-radius: 5px; background-color: #1678fb; color: white; text-decoration: none;">Download CSV</a>
+    </div>
+    <div class='table-container'>
+        <table>
+            <thead>
+                <tr>
+                    <th>Age</th>
+                    <th>SAVINGS</th>
+                    <th>Annual Income Required</th>
+                    <th>Monthly Income Required</th>
+                    <th>Annual Dividend Income</th>
+                    <th>Monthly Dividend Income</th>
+                    <th>Additional Cash Required</th>
+                </tr>
+            </thead>
+            <tbody>
+    """
+
+    # 각 연도별 투자와 배당 수익 및 월 생활비를 테이블에 추가
+    for age, investment, annual_income_required, annual_dividend_income, monthly_dividend_income, income_required, difference in post_retirement_investments:
+        additional_cash_required = f'{abs(difference):,.0f}' if difference < 0 else ''
+        result_html += f"""
+                <tr>
+                    <td>{age}</td>
+                    <td>{investment:,.0f}</td>
+                    <td>{annual_income_required:,.0f}</td>
+                    <td>{income_required:,.0f}</td>
+                    <td>{annual_dividend_income:,.0f}</td>
+                    <td>{monthly_dividend_income:,.0f}</td>
+                    <td>{additional_cash_required}</td>
+                </tr>
+        """
+
+    result_html += """
+            </tbody>
+        </table>
+    </div>
+    <p style="padding: 10px; border: 1px solid; border-radius: 5px; text-align: center; max-width: 400px; margin: 20px auto;">
+        <strong>Note:</strong> No additional investments or reinvestment of dividends after retirement.
+    </p>
+    """
+
+    return result_html
+
+            

modules/share_price_trend.py

@@ -0,0 +1,128 @@
+import io
+import matplotlib.pyplot as plt
+import FinanceDataReader as fdr
+import pandas as pd
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+def get_stock_prices(stock_code, days):
+    try:
+        df = fdr.DataReader(stock_code)
+        end_date = pd.to_datetime('today')
+        start_date = pd.date_range(end=end_date, periods=days, freq='B')[0]
+        df = df[(df.index >= start_date) & (df.index <= end_date)]
+        if df.empty:
+            print(f"<p style='color: red;'>No data available for {stock_code}</p>")
+            return None
+        return df['Close']
+    except Exception as e:
+        print(f"<p style='color: red;'>Failed to fetch data for {stock_code}: {e}</p>")
+        return None
+
+def share_price_trend(stock_codes, days):
+    stock_prices = {}
+    with ThreadPoolExecutor(max_workers=10) as executor:
+        futures = {executor.submit(get_stock_prices, stock_code.strip(), int(days)): stock_code.strip() for stock_code in stock_codes.split(',')}
+        for future in as_completed(futures):
+            stock_code = futures[future]
+            try:
+                prices = future.result()
+                if prices is not None:
+                    stock_prices[stock_code] = prices
+            except Exception as e:
+                print(f"<p style='color: red;'>Failed to fetch data for {stock_code}: {e}</p>")
+
+    if not stock_prices:
+        return "<p style='color: red;'>No data available for the provided stock codes.</p>"
+
+    plt.switch_backend('agg')
+    plt.style.use('tableau-colorblind10')
+
+    fig, ax = plt.subplots(figsize=(8, 4.5))
+    for stock_code, prices in stock_prices.items():
+        relative_prices = prices / prices.iloc[0]
+        ax.plot(prices.index, relative_prices, label=stock_code.upper())
+
+    # Remove the axes and ticks
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    ax.spines['bottom'].set_visible(False)
+    
+    ax.xaxis.set_visible(False)
+    ax.yaxis.set_visible(False)
+    
+    # Add grid for better readability
+    ax.grid(True, which='both', linestyle='--', linewidth=0.5)
+    
+    ax.legend()
+    plt.tight_layout()
+
+    svg_graph = io.StringIO()
+    plt.savefig(svg_graph, format='svg')
+    svg_graph.seek(0)
+    svg_data = svg_graph.getvalue()
+    plt.close()
+
+    svg_data = svg_data.replace('<svg ', '<svg width="100%" height="100%" ')
+    svg_data = svg_data.replace('</svg>', '''
+    <defs>
+        <linearGradient id="grad1" x1="0%" y1="0%" x2="100%" y2="0%">
+            <stop offset="0%" style="stop-color:rgb(173,216,230);stop-opacity:1" />
+            <stop offset="100%" style="stop-color:rgb(0,191,255);stop-opacity:1" />
+        </linearGradient>
+        <filter id="dropshadow" height="130%">
+            <feGaussianBlur in="SourceAlpha" stdDeviation="3"/>
+            <feOffset dx="2" dy="2" result="offsetblur"/>
