Tabib-HealthCare-Chatbot

🥈Silver

Purpose : Major Project Team Size : 4 Duration : 10 Months [ Oct. 1, 2021 - June 31, 2022 ] Key Skills : Rasa AI , Python , NLP , Flask , HTML , CSS , JavaScript It is a Web-based Chatbot to automate healthcare management with audio assistance. Users can get immediate medication for their symptoms and book appointments via an audio feature. In addition to text assistance, this chatbot has an audio assistance feature. This feature eliminates the restrictions that visually impaired patients face w

710Updated 4mo ago

Intermediate30min to implementautomation

Saves ~480 min per use

Quick InstallView Source

git clone https://github.com/aakif123/Tabib-HealthCare-Chatbot.git

Works with:

Claude

Overview

About This Skill

TaBiB is a web-based healthcare automation chatbot built with Rasa AI that provides both text and voice-enabled consultation. Users can describe symptoms, receive medication recommendations, and book appointments through an intuitive interface. The chatbot features audio assistance using Web Speech API and Talkify API, making healthcare services accessible to visually impaired patients who face limitations with text-only healthcare applications. Developed as a major project using Python, NLP, Flask, and JavaScript, TaBiB was presented at the 6th National Conference of Science and Engineering in 2022.

How to Use

Install Anaconda, create a Python 3.8 environment, and install dependencies (Flask, Rasa). Run the Rasa server from the rasabot directory on port 5002, then start the Flask web app from the web_app directory. Access the chatbot at localhost:5000 in your browser.

Use Cases

Accessible symptom assessment for visually impaired patients

Immediate medication recommendations via voice or text

Appointment scheduling through audio-enabled interface

Healthcare automation for reducing patient wait times

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/aakif123/Tabib-HealthCare-Chatbot

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I need to develop a healthcare chatbot using Rasa AI, Python, and NLP. The chatbot should have both text and audio assistance features. It should be able to provide immediate medication recommendations based on symptoms and allow users to book appointments. The target users are [COMPANY] in the [INDUSTRY] sector. Can you provide a detailed plan and code snippets for implementing this chatbot?

Example Output

# Healthcare Chatbot Development Plan

## Overview
The healthcare chatbot will be developed using Rasa AI, Python, and NLP. It will feature both text and audio assistance to cater to all users, including those with visual impairments. The chatbot will provide immediate medication recommendations and appointment booking functionality.

## Key Features
- **Text Assistance**: Users can input their symptoms via text and receive medication recommendations.
- **Audio Assistance**: Users can interact with the chatbot via voice commands, making it accessible to visually impaired individuals.
- **Appointment Booking**: Users can schedule appointments with healthcare providers directly through the chatbot.

## Implementation Plan
1. **Setup Rasa AI Environment**: Install Rasa AI and configure the development environment.
2. **Develop NLP Models**: Train NLP models to understand user inputs related to symptoms and medication.
3. **Integrate Audio Features**: Implement audio input and output capabilities using Python libraries.
4. **Create User Interface**: Design a user-friendly interface using HTML, CSS, and JavaScript.
5. **Test and Deploy**: Conduct thorough testing and deploy the chatbot on a web platform.

## Code Snippets
```python
# Example code snippet for symptom analysis
from rasa_nlu.training_data import load_data
from rasa_nlu.config import RasaNLUConfig
from rasa_nlu.model import Trainer

def train_nlp_model(data_path, config_path):
    training_data = load_data(data_path)
    trainer = Trainer(RasaNLUConfig(config_path))
    trainer.train(training_data)
    model_directory = trainer.persist()
    return model_directory

# Example code snippet for audio input
import speech_recognition as sr

def recognize_speech(audio_file):
    recognizer = sr.Recognizer()
    with sr.AudioFile(audio_file) as source:
        audio = recognizer.record(source)
    try:
        text = recognizer.recognize_google(audio)
        return text
    except sr.UnknownValueError:
        return "Could not understand audio"
    except sr.RequestError:
        return "API unavailable"
```