| --- |
| title: DASS QuickText Model |
| emoji: 📚 |
| colorFrom: indigo |
| colorTo: red |
| sdk: gradio |
| sdk_version: 6.8.0 |
| app_file: app.py |
| pinned: false |
| --- |
| # 🌿 心理健康風險程度測試 (DASS-12) |
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| 這是一個基於 **DASS (Depression, Anxiety and Stress Scale)** 簡化版開發的自動化心理健康評估工具。透過 12 題心理狀態問答，利用機器學習模型預測潛在的心理健康風險程度。 |
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|
| ## 🚀 功能特點 |
| - **即時評估**：透過互動式介面，5 分鐘內獲得評估結果。 |
| - **三維度分析**：分別計算「焦慮」、「憂鬱」與「壓力」的分數比重。 |
| - **雲端紀錄**：測試結果會自動去識別化並加密儲存至後端雲端資料庫（Google Sheets），供學術研究與趨勢分析。 |
| - **歷史紀錄**：支援瀏覽器當次 Session 的歷史紀錄查看。 |
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|
| ## 🛠 技術棧 |
| - **前端介面**: [Gradio](https://gradio.app/) |
| - **數據處理**: Pandas, Numpy |
| - **機器學習**: LightGBM (LGBMClassifier) |
| - **後端儲存**: Google Sheets API (via gspread) |
| - **部署環境**: Hugging Face Spaces |
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|
| ## 📁 檔案說明 |
| - `app.py`: Gradio 主程式與邏輯控制。 |
| - `DASS_model.bin`: 已訓練完成的機器學習模型。 |
| - `AutoPreprocess.py`: 自定義數據預處理類別。 |
| - `requirements.txt`: 專案執行環境所需的 Python 套件清單。 |
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|
| ## 🔐 隱私聲明 |
| 本專案極為重視使用者隱私： |
| 1. 使用者輸入之「暱稱」僅用於介面呈現，**不會**被傳送至雲端資料庫，亦**不會**用於模型計算。 |
| 2. 儲存之數據包含：測試時間、人口學基本資訊（性別/年齡/家庭人數）、各維度分數及 12 題原始答案。 |
| 3. 本工具非醫療器材，結果僅供參考。 |
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|
| ## 👥 開發團隊 |
| - 第四組：心理健康風險預測小組 🌿 |
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|
| - # 🌿 Psychological Health Risk Assessment (DASS-12) |
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| This is an automated psychological health evaluation tool based on the simplified **DASS (Depression, Anxiety and Stress Scale)**. It leverages a Machine Learning model to predict potential mental health risk levels based on 12 psychological state questions. |
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|
| ## 🚀 Key Features |
| - **Instant Assessment**: Interactive interface that provides evaluation results within 5 minutes. |
| - **Three-Dimensional Analysis**: Calculates and visualizes specific scores for Anxiety, Depression, and Stress. |
| - **Cloud-Based Storage**: Test results are anonymized and securely stored in a cloud database (Google Sheets) for academic research and trend analysis. |
| - **Session History**: Supports viewing history logs within the current browser session. |
|
|
| ## 🛠 Technical Stack |
| - **Frontend Interface**: [Gradio](https://gradio.app/) |
| - **Data Processing**: Pandas, Numpy |
| - **Machine Learning**: LightGBM (LGBMClassifier) |
| - **Backend Storage**: Google Sheets API (via gspread) |
| - **Deployment**: Hugging Face Spaces |
|
|
| ## 📁 File Structure |
| - `app.py`: The main Gradio application logic. |
| - `DASS_model.bin`: The pre-trained machine learning model. |
| - `AutoPreprocess.py`: Custom data preprocessing class. |
| - `requirements.txt`: List of Python dependencies required for execution. |
|
|
| ## 🔐 Privacy & Ethics |
| Privacy is our top priority: |
| 1. **Anonymity**: The "Nickname" input is used solely for the user interface and is **NEVER** transmitted to the database or used in model calculations. |
| 2. **Data Collection**: Collected data includes: timestamp, basic demographics (Gender/Age/Family Size), dimensional scores, and the 12 raw responses. |
| 3. **Disclaimer**: This tool is not a medical device. Results are for reference only and do not constitute a formal diagnosis. |
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|
| ## 👥 Development Team |
| - Group 4: Mental Health Risk Prediction Team 🌿 |
| - |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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