ZenZoneBot

ZenZoneBot is a Flask-based AI chatbot designed to assist users in understanding their emotional state, detecting potential mental health concerns, and providing helpful resources. The chatbot uses sentiment analysis, disorder keyword detection, and intent prediction to generate meaningful responses.

Features

• Sentiment analysis to detect user emotions.
• Disorder keyword detection to identify potential mental health concerns.
• Intent prediction to understand user needs (e.g., seeking advice, resources, or emotional support).
• Context-aware conversation flow.
• Provides helpful self-care tips and resources for mental health support.

Installation

Prerequisites

Make sure you have the following installed on your system:

• Python 3.7+
• Flask
• Joblib
• Required Python libraries

Setup

1. Clone the repository:

   git clone https://github.com/your-repo/ZenZoneBot.git
   cd ZenZoneBot

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask application:

   python app.py

Where to Run

After starting the Flask application, open your browser and go to:

http://127.0.0.1:5000/

This will open the chatbot interface where you can interact with ZenZoneBot.

Project Structure

ZenZoneBot/
│── templates/
│   ├── index.html
│   ├── about_us.html
│   ├── chatui.html
│── keyword1.py  # Disorder keyword matching
│── text_sum1.py # Text summarization module
│── int.py       # Intent prediction module
│── sent.py      # Sentiment analysis module
│── app.py       # Flask application
│── requirements.txt

API Endpoints

Home Page

• Endpoint: /
• Method: GET
• Description: Renders the home page.

About Us Page

• Endpoint: /about_us
• Method: GET
• Description: Renders the About Us page.

Chat UI

• Endpoint: /chatui
• Method: GET
• Description: Renders the chatbot UI.

Process User Input

• Endpoint: /process_input
• Method: POST
• Description: Processes user input, detects sentiment, identifies disorder-related keywords, predicts intent, and provides appropriate responses.
• Request Body:

  {
    "user_prompt": "I feel really anxious about my exams."
  }

• Response:

  {
    "message": "We all face worries. What's been on your mind recently?"
  }

Conversation Flow

1. Sentiment Detection: Identifies user emotions such as joy, fear, anger, sadness, etc.
2. Disorder Detection: Detects keywords related to mental health conditions like depression, anxiety, PTSD, ADHD, and eating disorders.
3. Intention Detection: Determines whether the user is expressing distress, asking for resources, or seeking advice.
4. Confirmation Response: Provides final reassurance and additional resources if needed.

License

This project is licensed under the MIT License.

Contact

For questions or suggestions, feel free to reach out at mridulchawla20@example.com.