Wellness-Chatbot

A simple, rule-based chatbot designed to provide support and information on mental wellness topics. Built with Python and Flask, this project utilizes a knowledge base to provide answers to user questions conversationally.

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✨ Features

• Conversational Interface: Ask questions in plain English and get helpful responses.
• Knowledge-Based: The chatbot's intelligence comes from a intents.json file, making it easy to extend and modify its expertise.
• Simple Web Interface: A basic frontend allows users to interact with the chatbot in a web browser.
• Lightweight Backend: Uses the Flask web framework to serve the chatbot's logic.

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📂 Project Structure

The repository is organized as follows:

wellness-chatbot/
│
├── backend/
│   └── (Backend logic files)
│
├── chatbot/
│   └── (Chatbot-specific scripts and files)
│
├── frontend
│   
│
├── .gitignore
├── LICENSE
├── app.py                  # Main Flask application file
└── intents.json     # The "brain" of the chatbot

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Follow these instructions to get a copy of the project up and running on your local machine.

Prerequisites

You need to have Python 3 installed on your system. You can download it from python.org.

Installation

1. Clone the repository:

   git clone [https://github.com/v4rshh/wellness-chatbot.git](https://github.com/v4rshh/wellness-chatbot.git)

2. Navigate to the project directory:

   cd wellness-chatbot

3. Install the required dependencies: (Note: It is recommended to create a virtual environment first)

   # Assuming dependencies are listed in a requirements.txt file.
   # If not, you may need to install Flask and other libraries manually.
    pip install -r requirements.txt

Running the Application

1. Start the Flask server:

   python app.py

2. Open your web browser and go to http://127.0.0.1:5000 (or the address shown in your terminal). You should now be able to interact with the chatbot.

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🛠️ How It Works

This chatbot uses a simple pattern-matching approach.

1. The user sends a message through the web interface.
2. The Flask backend receives the message.
3. The app.py script loads the intents.json file.
4. It searches the knowledge base for a question that matches the user's input.
5. If a match is found, it returns the corresponding answer. If not, it provides a default "I don't know"(fallback) response.