+            <feMerge> 
+                <feMergeNode/>
+                <feMergeNode in="SourceGraphic"/> 
+            </feMerge>
+        </filter>
+    </defs>
+    <style>
+        @keyframes lineAnimation {
+            from {
+                stroke-dasharray: 0, 1000;
+            }
+            to {
+                stroke-dasharray: 1000, 0;
+            }
+        }
+        path {
+            animation: lineAnimation 1s linear forwards;
+        }
+    </style>
+    </svg>''')
+
+    svg_data = svg_data.replace('stroke="#1f77b4"', 'stroke="url(#grad1)" filter="url(#dropshadow)"')
+
+    html_table = "<h3>Stock Prices Data</h3><div class='table-container'><table>"
+    html_table += "<thead><tr><th>Date</th>"
+    for stock_code in stock_prices.keys():
+        html_table += f"<th>{stock_code.upper()}</th>"
+    html_table += "</tr></thead><tbody>"
+
+    # Create a date range with only business days
+    all_dates = pd.date_range(start=min(df.index.min() for df in stock_prices.values()), end=pd.to_datetime('today'), freq='B')
+    all_dates = all_dates[::-1]  # Reverse the order of dates
+
+    for date in all_dates:
+        html_table += f"<tr><td>{date.strftime('%Y-%m-%d')}</td>"
+        for stock_code in stock_prices.keys():
+            price = stock_prices[stock_code].get(date, None)
+            if price is not None:
+                html_table += f"<td>{price:,.2f}</td>"
+            else:
+                html_table += "<td>N/A</td>"
+        html_table += "</tr>"
+
+    html_table += "</tbody></table></div>"
+
+    graph_html = f'<h3>Relative Stock Prices Over the Last {days} Days</h3>{svg_data}'
+    return graph_html + html_table
+
+# Example usage
+# stock_codes = "AAPL,MSFT,GOOGL"
+# days = 30
+# result = share_price_trend(stock_codes, days)
+# print(result)

modules/utils.py

@@ -0,0 +1,164 @@
+import ssl
+import logging
+import gradio as gr
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
+from datetime import datetime
+
+# 로그 설정
+#logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# SSL 인증서 검증 비활성화
+ssl._create_default_https_context = ssl._create_unverified_context
+
+current_time = datetime.now().strftime("%b-%d-%Y")
+
+def load_css():
+    with open('style.css', 'r', encoding='utf-8') as file:
+        css = file.read()
+    return f"<style>{css}</style>"
+
+
+def format_quantity(quantity):
+    if quantity < 0:
+        return f"({-quantity:,})"
+    else:
+        return f"{quantity:,}"
+
+currency_symbols = {
+    "KRW": "₩",
+    "USD": "$",
+    "CAD": "$",
+    "EUR": "€",
+    "JPY": "¥",
+    "GBP": "£"
+}
+
+def get_currency_symbol(currency_code):
+    return currency_symbols.get(currency_code.upper(), "")
+
+def get_currency_codes():
+    return list(currency_symbols.keys())
+
+currency_codes = get_currency_codes()
+
+# Helper function to add buttons
+def clear_buttons(inputs):
+    clear_button = gr.ClearButton(value="Clear")
+    clear_button.click(
+        fn=lambda: [None] * len(inputs),
+        inputs=[],
+        outputs=inputs
+    )
+    return clear_button
+
+def submit_buttons(inputs, update_fn, output):    
+    submit_button = gr.Button(value="Run", variant="primary")
+    submit_button.click(
+        fn=update_fn,
+        inputs=inputs,
+        outputs=output
+    )
+    return submit_button
+
+def on_change(inputs, update_ouutput, outputs):
+    for input_component in inputs:
+        input_component.change(
+            fn=update_ouutput,
+            inputs=inputs,
+            outputs=outputs
+        )
+
+def render_components(component_rows):
+    for row in component_rows:
+        if isinstance(row, list):
+            with gr.Row():
+                for component in row:
+                    component.render()
+        else:
+            row.render()
+
+def create_tab(tab_name, inputs, outputs, update_fn, examples, component_rows):
+    with gr.TabItem(tab_name):
+        with gr.Row():
+            with gr.Column(elem_classes="input"):
+                render_components(component_rows)
+                clear_buttons(inputs)
+                submit_buttons(inputs, update_fn, outputs)
+                # gr.Examples(examples=examples, cache_examples=False, inputs=inputs)
+            with gr.Column():
+                outputs.render()
+        on_change(inputs, update_fn, outputs)
+
+import matplotlib.pyplot as plt
+import squarify
+from io import BytesIO
+import base64
+from matplotlib import font_manager
+
+# Global dictionary to store color mapping
+color_map_storage = {}
+
+def get_color_for_label(label, color_map, num_labels):
+    """Retrieve or generate color for the given label."""
