https://github.com/shaneperry0102/health-chatbot
This is a healthcare assistant powered by LangChain and Streamlit.
https://github.com/shaneperry0102/health-chatbot
chatbot langchain langgraph langgraph-python streamlit
Last synced: 3 months ago
JSON representation
This is a healthcare assistant powered by LangChain and Streamlit.
- Host: GitHub
- URL: https://github.com/shaneperry0102/health-chatbot
- Owner: shaneperry0102
- License: apache-2.0
- Created: 2025-01-05T02:06:43.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-20T00:00:27.000Z (4 months ago)
- Last Synced: 2025-01-20T00:28:59.574Z (4 months ago)
- Topics: chatbot, langchain, langgraph, langgraph-python, streamlit
- Language: Python
- Homepage: https://healthcare-assistant.streamlit.app/
- Size: 17.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Healthcare AI Assistant Application
This application is an AI-powered healthcare assistant that integrates web search capabilities and Youtube video recommendation, offering a helpful environment for patients, doctors and concerned users.
## Key Features and Functionalities
1. **Intelligent Chat Interface**:
- Powered by Llama3 70B 8192, an advanced AI model from Groq.
- Enables natural language interactions for queries.
2. **LangGraph-based Workflow**:
- Orchestrates AI decision-making processes.
- Provides a real-time Mermaid diagram of the workflow in the sidebar.
3. **Intuitive Streamlit Interface**:
- Offers a clean, user-friendly interface for seamless interaction.
4. **Web Resource Access**:
- Capable of making API requests and accessing online information.
## Setup and Usage
### Python Dependency Installation
Before running the application, ensure you have configured the necessary API keys in the `secrets.toml` file located at the `.streamlit` of the project directory. Follow these steps for Python dependency installation:
1. Create a virtual environment by running:
```sh
python -m venv .venv
```
This command creates a new directory named `.venv` in your project directory, which will contain the Python executable and libraries.
2. Activate the virtual environment:
- On Windows, run:
```cmd
.\.venv\Scripts\activate
```
- On macOS and Linux, run:
```sh
source .venv/bin/activate
```
After activation, your terminal prompt will change to indicate that the virtual environment is active.
3. With the virtual environment activated, install the required Python packages by running:
```sh
pip install -r requirements.txt
```
This command reads the `requirements.txt` file and installs all the listed packages along with their dependencies.
Remember to activate the virtual environment (`venv`) every time you work on this project. To deactivate the virtual environment and return to your global Python environment, simply run `deactivate`.
### Starting the Application
Finally, to start the application:
1. Launch the Streamlit application:
```sh
streamlit run app.py
```
2. Access the application via your web browser to start interacting with the AI assistant.