MindfulMe

MindfulMe is a privacy-preserving mental health assessment system that performs sentiment analysis, suicide risk detection, and standard mental health questionnaire scoring entirely on the user's local machine.

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📌 Key Features

✅ Hybrid Text Analysis (Module 1 - NLP + Risk Detection)

• General sentiment classification using a Hugging Face transformer model
• Suicide risk detection using a custom ML model trained on TF-IDF + Logistic Regression
• Returns:
  • sentiment → positive / neutral / negative
  • tone → high-risk / low-risk
  • confidence_score

✅ Clinical Questionnaire Scoring (Module 2)
Automated scoring for:

• PHQ-9 (Depression)
• GAD-7 (Anxiety)
  Returns total score + severity category.

✅ Frontend UI (Static HTML + TailwindCSS)
A lightweight multi-page UI (without frameworks) that interacts with Flask APIs using fetch.

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🏗️ Project Structure

MindfulMe/
│
├── data/
│   ├── raw/
│   │   └── suicide_detection_data.csv
│   ├── processed/
│   │   └── processed_suicide_data.csv
│   └── results/
│       └── analysis_results.csv
│
├── risk_engine/
│   ├── module1_api.py
│   └── module2_api.py
│
├── scripts/
│   ├── data_preprocessor.py
│   ├── final_analyzer.py
│   ├── risk_classifier_model.pkl
│   └── tfidf_vectorizer.pkl
│
├── ui/
│   ├── login_page.html
│   ├── expression_page.html
│   ├── decision_page.html
│   ├── questionnaire.html
│   └── stats_page.html
│
├── AI Powered Chatbots for Mental Health Assessment and Support.pdf
├── README.md
└── requirements.txt

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⚙️ Tech Stack

Python

Flask + Flask-CORS

Hugging Face Transformers

Scikit-learn

Pandas / NumPy

NLTK

HTML + TailwindCSS + JavaScript

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🚀 How It Works (Workflow)

🔹 Module 1: Text Sentiment + Suicide Risk Detection (Port 5001)

User enters a message in the UI

UI calls: POST http://localhost:5001/analyze

API returns:

sentiment label (transformer)

high-risk/low-risk tone (custom model)

confidence score

🔹 Module 2: Questionnaire Scoring (Port 5002)

User answers PHQ-9 / GAD-7

UI calls: POST http://localhost:5002/score/questionnaire

API returns:

total score

severity level

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🔌 API Usage

✅ Module 1: Analyze Text

Endpoint

POST /analyze

Example Request

{ "text": "I feel very hopeless and tired of everything." }

Example Response

{ "text": "I feel very hopeless and tired of everything.", "sentiment": "negative", "tone": "high-risk", "confidence_score": 0.92 }

✅ Module 2: Score Questionnaire (PHQ-9 or GAD-7)

Endpoint

POST /score/questionnaire

PHQ-9 Example { "type": "phq9", "answers": [1,2,0,1,2,1,0,1,1] }

GAD-7 Example { "type": "gad7", "answers": [0,1,1,2,0,1,2] }

Example Response

{ "questionnaire": "PHQ-9", "risk_score": 9, "level": "mild depression" }

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🧪 Training / Reproducing the Model

✅ Step 1: Preprocess the raw dataset cd scripts python data_preprocessor.py

Input:

data/raw/suicide_detection_data.csv

Output:

data/processed/processed_suicide_data.csv

✅ Step 2: Train the custom risk classifier python final_analyzer.py

Outputs:

scripts/tfidf_vectorizer.pkl

scripts/risk_classifier_model.pkl

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🛡️ Safety & Ethical Disclaimer

MindfulMe is intended for educational and research purposes only.

✅ This project is NOT a medical device
✅ This project does NOT provide clinical diagnosis
✅ Users should consult licensed professionals for real support

If you or someone you know is in immediate danger or distress, please contact local emergency services or a verified crisis hotline.

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Contributors

Name
Akilan V S
Subash Venkat
Muhibullah S M