README.md

# Dump your knowledge, let AI refine it

## Installation

Create a Python environment with Python 3.10+. Install the requirements and the package:

``` sh
python -m pip install -r requirements.txt
python -m pip install .
```

For development, instead do:

``` sh
python -m pip install -r requirements.txt
python -m pip install -r requirements-dev.txt
python -m pip install -e .
```

## Starting

### Preparing environemnt

Set an environemnt variable called "HF_HUB_TOKEN" with your Hugging Face token
or create a `.env` file with that env var.

### Running the app

Run the `start.sh` script. This may require a `chmod +x start.sh` if not already executable.

```sh
./start.sh
```

Instead, you can also run each part individually. For this, in one terminal, start the background worker:

```sh
python src/gistillery/worker.py
```

In another terminal, start the web server:

```sh
uvicorn src.gistillery.webservice:app --reload --port 8080
```

For example requests, check `requests.org`.

A very simple web interface is available via gradio. To start it, run:

```sh
python demo.py
```

and navigate to the indicated URL (usually http://127.0.0.1:7860).

## Docker

To run everything with Docker, first build the image:

```sh
docker build -t gistillery:latest .
```

Next run the container:

```sh
docker run -p 7860:7860 -e GRADIO_SERVER_NAME=0.0.0.0 -v $HOME/.cache/huggingface/hub:/data gistillery:latest
```

Note that the Hugging Face cache folder is mounted as a docker volume to make use of potentially available local model cache instead of downloading the transformers models each time the container is started. To prevent that, remove the `-v ...` parameter. The database used for storing the results is ephemeral and will be deleted when the docker container is stopped.

## Checks

### Running tests

```sh
python -m pytest tests/
```

### Other

```sh
mypy src/
black src/ && black tests/
ruff src/ && ruff tests/
```

## TODOs

### Tools

i. Reading pdf in general
i. Reading arxiv
i. Generating text from youtube videos using whisper

### Deployment

i. Make DB location configurable, mountable when running in docker (otherwise, it will be deleted each time the container is stopped).