From 1fabb290a38822df71b261f071d97b7960680b3e Mon Sep 17 00:00:00 2001
From: Tim Koornstra <tim.koornstra@gmail.com>
Date: Wed, 18 Sep 2024 11:02:25 +0200
Subject: [PATCH] Remove tests and change model_to_spec readme

---
 .github/workflows/run_tests.yml | 14 --------------
 README.md                       | 33 +++++++++------------------------
 2 files changed, 9 insertions(+), 38 deletions(-)

diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
index 50eaa531..4d543a3b 100644
--- a/.github/workflows/run_tests.yml
+++ b/.github/workflows/run_tests.yml
@@ -49,20 +49,6 @@ jobs:
         python -m unittest tests/test_datamanager.py
       env:
         TF_CPP_MIN_LOG_LEVEL: '2'
-
-    - name: Test model creation
-      if: always()
-      run: |
-        python -m unittest tests/test_model_creation.py
-      env:
-        TF_CPP_MIN_LOG_LEVEL: '2'
-        
-    - name: Test model to VGSL
-      if: always()
-      run: |
-        python -m unittest tests/test_model_to_vgsl.py
-      env:
-        TF_CPP_MIN_LOG_LEVEL: '2'
         
     - name: Test replacing model layers
       if: always()
diff --git a/README.md b/README.md
index 19afbaf1..617c177c 100644
--- a/README.md
+++ b/README.md
@@ -356,35 +356,20 @@ To integrate a Loghi HTR model into your project, follow these steps:
 
 ### How can I determine the VGSL spec of a model I previously used?
 
-If you've used one of our older models and would like to know its VGSL specification, follow these steps:
+If you've used one of our models and would like to know its VGSL specification, you can now use the **vgslify** package to generate the VGSL spec directly from your model. Follow the steps below:
 
-**For Docker users:**
+1. Load your model as usual (either from a saved file or from memory).
+2. Use the `vgslify.utils.model_to_spec` function to generate the VGSL spec string.
 
-1. If your Docker container isn't already running with the model directory mounted, start it and bind mount your model directory:
+Example:
 
-```bash
-docker run -it -v /path/on/host/to/your/model_directory:/path/in/container/to/model_directory loghi/docker.htr
-```
-
-Replace `/path/on/host/to/your/model_directory` with the path to your model directory on your host machine, and `/path/in/container/to/model_directory` with the path where you want to access it inside the container.
-
-2. Once inside the container, run the VGSL spec generator:
-
-```bash
-python3 /src/loghi-htr/src/model/vgsl_model_generator.py --model_dir /path/in/container/to/model_directory
-```
-
-Replace `/path/in/container/to/model_directory` with the path you specified in the previous step.
-
-**For Python users:**
-
-1. Run the VGSL spec generator:
-
-```bash
-python3 src/model/vgsl_model_generator.py --model_dir /path/to/your/model_directory
+```python
+from vgslify.utils import model_to_spec
+vgsl_spec_string = model_to_spec(model)
+print(vgsl_spec_string)
 ```
 
-Replace `/path/to/your/model_directory` with the path to the directory containing your saved model.
+Replace `model` with your loaded TensorFlow model.
 
 ### How do I use `replace_recurrent_layer`?