Anote-PrivateGPT-Desktop

How to run it locally

1. Clone the repo git clone https://github.com/nv78/Anote-PrivateGPT-Desktop and cd Anote-PrivateGPT-Desktop

Backend

First, compile the backend

1. cd backend

2. Create a virtual env in python -m venv venv source venv/bin/activate
   For Windows: use Command Prompt .\venv\Scripts\Activate

3. Install requirements pip install -r requirements.txt

4. Compile pyinstaller --onefile app.py --add-data "database.db:."

5. Now, you should have an output in ./backend/dist called app. You will copy this into ./appdist

Frontend

1. cd frontend
2. Install dependencies and Build react app npm install --force npm run build

Install Ollama

1. Go to https://ollama.com/download and download Ollama for Mac.

2. Once you have followed installation instructions, you will want to run ollama run llama2 and ollama run mistral

Running the whole app

1. In the home directory (/Anote-Private-GPT), you will install dependencies: npm install --force

2. Then you will run the app by doing npm run start

Dev stuff

To Run the code:

1. Open backend folder in terminal cd backend

2. Create a virtual env in python -m venv venv source venv/bin/activate
   For Windows: use Command Prompt .\venv\Scripts\Activate

3. Install pyinstaller pip install pyinstaller

4. Build the backend

To include the db: pyinstaller --onefile app.py --add-data "database.db:."

Note: might have to do pyinstaller --onefile app.py --hidden-import flask

Put the flask app, which is in the folder backend/dist in appdist

6. Open frontend folder in terminal cd .. cd frontend

7. Install dependencies and Build react app npm install --force npm run build

8. Go back to main folder cd ..

9. Install all dependencies and run electron npm install npm start

10. To package/bundle, run for mac: npm run make, and for Linux: sudo npx electron-forge make --platform=linux --arch=x64

Install private models (should include this under installation instructions under the app later):

1. Follow installation instructions at https://github.com/ollama/ollama
2. On your terminal, run ollama run llama2 (let's make this a shell script that is spawned as a child function?)