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Welcome to the Medicare-ChatBot repository! This project aims to provide a comprehensive chatbot solution for healthcare services using IBM-Watson. The chatbot is designed to assist users with medical inquiries, appointment scheduling, and general health information.

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durjaysamrat/Medicare-ChatBot

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💬 Medicare ChatBot

Welcome to the Medicare ChatBot repository! 🤖💊
This project introduces an intelligent chatbot designed to assist users with medical queries and provide healthcare-related information. Using Natural Language Processing (NLP) and Machine Learning (ML), the bot simulates human-like conversations, offering reliable guidance and support for non-critical medical inquiries.


🌟 Overview

The Medicare ChatBot is built to help users access basic healthcare information without the need to visit a doctor for minor queries. It leverages cutting-edge NLP techniques to interpret user questions and provide accurate, context-aware responses.

Key Features:

  • Supports symptom-based queries to suggest possible conditions.
  • Offers general healthcare tips and remedies.
  • Provides 24/7 availability for non-critical assistance.
  • User-friendly interface for seamless interaction.

💻 Tech Stack

  • Backend: Flask (Python)
  • Natural Language Processing:
    • NLTK
    • SpaCy
    • Transformer Models
  • Frontend: HTML, CSS, JavaScript
  • Database: SQLite
  • Deployment: Render, Flask Server

🚀 Getting Started

Prerequisites

  1. Python (3.7 or later)
  2. Required Python Libraries (Install via requirements.txt):
    • Flask
    • NLTK
    • SpaCy
    • Pandas

Installation

  1. Clone the repository:

    git clone https://github.com/durjaysamrat/Medicare-ChatBot.git  
  2. Navigate to the project folder:

    cd Medicare-ChatBot  
  3. Install dependencies:

    pip install -r requirements.txt  
  4. Run the Flask server:

    python app.py  
  5. Open the chatbot in your browser:

    http://localhost:5000  
    

🧠 How It Works

  1. User Input: The user asks a question or describes symptoms.
  2. NLP Engine: The input is processed using tokenization, stemming, and entity recognition.
  3. Response Generation:
    • The bot queries its database for relevant information.
    • Predefined responses are combined with AI-generated text for context-specific replies.
  4. Output: The chatbot provides the response in a human-friendly format.

🎯 Use Cases

  • Symptom Checker: Offers preliminary analysis based on user-provided symptoms.
  • Healthcare Tips: Provides general tips for well-being and prevention.
  • Medical FAQs: Answers frequently asked questions about common conditions.

📂 Project Structure

Medicare-ChatBot/
│
├── templates/            # HTML files for the user interface
├── static/               # CSS, JavaScript, and assets
├── app.py                # Flask application
├── chatbot_model.py      # Core logic for NLP and response generation
├── database.db           # SQLite database for medical data
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

🖼️ Demo

🌟 ChatBot Interface:

Whatsapp UI


🤝 Contributions

Contributions are welcome! 🎉

  • Report bugs or suggest new features via issues.
  • Fork the repository, make changes, and submit a pull request.

📫 Connect

LinkedIn
GitHub
Email


If you find this project helpful, give it a star!
Let’s make healthcare more accessible with intelligent solutions. 💡💊

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Welcome to the Medicare-ChatBot repository! This project aims to provide a comprehensive chatbot solution for healthcare services using IBM-Watson. The chatbot is designed to assist users with medical inquiries, appointment scheduling, and general health information.

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