Maṉa

This project focuses on leveraging machine learning and natural language processing (NLP) techniques to analyze mental health-related text data. It includes a fine-tuned version of the Roberta transformer model, named MHRoberta, specifically designed for mental health analysis tasks. The model is trained using the PEFT (Parameter-Efficient Fine-Tuning) method on a mental health dataset.

MHRoberta (a Large Language Model for mental health analysis)

We developed our own model called 'MHRoberta' is Mental Health Roberta model. It is pretrained Roberta transformer based model fine-tunned on Mental Health dataset by adopting PEFT method.

Key Features

• MHRoberta Model: A transformer-based model fine-tuned for mental health analysis tasks.
• Chatbot Integration: A FastAPI-powered chatbot that interacts with users, detects their mental state, and provides empathetic responses based on the detected state.
• Local and Cloud Inference: Supports both local fallback models and cloud-based inference using Hugging Face's Inference API.
• Mental State Detection: Automatically detects mental states from user input and tailors responses accordingly.

create an virtual python environment

conda create -n env.0.0.0 python=3.13.2 -y

conda activate env.0.0.0

install requirements file

pip install -r requirement.txt

how to run this project

export this huggingface tokens in terminal

export HUGGINGFACE_TOKEN=your_huggingface_token
export HF_INFERENCE_API_KEY=your_inference_api_key

export the project root in terminal

export PYTHONPATH=/workspaces/MHRoberta-a-LLM-for-mental-health-analysis

run the backend file in terminal

python webapp_setup/chatbot.py

sequenceDiagram
    actor User as 🧑‍💻 User
    participant Maṉa_UI as 🌐 Maṉa Web App
    participant Social_API as 🔗 Social Media API
    participant Sentiment_Analysis as 📊 Sentiment Analysis (RoBERTa)
    participant MaṉaNow as 🤖 MaṉaNow (DeepSeek-R1)
    participant MaṉaChat as 💬 MaṉaChat (Llama-3.2-3B)
    participant Report_System as 📄 Report Generation

    User ->> Maṉa_UI: Clicks "Get Started" 🚀
    Maṉa_UI ->> Social_API: Fetches user comments 📝
    Social_API -->> Maṉa_UI: Returns comments 📥
    Maṉa_UI ->> Sentiment_Analysis: Analyzes sentiment 🔍
    
    alt Positive Count >= Negative Count 👍
        Maṉa_UI ->> User: Display Sentiment Report ✅
        User ->> MaṉaChat: Starts conversation 💬
        MaṉaChat -->> User: Provides mental health tips 🧘‍♂️
    else Negative Count > Positive Count 🚨
        Maṉa_UI ->> MaṉaNow: Trigger mental health assessment 🔴
        MaṉaNow ->> User: Asks mental health-related questions ❓
        User -->> MaṉaNow: Answers questions 📝
        MaṉaNow ->> Report_System: Generates report 📄
        Report_System -->> User: Displays final mental health report 🏥
    end
    
    User ->> Maṉa_UI: Can download report or seek advice 📥
    User ->> MaṉaChat: Asks for guidance 💡
    MaṉaChat -->> User: Provides personalized mental health support 🤗

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graph TD;
    A[User Visits Homepage] -->|Selects MaṉaChat| B(MaṉaChat);
    A -->|Selects MaṉaNow| C(MaṉaNow);
    
    %% MaṉaChat Flow
    B --> D{User Inputs Query};
    D --> E[Retrieve AI-based Response];
    E --> F[Display Mental Health Tips];
    
    %% MaṉaNow Flow
    C --> G{User Uploads Comments or Fetch via API};
    G --> H[Perform Sentiment Analysis];
    H --> I{Negative Sentiment > Positive Sentiment?};
    
    I -- Yes --> J[Trigger MaṉaNow Questions];
    J --> K[User Responds];
    K --> L[Generate Mental Health Report];
    L --> M[Store Report in Database];
    M --> N[Display Report to User];
    
    I -- No --> O[End Session];

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