+    if label not in color_map_storage:
+        cmap = plt.get_cmap(color_map)
+        # Generate a color based on label index
+        index = len(color_map_storage) % num_labels
+        color_map_storage[label] = cmap(index / (num_labels - 1))
+    return color_map_storage[label]
+
+def plot_treemap(data, color_map='Set3', font_path='Quicksand-Regular.ttf', legend_fontsize=30):
+    # 데이터 필터링: 비중이 0이 아닌 항목만 추출
+    filtered_data = {k: v for k, v in data.items() if v > 0}
+    
+    if not filtered_data:
+        return '<p>No data to display.</p>'  # 데이터가 없는 경우 처리
+
+    labels = [label.upper() for label in filtered_data.keys()]  # 라벨을 대문자로 변환
+    sizes = list(filtered_data.values())
+    
+    # 폰트 설정
+    font_properties = font_manager.FontProperties(fname=font_path, size=legend_fontsize)
+
+    # 색상 맵을 설정
+    num_labels = len(labels)
+
+    if len(labels) == 1:
+        # 항목이 하나일 때, 전체 사각형을 그립니다.
+        rectangles = [{'x': 0, 'y': 0, 'dx': 100, 'dy': 100}]
+        colors = [get_color_for_label(labels[0], color_map, num_labels)]  # 색상 하나로 채우기
+    else:
+        norm_sizes = squarify.normalize_sizes(sizes, 100, 100)
+        rectangles = squarify.squarify(norm_sizes, x=0, y=0, dx=100, dy=100)
+        colors = [get_color_for_label(label, color_map, num_labels) for label in labels]
+
+    # 트리맵 시각화
+    fig, ax = plt.subplots(figsize=(12, 8), dpi=300)  # figsize와 dpi를 설정하여 해상도 높이기
+    for rect, color in zip(rectangles, colors):
+        x, y, dx, dy = rect['x'], rect['y'], rect['dx'], rect['dy']
+        ax.add_patch(plt.Rectangle((x, y), dx, dy, color=color, alpha=0.7))
+
+    # 범례 생성
+    handles = [plt.Line2D([0], [0], marker='s', color='w', label=f'{label} {size:.1f}%',
+                         markersize=45, markerfacecolor=get_color_for_label(label, color_map, num_labels))
+               for label, size in zip(labels, sizes)]
+    
+    # 범례 추가, 제목 제거, 글자 크기를 키우고 범례 박스를 조정
+    ax.legend(handles=handles, loc='upper left', bbox_to_anchor=(1, 1), 
+              prop=font_properties, frameon=False)
+    
+    ax.set_xlim(0, 100)
+    ax.set_ylim(0, 100)
+    ax.axis('off')  # 축을 숨깁니다.
+    
+    # SVG로 저장
+    buf = BytesIO()
+    plt.savefig(buf, format='svg', bbox_inches='tight')  # bbox_inches='tight'를 추가하여 범례가 잘리는 문제를 방지
+    plt.close(fig)
+    buf.seek(0)
+    
+    # SVG 데이터를 base64로 인코딩
+    svg_str = buf.getvalue().decode('utf-8')
+    return f'<img src="data:image/svg+xml;base64,{base64.b64encode(svg_str.encode("utf-8")).decode("utf-8")}" />'