updated docs

README.md

@@ -14,14 +14,21 @@ This app provides endpoints to showcase fine-tuning of models for image and text
 It is possible to execute one training at a time and to get status information via the `/get_training_status` endpoint.
 Via the `/stop_training` endpoint stopping the currently running training is possible. After the training, the fine-tuned model is uploaded to huggingface hub. 
 
+![Swagger UI](./docs/swagger.png)
+
+Further details for parameters of the endpoints see: https://your-domain/docs
+
+## Deployments
+This app can be deployed on hugging face spaces or run on any docker environment. Regardless on the infrastructure, it is recommended to use GPUs for executing the trainings. 
+- Hugging face spaces: Duplicate the space and run it on a GPU. 
+- Docker environments: Use the docker-compose file in the repository to run the app locally. 
+
 ## Neccesary Environment Variables
 - `AUTHENTICATION_TOKEN`: Secret that is necessary to authorize calling the apps endpoints. 
+   When running as private hugging face space, this needs to be the same as a hugging face token. 
 - `HUGGINGFACE_TOKEN`: Huggingface token to be used for accessing the huggingface hub and uploading the models. 
 - `HUGGINGFACE_ORGANIZATION`: Organization to be used for uploading the fine-tuned models.
 
-Further details for parameters of the endpoints see: https://your-domain/docs
-
-
 ## Image Classification
 
 Use `/training/image_classification` for fine tuning a model for image classification tasks. 

src/main.py

@@ -23,7 +23,11 @@ from pathlib import Path
 import tempfile
 
 
-app = FastAPI()
+app = FastAPI(
+    title="Pimcore Fine-Tuning Service",
+    description="This service allows you to fine-tune image and text classification models and upload them to hugging face hub.",
+    version="1.0.0"
+)
 
 environmentVariableChecker = EnvironmentVariableChecker()
 environmentVariableChecker.validate_environment_variables()