.gitea/workflows/test.yaml

@@ -1,20 +0,0 @@
-name: pdf2text test
-run-name: syntax check
-on: [push]
-
-jobs:
-  Compile:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v4
-      - name: List files in the repository
-        run: |
-          ls ${{ gitea.workspace }}
-      - run: echo "Install dependencies."
-      - run: apt update
-      - run: apt install python3
-      - run: python3 -m pip install -r requirements.txt
-      - run: "Check if starts correcly. Syntax check."
-      - run: ./pdf2text .
-      - run: echo "This job's status is ${{ job.status }}."

README.md

@@ -3,10 +3,10 @@
 I've converted 8gb of PDF's to text in one afternoon on a decade old x270 using this script. Performant enough imho. Try to get 8Gb in your LLM and getting it to actually use it. That's the challenge.
 
 ## Convert all PDF's to text
-This is an [script](/pdf2text) for converting a batch of PDF's to text for machine learning.
+This is an script for converting a batch of PDF's to text for machine learning.
 It only has two dependencies:
- - `python3` 
+ - python3
- - `pdf.miner` (python requirement, specified in [requirements.txt](/requirements.txt) file) 
+ - pdf.miner (python requirement, specified in requirements.txt file) 
 
 ## Installation
 ```bash
@@ -22,6 +22,3 @@ source .venv/bin/activate
 ./pdf2text [source/destination dir]
 ```
 You read that correctly, the source directory is also the destination directory.
-
-## Todo:
-Make decent python package so it's installable on system without having to load environment first. Not sure if worth it, it's not something you daily use.