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https://github.com/manas1331/pesu_io_project
This is a general health chatbot which is built using Gemini model and it answers all health related queries asked by the user. Built using the concept of RAG, embeddings and LLM.
https://github.com/manas1331/pesu_io_project
chatbot gemini health healthcare llm rag streamlit
Last synced: 10 days ago
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This is a general health chatbot which is built using Gemini model and it answers all health related queries asked by the user. Built using the concept of RAG, embeddings and LLM.
- Host: GitHub
- URL: https://github.com/manas1331/pesu_io_project
- Owner: manas1331
- License: mit
- Created: 2024-11-09T16:59:06.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-09T17:16:55.000Z (2 months ago)
- Last Synced: 2025-01-10T20:43:02.167Z (10 days ago)
- Topics: chatbot, gemini, health, healthcare, llm, rag, streamlit
- Language: Python
- Homepage:
- Size: 2.15 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PESU IO Course Project: Health Assistant Bot
## Overview
This project is a Health Assistant Bot built using Google's Gemini model. It is part of the **PES University** (PESU) IO course. The bot provides assistance in health-related topics, offering actionable steps, health tips, and factual information based on user queries. It also features a **Chatbot** page and an **FAQ** page displaying frequently asked questions related to health and wellness.
### Features:
- **Chatbot**: The chatbot interacts with users, answering health-related queries using the Gemini model.
- **FAQ Page**: A page that displays frequently asked questions along with the bot’s responses.
- **Health Tips**: Provides general health advice like hydration, diet, exercise, and mental well-being.
- **Location-Based Services**: The bot can extract coordinates from user queries and help with location-based searches (e.g., nearby hospitals).
## Installation
To set up the project on your local machine, follow these steps:
### Prerequisites:
- Python 3.x
- Streamlit
- `google-generativeai` library (for interacting with the Gemini model)
- `llama-index` (for prompt management)
### Step-by-Step Setup:
1. Clone the repository:
```bash
git clone https://github.com//health-assistant-bot.git
cd health-assistant-bot
```
2. Install the required Python libraries:
```bash
pip install -r requirements.txt
```
3. Set up your **Google Gemini API Key**:
- Create a project in the Google Cloud Console.
- Enable the Gemini API.
- Create an API key and store it in a safe place.
- Replace `"YOUR_API_KEY"` in the code with your actual API key.
4. Run the Streamlit app:
```bash
streamlit run home.py
```
5. Visit the app in your browser at `http://localhost:8501`.
## Usage
Once the application is running, you can interact with the bot through the following pages:
1. **Chatbot**:
- Ask health-related questions (e.g., "How can I improve my diet?" or "What are some exercises for back pain?").
- The bot will provide an AI-generated response using the Gemini model.
2. **FAQ**:
- Common health queries will be displayed on the FAQ page, where users can find answers to frequently asked questions such as:
- "How can I stay hydrated?"
- "What foods are good for immunity?"
- "How often should I exercise?"
3. **Location-Based Health Services**:
- You can provide a query with coordinates (e.g., "(12.9716, 77.5946)") to find nearby hospitals or health facilities.
## Code Structure
- `app.py`: Main Streamlit app for running the chatbot and FAQ pages.
- `chatbot.py`: Handles the chatbot interactions and responses.
- `faq.py`: Displays the FAQ section with predefined health-related questions and answers.
- `home.py`: Helper functions for generating responses and handling user input.
## Technologies Used
- **Streamlit**: For building the interactive web application.
- **Google Gemini**: For natural language processing and generating health-related responses.
- **Python**: For backend functionality.
- **Regular Expressions**: For extracting coordinates from user queries.
## Contributing
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes.
4. Push to your forked repository.
5. Open a pull request.
## License
This project is licensed under the MIT License.
## Acknowledgments
- Thanks to **Google Gemini** for providing the powerful generative model used in this project.
- Special thanks to **PES University** for providing the opportunity to work on this project.