smithla02/fact-finder

Streamlit App to Discover Facts about Anything and Everything

Fact Finder Streamlit App

Welcome to the Fact Finder Streamlit App repository. This app fetches and displays facts based on user-selected topics and personas, leveraging OpenAI's GPT models for content generation and SQLAlchemy for data persistence, and Streamlit as a front end.

Using the deployed app

The app is deployed using Streamlit cloud, and can be accessed here: https://fact-finder.streamlit.app/

Features

• Fetch facts from OpenAI based on selected topics and personas.
• Cache facts in a SQLite database for quick retrieval.
• User interface built with Streamlit for an interactive experience.

Project Structure

The project has the following structure:

• app/: Directory containing the main application logic.
  • streamlit_app.py: The main Streamlit application file.
  • database.py: Contains SQLAlchemy database operations for storing and retrieving facts.
  • config.py: Configuration settings for the application, including environment variables.
  • openai_retrieval.py: Handles fetching data from OpenAI.
  • prompts.py: Constructs prompts for querying OpenAI.
• Dockerfile: Contains information about building a Docker image for the repository.
• .pre-commit-config.yaml: Contains linting and formatting commit hooks for maintaining consistency in this repository
• data/: Contains information about topics and topic associations.

Prerequisites

To run in Docker:

• Docker

To run locally:

• Python 3.8+
• pip
• Virtual environment (recommended)
• Docker (for containerization)

Exporting 'OPENAI_API_KEY' as an environment variable

To run this app in Docker or locally, you must have your own 'OPENAI_API_KEY'.

• Instructions for setting up an OPENAI_API_KEY can be found here.

• To export your OPENAI_API_KEY to your .zshrc or .bashrc file to have them available for any terminal session, you can use the following commands.

Exporting to '.zshrc' or '.bashrc' File:

1. Exporting the Environment Variable:

   • For .zshrc (Zsh shell):

     echo 'export OPENAI_API_KEY=your_openai_api_key_here' >> ~/.zshrc

   • For .bashrc (Bash shell):

     echo 'export OPENAI_API_KEY=your_openai_api_key_here' >> ~/.bashrc

2. Reloading the Shell Configuration:

   • For Zsh:

     source ~/.zshrc

   • For Bash:

     source ~/.bashrc

Exporting in a Single Terminal Session:

If you want to export the OPENAI_API_KEY in a single terminal session without modifying the shell configuration files, you can do it directly in the terminal:

export OPENAI_API_KEY=your_openai_api_key_here

This command sets the OPENAI_API_KEY environment variable for the current terminal session. Remember that this approach will only persist for the duration of that terminal session.

Running the App with Docker

To run the Fact Finder Streamlit App using Docker, you can pull the pre-built image from Docker Hub and run it in a container.

Pulling the Docker Image

Pull the latest version of the Fact Finder Streamlit App image from Docker Hub:

docker pull smithla02/fact-finder

Running the Docker Container

To run the Docker container and open in your browser, pass the OPENAI_API_KEY as an environment variable and map port 8501 for access:

docker run -e OPENAI_API_KEY -p 8501:8501 smithla02/fact-finder & sleep 2 && open http://localhost:8501

Accessing the App

After starting the container, open your web browser and navigate to http://localhost:8501 to view the app.

Stopping the Container

To stop the running container, find the container using docker ps | grep 'fact-finder', then stop it using docker stop <container_id>.

Running the App Locally

Setting Up the Environment

1. Clone the repository to your local machine.
2. Navigate to the project directory.
3. Create a virtual environment:
   • Linux/macOS: python3 -m venv venv
   • Windows: py -m venv venv
4. Activate the virtual environment:
   • Linux/macOS: source venv/bin/activate
   • Windows: .\venv\Scripts\activate
5. Install the required dependencies: pip install -r requirements.txt

Running the Streamlit App

With the environment set up and activated, run the Streamlit app using:

streamlit run streamlit_app.py

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any bugs or feature requests.