.Net: Fixed warning in release pipeline about Docker base image in examples

dotnet/samples/Demos/QualityCheck/README.md

@@ -3,6 +3,7 @@
 This sample provides a practical demonstration how to perform quality check on LLM results for such tasks as text summarization and translation with Semantic Kernel Filters.
 
 Metrics used in this example:
+
 - [BERTScore](https://github.com/Tiiiger/bert_score) - leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity.
 - [BLEU](https://en.wikipedia.org/wiki/BLEU) (BiLingual Evaluation Understudy) - evaluates the quality of text which has been machine-translated from one natural language to another.
 - [METEOR](https://en.wikipedia.org/wiki/METEOR) (Metric for Evaluation of Translation with Explicit ORdering) - evaluates the similarity between the generated summary and the reference summary, taking into account grammar and semantics.
@@ -14,7 +15,7 @@ In this example, SK Filters call dedicated [server](./python-server/) which is r
 
 ## Prerequisites
 
-1. [Python 3.12](https://www.python.org/downloads/) 
+1. [Python 3.12](https://www.python.org/downloads/)
 2. Get [Hugging Face API token](https://huggingface.co/docs/api-inference/en/quicktour#get-your-api-token).
 3. Accept conditions to access [Unbabel/wmt22-cometkiwi-da](https://huggingface.co/Unbabel/wmt22-cometkiwi-da) model on Hugging Face portal.
 
@@ -25,29 +26,34 @@ It's possible to run Python server for task evaluation directly or with Docker.
 ### Run server
 
 1. Open Python server directory:
+
 ```bash
 cd python-server
 ```
 
 2. Create and active virtual environment:
+
 ```bash
 python -m venv venv
 source venv/Scripts/activate # activate on Windows
 source venv/bin/activate # activate on Unix/MacOS
 ```
 
 3. Setup Hugging Face API key:
+
 ```bash
 pip install "huggingface_hub[cli]"
 huggingface-cli login --token <your_token>
 ```
 
 4. Install dependencies:
+
 ```bash
 pip install -r requirements.txt
 ```
 
 5. Run server:
+
 ```bash
 cd app
 uvicorn main:app --port 8080 --reload
@@ -58,18 +64,42 @@ uvicorn main:app --port 8080 --reload
 ### Run server with Docker
 
 1. Open Python server directory:
+
 ```bash
 cd python-server
 ```
 
-2. Create `.env/hf_token.txt` file and put Hugging Face API token in it.
+2. Create following `Dockerfile`:
+
+```dockerfile
+# syntax=docker/dockerfile:1.2
+FROM python:3.12
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install "huggingface_hub[cli]"
+RUN --mount=type=secret,id=hf_token \
+    huggingface-cli login --token $(cat /run/secrets/hf_token)
+
+RUN pip install cmake
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY ./app /code/app
+
+CMD ["fastapi", "run", "app/main.py", "--port", "80"]
+```
+
+3. Create `.env/hf_token.txt` file and put Hugging Face API token in it.
+
+4. Build image and run container:
 
-3. Build image and run container:
 ```bash
 docker-compose up --build
 ```
 
-4. Open `http://localhost:8080/docs` and check available endpoints.
+5. Open `http://localhost:8080/docs` and check available endpoints.
 
 ## Testing