Update README to reflect installation notes and general things

README.md

@@ -1,11 +1,43 @@
 FabFire Adapter
 ===
-The FabFire adapter translates mqtt messages from the reader hardware to api calls to bffhd.
 
-# Usage
+The FabFire adapter translates MQTT messages from the FabReader hardware to API calls to bffhd.
-## Configuration in `config.toml`
+
+# Installation
+
+## With python3-venv
+
+Easy, native installation without overhead is possible with python3 virtual environment:
+
+```shell
+cd /opt/fabinfra/adapters/
+git clone https://gitlab.com/fabinfra/fabaccess/fabfire_adapter.git --recursive
+cd fabfire_adapter/
+python3 -m venv env
+. env/bin/activate
+pip3 install -r requirements.txt
+```
+
+## With the Dockerfile and Podman
+
+You can also install the fabfire adapter using Docker or Podman:
+
+```shell
+podman build -f Dockerfile -t fabinfra/fabfire_adapter
+podman run localhost/fabinfra/fabfire_adapter:latest
+
+#cleanup if required:
+podman system prune
+```
+
+# Configuration
+
+Edit the config file example provided in `config/config.toml`
+
 1. Set `hostname` and `port` for your MQTT Broker and bffhd instance
-2. Add your readers to the `[readers]` section. Every reader needs a unique subsection name:
+
+2. Add your desired readers to the `[readers]` section. Every reader needs a unique subsection name:
+   
    ```toml
    [readers]
     [readers.REPLACEME]
@@ -17,12 +49,14 @@ The FabFire adapter translates mqtt messages from the reader hardware to api cal
     machine = "urn:fabaccess:resource:Another"
    ```
 
-## Running
+# Running
-1. Install the requirements with `pip install -r requirements.txt`
+
-2. Run the adapter with at least python 3.7
+1. Run the adapter:
-```shell
+
+2. ```shell
    python3 main.py
    ```
+
 3. The adapter has to remain running for the readers to work
 
 # Provisioning